Goods transportation monitoring method and system based on Internet of Things

Abstract

The invention provides a cargo transportation monitoring method and system based on the Internet of Things, and relates to the technical field of logistics management. The method comprises the following steps: acquiring cargo transportation state data and monitoring an unauthorized physical contact behavior; performing field-level deep inspection on the data packet, identifying and positioning timestamp deviation and coordinate jump characteristics of a deception signal and a forged sensor data packet, and transmitting the data packet to a cloud platform through an encryption channel; establishing a normal transportation track and cargo state change baseline model, independently analyzing cargo state parameter dimensions, and discovering an abnormal mode; and through security event bus sharing information cooperative judgment, when a suspicious sign is found, a cross-level linkage response mechanism is triggered. According to the invention, the anti-attack capability and the abnormal response efficiency in the transportation process can be improved, and full-link data security and integrity protection of cargo transportation can be realized.

Description

Technical Field

[0001] This invention relates to the field of logistics management technology, and in particular to a cargo transportation monitoring method and system based on the Internet of Things. Background Technology

[0002] Cargo transportation security monitoring plays a crucial role in the modern logistics industry, directly impacting cargo integrity, transportation efficiency, and corporate economic interests. With the global expansion of logistics networks, ensuring safety during transportation has become a core requirement for industry development. Whether it's long-haul freight or urban delivery, security issues can severely affect supply chain stability and customer trust, thus necessitating innovative technologies to address increasingly complex threats. However, current security monitoring methods often fall short in the face of diverse threats. Many existing solutions focus on protecting single links, such as protecting only the equipment itself or network transmissions, neglecting the interconnectedness between different links. This fragmented approach struggles to cope with complex, multi-layered attacks, such as network data anomalies caused by physical device tampering or interference with transportation trajectory tracking by network spoofing, leading to vulnerabilities in the overall security system.

[0003] A deeper technical challenge lies in achieving information sharing and coordinated response across all stages. During transportation, physical equipment, network transmission, and business operations exist at different levels, operating independently without effective communication mechanisms. For example, when a terminal device detects unauthorized contact, if the abnormal information cannot be promptly transmitted to the network or business layer, the best opportunity to prevent the threat may be missed. Furthermore, this information silo phenomenon is particularly evident in actual operations. For instance, if a truck experiences equipment dismantling during transport, although on-site sensors may record the anomaly, the network layer cannot synchronously adjust its data verification strategy, and the business layer fails to adjust the transportation route in time, ultimately leading to a continuous escalation of cargo safety risks. This inability to effectively link information across different levels has become a bottleneck that urgently needs to be overcome in cargo transportation safety monitoring. Summary of the Invention

[0004] This invention provides a cargo transportation monitoring method and system based on the Internet of Things, which aims to improve the anti-attack capability and anomaly response efficiency of the transportation process, and realize the data security and integrity protection of the entire cargo transportation chain.

[0005] In a first aspect, the present invention provides a cargo transportation monitoring method based on the Internet of Things, which mainly includes: Collect cargo transportation status data and monitor unauthorized physical contact behavior; Perform in-depth field-level checks on the data packets to identify timestamp deviations, coordinate jump characteristics, and forged sensor data packets in the positioning spoofing signals, and transmit the data packets to the cloud platform through an encrypted channel; Establish a baseline model of normal transportation trajectory and cargo status changes, independently analyze the dimensions of cargo status parameters, and discover abnormal patterns; By sharing information and making collaborative judgments through a security event bus, a cross-level linkage response mechanism is triggered when suspicious signs are detected.

[0006] Furthermore, the collection of cargo transportation status data includes: Deploy physical tamper-proof sensors and security chips to monitor unauthorized access attempts to hardware interfaces; Identify debug port access attempts, which are determined by real-time monitoring of physical contact behavior; Collect cargo transportation status data, which includes cargo location and environmental parameters collected by sensors; The detected unauthorized physical contact behavior is converted into a detection event, which is then uploaded to the security event bus.

[0007] Furthermore, the deep field-level inspection of the data packets to identify timestamp deviations, coordinate jump characteristics, and forged sensor data packets in the location spoofing signal includes: Implement encrypted transmission protocols and data packet integrity verification mechanisms; Deeply examine and analyze the timestamp continuity and coordinate smoothness in the data packet fields to determine timestamp deviation and coordinate jump characteristics; Identify traffic feature anomalies in forged sensor data packets, including data packet injection patterns; Configure intrusion detection probes to perform real-time analysis of abnormal communication patterns and generate network layer detection events that are uploaded to the security event bus.

[0008] Furthermore, the establishment of the baseline model for normal transportation trajectories and changes in cargo status includes: A normal transportation behavior model is trained based on machine learning algorithms, and the normal transportation behavior model establishes a multi-dimensional baseline for cargo location and status parameters. Each parameter dimension of cargo status change is analyzed independently, and the actual parameters are compared with the baseline model to identify abnormal patterns, including cargo status change and route deviation. Local anomaly pattern identification is performed on sensor data injection attacks, and detection events are generated and uploaded to the security event bus.

[0009] Furthermore, the collaborative judgment through information sharing via the security event bus includes: Aggregate each detection event and determine the severity of the intrusion signs, where the severity is judged collaboratively based on multi-level information.

[0010] Furthermore, the independent analysis of each parameter dimension of the change in the goods status includes: For the goods location parameter dimension, obtain the actual location data and compare it with the baseline model; If the deviation of the actual location data exceeds the preset threshold, determine the route deviation abnormal mode; For the goods status parameter dimension, obtain the sensor data and compare it with the baseline model; If the local change of the sensor data is abnormal, identify the data injection attack mode.

[0011] Furthermore, the triggering of the cross-level linkage response mechanism includes: Switch to the backup communication channel; Lock the access permission of the suspicious device; Notify the security management personnel to intervene and handle, and the notification is based on the security event bus event.

[0012] Furthermore, the in-depth inspection and analysis of the timestamp continuity and coordinate smoothness in the data packet fields includes: Obtain the continuous timestamp sequence in the data packet; Judge whether there is a deviation in the timestamp sequence. If there is a deviation, mark the positioning spoofing signal; Obtain the coordinate sequence in the data packet; Judge whether there is a jump feature in the coordinate sequence. If there is a jump feature, confirm the forgery feature.

[0013] In a second aspect, an Internet of Things-based goods transportation monitoring system provided by the present invention includes: An acquisition and monitoring module, configured to acquire goods transportation status data and monitor unauthorized physical contact behavior; An inspection and identification module, configured to perform in-depth inspection at the field level on the data packet, identify the timestamp deviation of the positioning spoofing signal, the coordinate jump feature, and the forged sensor data packet, and transmit the data packet to the cloud platform through an encrypted channel; An independent analysis module, configured to establish a normal transportation trajectory and a baseline model of the change in the goods status, independently analyze the goods status parameter dimension, and discover abnormal modes; A collaborative judgment module, configured to share information through the security event bus for collaborative judgment, and trigger a cross-level linkage response mechanism when suspicious signs are found.

[0014] Furthermore, the acquisition module specifically includes: A deployment unit, configured to deploy physical anti-disassembly sensors and security chips to monitor unauthorized access attempts to the hardware interface; An access attempt unit for identifying access attempts to a debug port, where the access attempts are determined by real-time monitoring of physical contact behaviors; A status acquisition unit for collecting cargo transportation status data, where the transportation status data includes the cargo position and environmental parameters collected by sensors; A conversion unit for converting monitored unauthorized physical contact behaviors into detection events, and uploading the detection events to a security event bus.

[0015] The technical solutions provided by the embodiments of the present invention have the following beneficial effects: By deploying physical anti-tampering sensors and microscopic monitoring modules, the present invention can identify unauthorized contacts and hardware tampering behaviors in real time; adopt deep packet inspection and encryption protocols to accurately capture spoofing signals such as timestamp deviation and coordinate jumps; construct a transportation trajectory baseline model based on machine learning to detect anomalies in cargo status and routes, and through a security event bus, achieve information sharing and cross-layer linkage response at all levels, such as measures like locking the device, isolating traffic, and suspending updates, to ensure that threats are contained in a timely manner. The present invention ultimately realizes the protection of the security and integrity of full-link data in cargo transportation, significantly improves the anti-attack ability and anomaly response efficiency during the transportation process, and provides a reliable security guarantee for the logistics industry. Description of the Drawings

[0016] Figure 1 It is a schematic flow chart of the method for monitoring cargo transportation based on the Internet of Things according to the present invention.

[0017] Figure 2 It is a schematic diagram of the module structure of the method for monitoring cargo transportation based on the Internet of Things according to the present invention.

[0018] Figure 3 It is a schematic diagram of the module structure of the method for monitoring cargo transportation based on the Internet of Things according to the present invention.

[0019] Figure 4 It is a schematic diagram of the module structure of the method for monitoring cargo transportation based on the Internet of Things according to the present invention.

[0020] Figure 5 It is a schematic diagram of the module structure of the method for monitoring cargo transportation based on the Internet of Things according to the present invention.

[0021] Figure 6 It is a schematic diagram of the module structure of the method for monitoring cargo transportation based on the Internet of Things according to the present invention.

[0022] Figure 7 It is a schematic diagram of the module structure of the method for monitoring cargo transportation based on the Internet of Things according to the present invention.

[0023] Figure 8This is a schematic diagram of the module structure of the Internet of Things-based cargo transportation monitoring method of the present invention.

[0024] Figure 9 This is a schematic diagram of the module structure of the Internet of Things-based cargo transportation monitoring method of the present invention.

[0025] Figure 10 This is a schematic diagram of the module structure of the Internet of Things-based cargo transportation monitoring method of the present invention. Detailed Implementation

[0026] To further understand the content of this invention, a detailed description of the invention is provided in conjunction with the accompanying drawings and embodiments. The specific embodiments described herein are for illustrative purposes only and are not intended to limit the invention. It should also be noted that, for ease of description, only the parts relevant to the invention are shown in the accompanying drawings.

[0027] like Figures 1-10 The cargo transportation monitoring method based on the Internet of Things provided in this embodiment of the invention may specifically include: S1 collects cargo transportation status data and monitors unauthorized physical contact behavior.

[0028] Physical anti-tamper sensors are embedded in the terminal equipment of cargo transportation to monitor the integrity of the equipment casing in real time. If unauthorized physical contact or abnormal opening of the casing is detected, the time and status of the abnormal event are recorded by the built-in security chip, and the abnormal information is encrypted and stored in a protected area inside the equipment. The abnormal information obtained from the physical anti-tamper sensors is initially verified and formatted by the security chip, and converted into standard event log data. This event log data is then transmitted to the communication module of the terminal equipment, ready to be uploaded to the upper-level monitoring platform. After receiving the event log data, the communication module of the terminal equipment encapsulates the data using a preset encryption protocol to ensure confidentiality during transmission. At the same time, cargo transportation status data, such as location and environmental parameters, are recorded and packaged together with the event log data to form a complete transportation status report. The packaged transportation status report is transmitted to the monitoring platform via a wireless channel. Before transmission, the data integrity is verified. If the verification passes, transmission continues, ensuring that the monitoring platform can promptly obtain abnormal contact information and cargo transportation status data of the terminal equipment for subsequent protection decisions.

[0029] When physical anti-tamper sensors are embedded in cargo transportation terminal equipment, real-time monitoring of the integrity of the equipment casing can be achieved by integrating miniature vibration detectors and photosensitive elements. Such sensors can detect any minor physical disturbances, such as loose screws or external prying, thereby triggering an alarm mechanism. The purpose of this is to detect potential intrusion attempts early and ensure that the equipment is protected from the risk of data leakage caused by tampering. The beneficial effect is to improve the overall reliability of transportation safety.

[0030] In one possible implementation, after obtaining abnormal information from the physical anti-tamper sensor, the process of initial verification through the security chip involves comparing data signatures. If the signatures match, they are formatted as event log data. This process helps filter false alarms and standardizes the information format for easier subsequent transmission. The rationale for this is to reduce the false alarm rate and provide a reliable data foundation for the upper-layer platform. The beneficial effect is to optimize system response efficiency and avoid unnecessary waste of resources.

[0031] When the terminal device's communication module receives event log data and encapsulates it using a preset encryption protocol, it can use symmetric key algorithms such as Advanced Encryption Standard (AES) to protect data confidentiality. At the same time, it packages cargo transportation status data, such as real-time location coordinates and temperature readings, with the log. This packaging method ensures information integrity and relevance. The purpose of this is to prevent interception or tampering during transmission. The beneficial effect is to maintain the authenticity of cargo status data and support the monitoring platform in making accurate protection decisions.

[0032] In one possible implementation, when verifying the data integrity of the packaged transport status report before transmitting it wirelessly, a hash function can be used to generate a verification value and compare it with the original value. If they match, the data is transmitted. This verification mechanism connects the trust chain between the local device and the remote platform. The reason for doing this is to prevent man-in-the-middle attacks. The beneficial effect is to ensure the timely and reliable acquisition of abnormal contact information and transport status data, and to strengthen the defense-in-depth capability of the entire protection system.

[0033] For an extended example of the entire process, in the scenario of railway freight transportation, after the terminal device detects that the casing is abnormally opened, the security chip records and encrypts the abnormal information, and then verifies it, converts it into log data, packages it with the location data, and transmits it to the platform. In this way, the chain from monitoring to transmission forms a closed-loop protection, which has the beneficial effect of reducing the success rate of intrusion. At the same time, similar applications in road transportation can cope with environments with a lot of vibration interference by adjusting the sensor sensitivity to ensure monitoring accuracy.

[0034] In one possible implementation, another example is in the monitoring of shipping containers. Physical anti-tamper sensors combined with security chips can detect unauthorized opening of container doors. Abnormal information is verified and encapsulated before being reported along with environmental parameters such as humidity data. This multi-layered protection supports each other and has the beneficial effect of improving cross-scenario adaptability. The reason is that although physical threats vary in different transportation modes, unified event log processing ensures consistent response.

[0035] The example of encryption protocols and integrity verification is supported by air cargo transportation. In the face of high-altitude signal interference, the pre-defined protocol encapsulates the status report and verifies it before transmission, which can effectively resist the injection of forged data. The purpose of doing so is to maintain the stability of the data link and reduce the risk of economic loss. At the same time, it corroborates the aforementioned railway example, jointly proving the universal value of this method in multimodal transportation.

[0036] S2, perform field-level in-depth inspection of the data packet to identify timestamp deviations, coordinate jump characteristics, and forged sensor data packets of the positioning spoofing signal, and transmit the data packet to the cloud platform through an encrypted channel.

[0037] At the network transport layer, a deep field-level inspection of data packets is performed. First, timestamp and coordinate fields are extracted from the transmitted data packets. The extracted timestamp data is compared with a pre-established standard time series to determine if there are any minor deviations. Simultaneously, continuity analysis is performed on the coordinate field data to determine if any jumps occur, obtaining preliminary anomaly characteristic data. Based on the preliminary anomaly characteristic data obtained from the timestamp and coordinate fields, the source address and checksum of the data packet are further combined to verify whether the data packet conforms to preset integrity rules. If the verification result shows signs of data packet tampering, the data packet is marked as a suspected forgery data packet, and its anomaly characteristic details are recorded. For records marked as suspected forgery data packets, preset encrypted channel transmission rules are invoked to transmit the anomaly characteristic details of these data packets in encrypted form to the cloud platform. The comparison results of the original data packet's timestamp deviation and coordinate jump characteristics are retained for subsequent verification and tracking. After receiving the encrypted anomaly characteristic details, the cloud platform stores and classifies the timestamp deviation and coordinate jump characteristics. These characteristics are compared with historical data packet anomaly records to determine if there are repeated location spoofing signal patterns, thus completing the deep inspection target of network transport layer data packets.

[0038] In one possible implementation, extracting timestamp and coordinate fields from transmitted data packets involves parsing the packet header structure. For example, data packets typically contain timestamps as a numerical sequence representing the time the data was generated, while coordinate fields record location information such as latitude and longitude values. This extracted data can be directly used for comparison. The pre-established standard time series refers to a reference sequence based on device clock synchronization. The role of comparing and judging small deviations is to detect GPS spoofing signals early, as deviations may indicate that the signal has been tampered with, thereby improving the sensitivity of detection. At the same time, the continuity analysis of the coordinate fields is to determine the jump phenomenon by checking the smooth transition of coordinates between adjacent data packets. This helps to identify abnormal patterns of spoofing injection. The beneficial effect is to obtain preliminary abnormal feature data to provide basic support for subsequent verification.

[0039] The verification process for preliminary abnormal data, combined with the source address and checksum field, refers to the source address serving as the identifier of the data packet sender, while the checksum field is usually a hash value used to ensure data integrity. Verifying whether it conforms to the preset integrity rules means checking whether these fields match the expected specifications. If signs of tampering are shown, it is marked as a suspected forged data packet and the abnormal characteristics are recorded. The purpose of this is to strengthen the identification of the authenticity of the data packet. The beneficial effect is to reduce false positives and accurately lock potential threats. In this way, anomalies are confirmed from multiple aspects such as the mutual support of the address and checksum.

[0040] In one possible implementation, invoking a preset encrypted channel transmission rule is the process of sending the details of the abnormal features to the cloud platform in encrypted form. For example, the details are encapsulated using a symmetric encryption key, while retaining the comparison results of timestamp deviation and coordinate jump features. Here, the encrypted channel refers to a secure transmission path such as a channel based on the TLS protocol. The beneficial effect is to protect sensitive information from being intercepted, thereby ensuring the security of tracking. The logical progression from marking records to transmission ensures the complete transmission of abnormal data.

[0041] The storage and classification of anomaly feature details received on the cloud platform involves categorizing timestamp deviations and coordinate jump features into database categories, and then comparing them with anomaly records in historical data packets to determine recurring location spoofing signal patterns. For example, historical records may show that specific deviation patterns appear repeatedly. The purpose of this process is to accumulate threat intelligence. The beneficial effect is to achieve a complete closed loop of deep detection of network transport layer data packets. Through classification and comparison, the logical progression from core anomaly features to pattern recognition supports the comprehensive identification of spoofing signals.

[0042] In one possible implementation, the above extraction and comparison process can be applied to monitor vehicle location in actual cargo transportation scenarios. For example, when the coordinates of the data packet suddenly jump from one city to another without matching the driving speed, continuity analysis can capture this jump. The beneficial effect is to prevent logistics risks caused by route forgery. The verification combined with the validation field supports end-to-end protection from the device end to the cloud. The mutually supportive directions include the joint judgment of time deviation and coordinate anomaly, which further improves the detection accuracy.

[0043] Specifically, the encrypted transmission to the cloud platform ensures the secure sharing of anomaly details in a multi-device network. For example, after a forged data packet is marked in a sensor network, it is uploaded through an encrypted channel to avoid secondary attacks. The beneficial effect is to achieve real-time response, while the comparison by the cloud platform supports the current detection from a historical perspective, forming a time-dimensional thought chain, thereby maintaining the system's defense in depth under diverse intrusion threats.

[0044] S3. Establish a baseline model of normal transportation trajectory and cargo status changes, conduct independent analysis of cargo status parameter dimensions, and discover abnormal patterns.

[0045] In freight transportation operations, a baseline dataset is pre-established to track transportation trajectories and changes in cargo status. This dataset is compiled from historical transportation records, extracting information on vehicle routes and the normal fluctuation range of cargo status parameters. This data forms an initial reference template for subsequent comparison and judgment. Based on this initial template, real-time vehicle route data and cargo status parameter information are collected for each transportation task. The collected real-time route and status parameters are compared item by item with the data in the reference template to determine if any deviations or abnormal fluctuations exist. During the comparison process, preset threshold ranges are set for each parameter dimension. If the real-time value of a parameter exceeds the corresponding threshold range, the parameter is marked as an anomaly, and the specific time and location information of the anomaly are recorded. For the marked anomalies, the overall context of the transportation task is considered, linking the anomalies to the business objectives of route deviation and changes in cargo status to form a preliminary judgment result of the anomaly pattern, providing a direct reference for subsequent freight transportation monitoring.

[0046] In the business process of freight transportation, the process of establishing a baseline dataset in advance involves extracting key information from historical transportation records, such as obtaining regular route information from past vehicle driving logs. This information includes standard waypoints from the origin to the destination and the expected time period. At the same time, the normal fluctuation range of cargo status parameters, such as the temperature variation within a specified range, is extracted. These are organized into an initial reference template. Doing so can provide a reliable basis for comparison in real-time monitoring and help identify potential problems early.

[0047] In one possible implementation, for cold chain transportation operations, the reference template could include historical data showing that cargo humidity fluctuates between 5% and 10%. This helps to quickly identify abnormal humidity spikes during actual transportation, thereby improving transportation safety.

[0048] The process involves real-time data collection and comparison based on an initial reference template. For each transportation task, GPS devices are used to collect vehicle travel route data, such as latitude and longitude coordinate sequences, and sensors collect cargo status parameters, such as real-time weight or vibration level. These data are then compared item by item with the corresponding items in the template to determine deviations. For example, if the real-time coordinates deviate from the template path by more than a preset distance during route comparison, it is considered an abnormal fluctuation. This approach can capture deviations in transportation in real time, which helps prevent cargo damage or delays.

[0049] In one possible implementation, for express delivery and logistics operations, if the collected real-time route data shows that a vehicle is detouring on a non-standard road, an alert can be issued immediately after comparison. This, along with the comparison of cargo status parameters, supports comprehensive anomaly detection. The specific method for setting preset threshold ranges for each parameter dimension during the comparison process—for example, setting a distance threshold range for route parameters and upper and lower limits for cargo temperature parameters—is used. If the real-time value exceeds these limits, it is marked as an anomaly, and the time and location are recorded. This refines anomaly identification and facilitates tracing the root cause of problems.

[0050] In one possible implementation, for the transportation of perishable goods, the threshold range of the temperature parameter is set to zero to ten degrees. If the temperature exceeds the threshold, an anomaly is marked and recorded, such as if it occurs within five minutes on a certain road segment. This is supported by the threshold marking of other parameters, such as humidity, and provides multi-dimensional evidence to support subsequent judgment.

[0051] The process involves identifying anomalies by combining them with the overall context of the transportation task to determine anomaly patterns. Anomalies are linked to route deviations and changes in cargo status; for example, a route anomaly is linked to a simultaneous cargo vibration anomaly to identify a potential intrusion or accident pattern. This generates a comprehensive judgment result, which helps optimize the response efficiency of cargo transportation monitoring.

[0052] In one possible implementation, for international freight operations, if an anomaly indicates route deviation accompanied by temperature anomalies, a pattern judgment is formed, such as possible external interference. This is supported by the marking in the previous steps, ensuring logical consistency from benchmark establishment to final judgment.

[0053] S4, through information sharing and collaborative judgment via the security event bus, triggers a cross-level linkage response mechanism when suspicious signs are detected.

[0054] In all levels of cargo transportation monitoring, a security event bus for information sharing is deployed to uniformly converge the abnormal signal data generated by the device layer, network layer, and application layer into this bus, facilitating data interaction and collaborative judgment between levels. Obtain the abnormal signal data of each level from the security event bus, conduct comprehensive comparison on this data. If the data of a certain level exceeds the preset threshold, it is determined as a suspicious sign, triggering a cross-level linkage response process. When triggering the linkage response process, first switch to a pre-established backup communication channel to ensure the continuity of data transmission. At the same time, perform an access permission locking operation on the detected suspicious device to restrict its further operations. After completing the communication channel switching and permission locking, send a notification message to a preset management terminal through the security event bus to prompt relevant abnormal situations, ensuring the timeliness and effectiveness of the intrusion detection response mechanism in cargo transportation monitoring.

[0055] Specifically, in cargo transportation monitoring, the security event bus, as a unified information sharing platform, converges abnormal signal data such as unauthorized physical contact signals detected by vehicle-mounted terminals at the device layer, GPS spoofing traffic characteristics analyzed at the network layer, and route deviation patterns identified by machine learning models at the application layer. This bus is similar to a message queue system in an event-driven architecture and realizes asynchronous transmission and distribution of data through a publish-subscribe mechanism. For example, in one embodiment, when a device layer sensor captures an attempt to access the debug port, this signal is immediately encapsulated into an event message and pushed to the bus, and other level modules subscribe to this event to obtain real-time data. This can ensure efficient information interaction between levels, avoid delayed responses caused by isolated detection, and is beneficial to enhancing the overall synergy of intrusion detection.

[0056] The security event bus adopts a standardized event format when converging data, including a timestamp, level identifier, and abnormal type label, facilitating subsequent processing and supporting the depth of layered protection from multiple aspects. For example, when forged data packets are detected at the network layer, combining this data with device layer signals can mutually verify the authenticity of the abnormality from physical and network dimensions, thereby improving the judgment accuracy.

[0057] In a possible implementation, this convergence mechanism can also integrate historical data to form a spatio-temporal sequence of abnormal signals for tracking the evolution path of intrusions, which is beneficial to防范多样化威胁. After obtaining the abnormal signal data from the security event bus, the process of comprehensive comparison involves mapping multi-level data into a unified vector space and evaluating the consistency by calculating similarity or deviation metrics. If the deviation between the device layer signal and the network layer traffic characteristics exceeds a preset threshold such as an abnormal score threshold, it is determined as a suspicious sign and a response is triggered. For example, in the cargo transportation scenario, when the application layer discovers an abnormal change in the cargo status, check whether there is a physical contact record at the device layer simultaneously. This supports the reliability of the judgment from the aspect of data fusion and is beneficial to reducing false alarms.

[0058] The comparison process can be performed in steps. First, the data at each layer is normalized, and then a weighted sum is calculated to quantify the overall risk. This approach can lead to more accurate threat identification.

[0059] In one embodiment, the comparison of GPS spoofing signals examines the correlation between timestamp deviations and coordinate jumps, mutually supporting the intrusion confirmation from multiple directions of traffic and behavior. When the linkage response process is triggered, the operation of switching to a backup communication channel prioritizes ensuring transmission continuity. This channel is pre-configured as an encrypted backup link, such as switching from the primary satellite link to a terrestrial wireless link. Simultaneously, access permissions for suspicious devices are locked by revoking their authentication tokens. For example, when a network layer anomaly is detected, the system automatically isolates the debugging interface of the vehicle terminal. This approach, by jointly maintaining system security from both communication and access control perspectives, helps to prevent the spread of intrusion.

[0060] The handover process includes verifying the availability of the backup channel and seamlessly migrating the data stream, supporting the execution of the emergency plan from multiple aspects, including response timeliness. After the handover and locking are completed, an alarm is pushed to the management terminal via a mechanism that sends notification information through the security event bus. This notification includes anomaly details and response logs, such as pushing messages containing hierarchical signal summaries to the administrator's mobile device. This ensures the timeliness of human intervention. The information sharing and notification aspects mutually support the entire response mechanism, which is beneficial for achieving cross-level linkage in intrusion detection in cargo transportation monitoring.

[0061] A micro-level monitoring module is deployed at the hardware interface and debugging port of the vehicle-mounted terminal equipment. This module uses built-in contact sensing elements to acquire real-time changes in the physical contact state of the hardware interface, recording the time and duration of each contact, and storing this contact state change data as an initial contact log. For the data in the initial contact log, pre-established contact frequency and duration threshold rules are set. If a contact state change is detected exceeding the preset threshold, the contact event is marked as an abnormal contact record, and the time and port location information of the abnormal contact record are uploaded to the upper-layer processing unit. In the upper-layer processing unit, the abnormal contact record is further compared, and combined with a preset authorized access time window and port access permission list, it is determined whether there is an unauthorized access attempt. If unauthorized access is determined, a corresponding alarm message is generated, and the specific port location of the abnormal contact is recorded. For the generated alarm message, the specific port location of the abnormal contact is associated with the real-time monitoring status of the hardware interface and debugging port of the vehicle-mounted terminal equipment, locking the access permissions of the relevant ports to ensure that subsequent unauthorized access attempts to specific hardware interfaces and debugging ports are effectively blocked.

[0062] When deploying micro-level monitoring modules at the hardware interfaces and debugging ports of in-vehicle terminal equipment, built-in contact sensing elements can be used to acquire real-time changes in the physical contact state of the hardware interface. These contact sensing elements are sensors based on changes in capacitance or resistance. When an external object approaches or touches the port, it generates a change in electrical signal, recording the time and duration of each contact and storing these contact state changes as an initial contact log. The advantage of this approach is that it can detect potential intrusion intentions early, preventing physical tampering from spreading into the system. For example, in a cargo transportation scenario, if someone attempts to insert an unauthorized device through a USB port, the sensing element will immediately detect the contact change and generate a log, providing foundational data for subsequent analysis.

[0063] In one possible implementation, when setting pre-established contact frequency and duration threshold rules for the data in the initial contact log, if a change in contact status is detected exceeding the preset threshold, the contact event is marked as an abnormal contact record. These threshold rules are benchmarks derived from historical normal operation data; for example, the frequency threshold is set to no more than a certain number of times per hour, and the duration threshold is set to no more than a certain number of seconds. When these limits are exceeded, an anomaly is marked, and the time and port location information of the abnormal contact record are uploaded to the upper-layer processing unit. The benefits of this approach are improved detection accuracy, reduced false alarms, and timely upward flow of abnormal information, supporting overall security response. For example, during nighttime transportation, if consecutive short-term contacts exceed the threshold, the system will mark it as abnormal and upload the information, thereby preventing potential debugging port intrusion.

[0064] When further comparing abnormal access records in the upper-level processing unit, a preset authorized access time window and port access permission list are used to determine whether unauthorized access attempts exist. This authorized access time window is a legal operation period defined by the transportation scheduling plan, while the port access permission list lists the device identifiers and user roles allowed access. If unauthorized access is detected, a corresponding alarm message is generated, and the specific port location of the abnormal access is recorded. The advantage of this approach is that it achieves multi-dimensional verification, enhances the system's robustness, and avoids the limitations of relying on a single threshold. For example, in cross-border transportation, if an abnormal record occurs within the unauthorized time window, the system will generate an alarm and record the port location, thereby quickly locating the source of the problem and preventing data leakage.

[0065] In one possible implementation, when the generated alarm information is associated with the specific port location of the abnormal contact and the real-time monitoring status of the hardware interface and debugging port of the vehicle terminal equipment, a dynamic access control mechanism is used to lock the access permissions of the relevant ports. This locking mechanism involves port disabling at the software level and signal isolation at the hardware level, ensuring that subsequent unauthorized access attempts to specific hardware interfaces and debugging ports are effectively blocked. The beneficial effect of this is to form a closed-loop protection, promptly blocking intrusion paths and improving overall transportation security. For example, after detecting an anomaly in the debugging port, the system will associate it with the real-time status and lock permissions, thereby preventing further tampering and ensuring the integrity of the cargo monitoring system.

[0066] The network transport layer implements encrypted transmission protocols and data packet integrity verification mechanisms to prevent location spoofing signals and the injection of forged sensor data. Deep inspection analyzes traffic characteristics and minor deviations. At the network transport layer, during data transmission, encrypted transmission protocols are deployed to protect data content. Integrity verification of transmitted data packets determines whether tampering or forgery has occurred. If the verification result shows that the data packet content is inconsistent with the original transmission, the data packet is marked as an abnormal data packet. For marked abnormal data packets, their traffic characteristic data is extracted, and the timestamp field and coordinate information in the traffic characteristics are analyzed to check for minor deviations or discontinuous jumps. The detected deviation data is recorded as a deviation feature set. For the deviation feature set, it is further compared with a pre-established normal traffic characteristic range to determine whether the deviation feature set exceeds the normal range. If it does, an abnormal alarm message is generated. Simultaneously, the abnormal alarm message is compared with the characteristics of location spoofing signals to confirm whether forgery or injection has occurred. When forgery or injection is confirmed, the source information and transmission path of the relevant abnormal data packets are recorded, the affected data channels are isolated, and the security of subsequent data transmission is ensured. The network transport layer continuously monitors traffic characteristics and minor deviations to prevent location spoofing signals and the injection of forged sensor data.

[0067] In one possible implementation, when deploying an encrypted transmission protocol at the network transport layer, the transmission process can be protected by applying symmetric encryption to the data content. This effectively prevents data from being intercepted and read during transmission, which is beneficial for maintaining the confidentiality of cargo transportation monitoring. For example, in cargo transportation scenarios, when the vehicle terminal sends GPS positioning data, using this protocol for encryption prevents external attackers from deciphering the real coordinate information even if they capture the data packets, thereby reducing the potential impact of positioning spoofing signals. This protection mechanism can also be combined with subsequent integrity verification to ensure the security of the entire path of data from sending to receiving.

[0068] Regarding the integrity verification mechanism, when determining whether a data packet has been tampered with, the hash value of the data packet can be calculated and compared with the original hash. If they do not match, the data packet is marked as an abnormal data packet. This can promptly detect injection attempts to forge sensor data, which is beneficial to improving the overall reliability of the system. For example, in actual transportation, if the sensor temperature reading of a data packet is modified, the verification process will detect the hash mismatch, thereby isolating the packet to avoid affecting the monitoring of cargo status. This marking behavior directly supports the subsequent extraction of traffic characteristics of abnormal data packets, providing early warning to prevent more complex attacks.

[0069] For example, when extracting traffic characteristic data from abnormal data packets and analyzing timestamp fields and coordinate information, the continuity of timestamps can be checked. If a sudden jump is found, such as an unreasonable interval from one point in time to another, it is recorded as a deviation feature set. This can capture security risks caused by small deviations and is helpful in accurately identifying GPS spoofing signals. For example, in vehicle route tracking, if the coordinate information shows discontinuous displacement, such as jumping from one city to another in an instant, this analysis will highlight the deviation and help the system distinguish between normal fluctuations and malicious injection, thus laying the foundation for further comparison of deviation feature sets.

[0070] In one possible implementation, when comparing the deviation feature set with a pre-established normal traffic feature range, if it exceeds the range, an abnormal alarm message is generated and compared with the location spoofing signal features. This can confirm forgery or injection behavior and record the source information to isolate the channel, which is beneficial for continuous monitoring and prevention of threats to the network transport layer. For example, in transportation monitoring, when the deviation feature set shows that the coordinate jump exceeds the normal speed limit, the system will trigger an alarm to isolate the affected channel and ensure that the remaining data transmission is not interfered with. This mechanism as a whole strengthens the protection against location spoofing signals and injection of forged sensor data.

[0071] The packet field-level checks include timestamp continuity and coordinate smoothness verification, which are used to capture local anomalies in forged packets.

[0072] The data transmission stream retrieves field information from each data packet. A continuity check is performed on the timestamp field; by comparing the timestamp values ​​of adjacent data packets, it's determined whether there are discontinuous jumps or rollbacks. If discontinuous timestamps are found, the data packet is marked as a suspected forgery packet. For packets marked as suspected forgery, their coordinate field information is further extracted. By calculating the trend of coordinate value changes in adjacent data packets, the smoothness of the coordinates is verified to meet a preset threshold range. If the trend exceeds the threshold, the data packet is confirmed to have a local anomaly. Data packets confirmed to have local anomalies are isolated, and the specific numerical features of the anomaly fields are extracted and compared with a pre-established feature library of normal data packets to determine the specific deviation pattern of the anomaly fields. For the determined deviation pattern, the field information of the anomaly data packet is recorded in the anomaly log. Simultaneously, the field-level checks on subsequently received data packets are strengthened to ensure continuous capture of local anomalies in forgery packets.

[0073] For example, in cargo transportation monitoring, when obtaining field information of each data packet from the data transmission stream, real-time network traffic can be captured first, and then the timestamp field can be parsed. This parsing helps to identify potential threats early because it allows the system to perform preliminary screening before the data enters core processing, thereby improving the overall response speed of intrusion detection.

[0074] In one possible implementation, a continuity check is performed on the timestamp field. This involves comparing the timestamp values ​​of adjacent data packets to determine if there are any discontinuous jumps or rollbacks. For example, suppose one data packet has a timestamp of 10:00:00, and the next is 10:00:02. However, if there is a missing 10:00:01 or a sudden rollback to 09:59:59, this indicates a possible spoofing injection. This effectively prevents GPS spoofing signals from interfering with transportation routes because the continuity check ensures the logical consistency of the time series, resulting in higher accuracy and avoiding false alarms.

[0075] Specifically, if a timestamp is found to be discontinuous, the data packet is marked as a suspected forgery packet. This marking process is similar to adding a label, which facilitates subsequent tracking. From multiple perspectives, it supports a layered protection system because the marked packet can trigger network layer traffic analysis, forming a complete threat intelligence chain that supports each other, thereby reducing the risk of successful intrusion.

[0076] In one possible implementation, for data packets marked as suspected forgery, their coordinate field information is further extracted. The smoothness of the coordinates is verified by calculating the trend of coordinate value changes in adjacent data packets to see if it meets a preset threshold range. For example, if the coordinates smoothly change from (100, 200) to (101, 201) during normal transport, but the suspected packet suddenly jumps to (150, 250), this exceeds the threshold and indicates a local anomaly. The purpose of this verification is to capture subtle signs of forgery, resulting in accurate anomaly localization because it examines attack patterns from multiple angles along the coordinate dimension, avoiding blind spots from single checks.

[0077] Specifically, if the trend of change exceeds the threshold, it is confirmed that there is a local anomaly in the data packet. This confirmation process strengthens the reliability of the detection and supports the role of machine learning at the application layer from another perspective, because abnormal data can be used as input to optimize the behavior model and jointly improve the system's adaptability.

[0078] In one possible implementation, packets exhibiting localized anomalies are isolated, and specific numerical characteristics of the anomaly fields are extracted and compared with a pre-established database of normal packet characteristics. For example, if the average timestamp increment in the normal database is 1 second, while the anomaly field shows a deviation of 0.5 seconds, this comparison identifies the specific deviation pattern. This isolation and extraction prevents the spread of anomalies, providing a secure defense-in-depth approach because it allows the system to handle threats without interrupting transport monitoring, building a protective barrier from multiple directions, such as hardware and network layers.

[0079] Specifically, for identified deviation patterns, the field information of abnormal data packets is recorded in the anomaly log, while subsequent received data packets undergo enhanced field-level checks. For example, after a deviation such as a timestamp rollback pattern is logged, the system automatically increases the check frequency to check every packet. This ensures continuous capture of local anomalies in spoofed packets. The beneficial effect of this is to achieve dynamic response and bring sustained monitoring performance, because the enhanced checks are derived from log feedback, supporting the entire intrusion detection closed-loop mechanism and preventing isolated events from evolving into system-wide intrusions.

[0080] The application-layer behavior analysis engine trains a normal transportation behavior model based on machine learning algorithms, and the model establishes a multi-dimensional baseline for cargo location status and environmental parameters.

[0081] Real-time location and environmental parameter data are acquired during cargo transportation. This data includes the geographical coordinates of the transport vehicle, its speed, and the temperature and humidity information of the cargo. Data is collected periodically by onboard terminal devices and uploaded to cloud storage for subsequent baseline construction. A multi-dimensional set of transportation behavior features is constructed based on the collected location and environmental parameter data. This feature set covers the degree of deviation from the vehicle's normal route, the range of speed changes, and the temperature and humidity fluctuation range of the cargo. Statistical analysis of historical data forms a preliminary range of normal behavior. The transportation behavior feature set is trained using a pre-established machine learning algorithm, specifically a support vector machine (SVM), to generate a baseline model that reflects normal transportation behavior. This model maps multi-dimensional features to standard intervals to determine whether real-time data deviates from the normal range. During real-time monitoring, newly collected location and environmental parameter data are input into the baseline model to determine if abnormal fluctuations exist. If behavior deviating from the standard interval is detected, the abnormal event is recorded and the relevant data is marked, continuously providing behavioral judgment criteria for cargo transportation monitoring.

[0082] For example, in practical applications of cargo transportation monitoring, location data and environmental parameter data obtained during transportation can be achieved through the GPS module and sensors of the vehicle terminal. For instance, vehicles transporting fresh food will record geographical coordinates such as latitude and longitude, driving speed such as kilometers per hour, and cargo temperature such as degrees Celsius and humidity such as percentage in real time. These data are collected at fixed intervals and uploaded to the cloud for storage via wireless network. This ensures that the data is available in a timely manner and provides a complete historical record for subsequent analysis, which is beneficial to improving the accuracy of monitoring because real-time collection avoids misjudgments caused by data loss.

[0083] In one possible implementation, when constructing a multi-dimensional set of transportation behavior features based on the collected data, the degree of deviation of vehicles from their regular routes can be extracted from historical transportation records. For example, the deviation distance between the actual route and the planned route, the range of speed changes such as acceleration fluctuations, and the temperature and humidity fluctuation range of the cargo such as temperature changes from the upper and lower limits of the baseline value can be calculated. Through statistical analysis, such as calculating the average and standard deviation, a preliminary range of normal behavior can be formed. This helps to quantify the regularity of transportation and is beneficial for subsequent model training because the feature set transforms scattered data into processable structured indicators, thereby reducing noise interference and improving the sensitivity of anomaly detection.

[0084] For example, the process of training a set of transportation behavior features using a support vector machine (SVM) first requires understanding that an SVM is a supervised learning algorithm. It maximizes the interval between different categories of data points by constructing a hyperplane in the feature space. For example, during the training phase, the feature set of historical normal transportation is used as positive samples input into the algorithm. The algorithm iteratively optimizes the hyperplane parameters to distinguish between normal and abnormal patterns and generates a baseline model that maps multi-dimensional features such as location deviation and temperature and humidity fluctuations to standard ranges. This effectively captures complex patterns and is beneficial to the model's generalization ability because the SVM can avoid overfitting when processing high-dimensional data, thus reliably judging whether the data deviates from the normal range in actual monitoring.

[0085] In one possible implementation, when newly collected location data and environmental parameter data are input into the baseline model during real-time monitoring, abnormal fluctuations can be identified by comparing the matching degree between the input features and the model's standard range. For example, if the vehicle's speed changes beyond the trained range or the humidity of the cargo suddenly deviates from the fluctuation range, the system will record the event and mark the data as suspicious. This helps to respond quickly to potential intrusions and is beneficial to overall transportation safety because continuous behavior discrimination can detect route anomalies such as GPS spoofing early, thereby providing reliable anomaly identification support for cargo transportation monitoring and maintaining the system's in-depth protection.

[0086] The anomaly detection algorithm identifies local pattern changes in sensor data injection attacks, and these changes are compared with a baseline model to generate threat intelligence. Raw state parameters are obtained from sensor data collected during cargo transportation. Each parameter dimension is recorded independently to form an initial state parameter set, which includes real-time values ​​for multiple dimensions such as cargo location, temperature, and humidity. These values ​​are arranged in time series for subsequent comparative analysis. For the time series data in the state parameter set, the changing trend characteristics of each parameter dimension are extracted one by one. By comparing these values ​​point by point with a pre-established baseline of normal transportation behavior, local change patterns deviating from the normal range are identified. These local change patterns are manifested as abnormal fluctuations in parameter values ​​within a certain time period. The deviation between the local change patterns and the baseline is quantified, and a corresponding anomaly score is generated for each parameter dimension. If the score exceeds a preset threshold, it is determined to be a potential sensor data injection attack, and the scoring results are compiled into preliminary threat information. The preliminary threat information is categorized and summarized according to parameter dimensions and time periods. A structured threat intelligence report is generated based on the categorized information. The report records in detail the time of occurrence of abnormal changes, the parameter dimensions involved, and the degree of deviation, which is used for subsequent optimization and adjustment of cargo transportation monitoring and anomaly detection algorithms.

[0087] In one possible implementation, the process of obtaining raw state parameters from sensor data collected during cargo transportation can be achieved by deploying multiple sensors on the vehicle terminal. For example, a position sensor can capture the latitude and longitude coordinates of the cargo in real time, a temperature sensor can monitor the ambient temperature of the cargo, and a humidity sensor can record the air humidity level. These parameters are recorded independently to ensure the accuracy of each dimension. After forming an initial set of state parameters, arranging them in a time series helps to track dynamic changes. This approach can promptly detect potential anomalies and provide a complete data foundation for subsequent analysis, which is beneficial to improving the accuracy of intrusion detection.

[0088] Specifically, when extracting the trend characteristics of each parameter dimension from the time series data in the set of state parameters, a sliding window method can be used to scan the sequence. For example, this method can be applied to the sequence data of cargo location parameters to calculate the coordinate offset rate of continuous time points and compare it point by point with the pre-established baseline of normal transportation behavior. If the offset rate exceeds the normal fluctuation range defined by the baseline, it is identified as a local change pattern. Such a pattern, such as a sudden change in location coordinates in a short period of time, reflects the characteristics of abnormal fluctuations. The purpose of this processing is to capture weak intrusion signals as early as possible, thereby improving the technical effect of improving the system response speed.

[0089] In one possible implementation, a specific method for quantifying the deviation between the local change pattern and the baseline includes calculating the Euclidean distance to measure the degree of deviation. For example, an anomaly score is generated for the deviation value of the temperature parameter. A total score is obtained by accumulating the distance values ​​at multiple time points. If the total score exceeds a preset threshold, such as twice the standard deviation, it is determined to be a potential sign of sensor data injection attack. The score results are then compiled into preliminary threat information. This helps to quantify the risk level, provide actionable alarm basis, and thus enhance the reliability of the protection system.

[0090] Specifically, the process of classifying and summarizing the preliminary threat information according to parameter dimensions and time periods to generate a structured threat intelligence report can be achieved by using database query tools to classify information. For example, anomalies at the location dimension can be grouped by hourly segments. The report records in detail the parameter dimensions such as temperature deviation reaching its peak value and degree during a specific hour of transportation when the anomaly occurs. The resulting report is used for cargo transportation monitoring, which can optimize the adjustment of anomaly detection algorithms, bring the benefit of continuously improving intrusion identification capabilities, and support the robustness of the overall security mechanism from multiple perspectives.

[0091] The security event bus aggregates detection events from all levels for unified analysis, and the analysis determines the severity of the intrusion and activates the emergency response plan.

[0092] The system acquires event data from various layers, aggregating abnormal behavior records from the device, network, and application layers into a unified event bus. These records are initially filtered using pre-established event classification rules to distinguish between potentially intrusion-related abnormal records and ordinary operational deviation records, generating a preliminary event set. For this preliminary event set, a multi-dimensional comparison operation is performed, correlating unauthorized access records from the device layer, abnormal traffic characteristics from the network layer, and cargo status change records from the application layer to determine if there are any cross-layer abnormal consistency characteristics, forming a correlation judgment result. Based on the correlation judgment result, an intrusion severity score is calculated for each event set. The score is then graded using a preset threshold. If the score exceeds the threshold, the relevant event is marked as a high-risk event, generating a high-risk event list. For the high-risk event list, a preset emergency response plan is automatically triggered, including switching to a backup communication channel and restricting the permissions of suspicious devices. Simultaneously, the high-risk event list is pushed to a designated channel, ensuring that the security event bus completes the entire process from event aggregation to emergency response plan activation.

[0093] In one possible implementation, acquiring detection event data from various levels involves aggregating unauthorized contact alarms triggered by anti-tamper sensors at the device layer (e.g., in vehicle terminals), data packet integrity failure records detected by encrypted transmission protocols at the network layer, and records of abnormal changes in cargo status identified by machine learning models at the application layer, into a unified event bus. This aggregation can promptly integrate scattered information, thereby improving overall response efficiency. For example, in a cargo transportation scenario, if the device layer records a physical contact anomaly while the network layer simultaneously experiences a traffic peak, the event bus can quickly correlate them to avoid delays caused by isolated judgments. The beneficial effect is enhanced real-time performance of intrusion detection and reduced false alarm rate.

[0094] Specifically, the pre-established event classification rules filter records by defining thresholds for abnormal behavior, such as contact frequency exceeding the normal range or traffic fluctuations exceeding the baseline. This distinguishes between abnormal records that may involve intrusion, such as continuous GPS spoofing signal injection, and ordinary operational deviation records, such as brief signal interference. This approach provides the beneficial effect of precise filtering to focus on high-risk events and generate a preliminary set of events, further supporting the accuracy of subsequent analysis.

[0095] For example, performing a multi-dimensional comparison operation on the initially classified event set involves matching the timestamps of unauthorized contact records at the device layer with abnormal traffic characteristics at the network layer. For instance, if a contact record occurs within a specific time window and the traffic record shows a coordinate jump, it is considered a correlation. These are then compared with the cargo status change records at the application layer, such as the consistency between temperature anomalies and route deviations, thereby determining cross-layer abnormal consistency characteristics. This comparison can reveal hidden intrusion patterns, which is beneficial for early intervention and forms correlation judgment results to support the reliability of severity assessment.

[0096] In one possible implementation, this operation can also examine potential threats from multiple perspectives, such as event frequency and spatial correlation. For example, in transportation monitoring, if there is debugging port access at the device layer and local anomalies in state parameters at the application layer, consistency characteristics can be confirmed by comparison. The beneficial effect is that it improves the comprehensiveness of detection and mutually supports the defense depth from physical to application.

[0097] Specifically, the intrusion severity score of each event set is calculated based on the correlation judgment results by using a weighted summation method, such as giving higher weight to cross-layer consistency, to quantify the risk. For example, the score of a single layer anomaly is low, but the score increases after multiple layers are correlated. Then, a preset threshold is used to classify the event, such as if the score exceeds a certain limit. If it exceeds the limit, it is marked as a high-risk event and a list of high-risk events is generated. This calculation can bring the beneficial effect of quantitative assessment to achieve objective decision-making, avoid subjective bias, and ensure the targeting of emergency response.

[0098] For example, the scoring can be examined from multiple perspectives, such as the duration and scope of the event. For instance, when network layer anomalies are correlated with application layer deviations, the scoring can reflect potential sensor data injection attacks, thereby supporting the accuracy of the inventory generation and facilitating the efficient execution of subsequent contingency plans.

[0099] In one possible implementation, the automatic triggering of pre-defined emergency response actions for a high-risk event list includes switching to a backup communication channel to maintain data transmission continuity and restricting the permissions of suspicious devices to isolate risks. At the same time, the list is pushed to a designated channel, such as a security management personnel interface, to ensure that the security event bus completes the entire process from event aggregation to emergency response activation. This triggering can bring the beneficial effect of rapid isolation to minimize intrusion damage. For example, switching channels and pushing the list when GPS spoofing is detected can support system resilience from the communication and response sides, further emphasizing the overall effectiveness of defense in depth.

[0100] The cross-level linkage response includes locking interface access at the device layer, isolating abnormal traffic at the network layer, and suspending data updates at the application layer. This response mechanism ensures the integrity of cargo transportation data.

[0101] At the device layer, the interface access behavior of the in-vehicle terminal is monitored in real time. The built-in anti-disassembly sensor is used to obtain signals of unauthorized physical contact. If an abnormal contact behavior is detected, the locking mechanism is immediately triggered to restrict the access rights of all external interfaces, ensuring that the data interaction channel is not illegally exploited. At the network layer, the traffic characteristics of data transmission are continuously monitored. Taking the abnormal signals obtained after locking the interface at the device layer as the triggering condition, a traffic isolation program is started to separate the suspicious abnormal data packets from the normal communication path and prevent their further propagation, ensuring the purity of the transmission channel. At the application layer, the data updates of the cargo status and transportation trajectory are controlled. Based on the isolation results obtained after isolating abnormal traffic at the network layer, the relevant data update operations are suspended to prevent forged information from entering the core database, ensuring the integrity of the cargo transportation data. In the cross-layer联动 response, the status information after suspending the data update at the application layer is transmitted to each layer through a unified event bus, synchronously updating the locking status at the device layer and the isolation policy at the network layer, ensuring the continuous guarantee of the integrity of the cargo transportation data under multi-layer protection.

[0102] In a possible implementation, when the device layer monitors the interface access behavior of the in-vehicle terminal in real time, the built-in anti-disassembly sensor obtains signals of unauthorized physical contact by detecting the vibration of the shell or the opening of the cover. This kind of sensor usually adopts the micro-electromechanical system principle and generates an electrical signal when the vibration amplitude exceeds the preset threshold, thereby triggering the locking mechanism to restrict the access of external interfaces such as USB or serial ports. This can prevent attackers from injecting malicious code physically, which is beneficial to maintaining the security of the data interaction channel and avoiding the risk of tampering with the cargo transportation data.

[0103] Exemplarily, when the network layer continuously monitors the traffic characteristics of data transmission, after taking the abnormal signals obtained from the device layer as the triggering condition, a traffic isolation program is started. This program redirects the suspicious abnormal data packets to the isolation queue through software-defined network technology. For example, GPS data packets containing unmatched checksums are separated and prevented from entering the main communication path. This can ensure the purity of the transmission channel, reduce the impact of network layer intrusions on the overall system, and is beneficial to quickly responding to potential threats and maintaining the continuity of the data stream.

[0104] In a possible implementation, when the application layer controls the data updates of the cargo status and transportation trajectory, after taking the isolation results from the network layer as the basis, the relevant update operations are suspended. For example, when the isolation results show that there is forged sensor data, the system will freeze the database writing function to prevent abnormal information from contaminating the core database. This can ensure the integrity of the cargo transportation data and is beneficial to avoiding transportation delays or cargo losses caused by wrong information in decision-making.

[0105] For example, when application layer pause status information is transmitted through a unified event bus in a cross-level linkage response, the bus is a message middleware mechanism used for real-time event synchronization between different levels. For example, the pause information is broadcast to the device layer to update the lock status and the network layer to adjust the isolation policy. This enables collaboration under multi-layer protection, ensures the continuous protection of cargo transportation data integrity, helps to form a closed-loop defense system, and improves the overall intrusion resistance capability.

[0106] This invention provides an Internet of Things (IoT)-based cargo transportation monitoring system, comprising: The data acquisition and monitoring module is used to collect cargo transportation status data and monitor unauthorized physical contact behavior; The inspection and identification module is used to perform in-depth field-level inspections on data packets, identify timestamp deviations, coordinate jump characteristics, and forged sensor data packets of positioning spoofing signals, and transmit the data packets to the cloud platform through an encrypted channel; The independent analysis module is used to establish a baseline model of normal transportation trajectories and changes in cargo status, and to independently analyze the dimensions of cargo status parameters to discover abnormal patterns. The collaborative judgment module is used to share information and make collaborative judgments through the security event bus. When suspicious signs are detected, it triggers a cross-level linkage response mechanism.

[0107] Furthermore, the acquisition module specifically includes: Deployment unit for deploying physical tamper-proof sensors and security chips to monitor unauthorized access attempts to the hardware interface; An access attempt unit is used to identify debug port access attempts, which are determined by real-time monitoring of physical contact behavior; The status acquisition unit is used to collect cargo transportation status data, which includes cargo location and environmental parameters collected by sensors. A conversion unit is used to convert detected unauthorized physical contact behavior into detection events, which are then uploaded to the security event bus.

[0108] It should be noted that the modules or units provided in the embodiments of the present invention have the same implementation principle and technical effects as those in the aforementioned method embodiments. For the sake of brevity, the specific working process of the modules described above can be referred to the corresponding process in the aforementioned method embodiments, and will not be repeated here.

[0109] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A method for monitoring cargo transportation based on the Internet of Things, characterized in that, It includes: Collecting cargo transportation status data and monitoring unauthorized physical contact behavior; Performing in-depth field-level inspection on data packets, identifying and locating the timestamp deviation of spoofing signals, coordinate jump characteristics, and forged sensor data packets, and transmitting the data packets to the cloud platform through an encrypted channel; Establishing a baseline model for normal transportation trajectories and cargo status changes, independently analyzing the dimensions of cargo status parameters, and detecting abnormal patterns; Sharing information through a security event bus for collaborative judgment, and triggering a cross-level linkage response mechanism when suspicious signs are detected.

2. The method as described in claim 1, characterized in that, The collection of cargo transportation status data includes: Deploying physical anti-disassembly sensors and security chips to monitor unauthorized access attempts to hardware interfaces; Identifying access attempts to debugging ports, which are determined by real-time monitoring of physical contact behavior; Collecting cargo transportation status data, where the transportation status data includes the cargo position and environmental parameters collected by sensors; Converting the monitored unauthorized physical contact behavior into detection events, and uploading the detection events to the security event bus.

3. The method as described in claim 1, characterized in that, The in-depth field-level inspection on data packets to identify and locate the timestamp deviation of spoofing signals, coordinate jump characteristics, and forged sensor data packets includes: Implementing an encrypted transmission protocol and a data packet integrity verification mechanism; Deeply inspecting and analyzing the timestamp continuity and coordinate smoothness in the data packet fields to determine the timestamp deviation and coordinate jump characteristics; Identifying abnormal traffic characteristics in forged sensor data packets, where the abnormal traffic characteristics include data packet injection patterns; Configuring intrusion detection probes to perform real-time analysis on abnormal communication patterns and generating network layer detection events for uploading to the security event bus.

4. The method as described in claim 1, characterized in that, The establishment of a baseline model for normal transportation trajectories and cargo status changes includes: Training a normal transportation behavior model based on machine learning algorithms, where the normal transportation behavior model establishes multi-dimensional baselines for cargo positions and status parameters; Independently analyzing each parameter dimension of the cargo status change, comparing the actual parameters with the baseline model, and detecting abnormal patterns, where the abnormal patterns include cargo status changes and route deviations; Performing local abnormal pattern recognition on sensor data injection attacks and generating detection events for uploading to the security event bus.

5. The method as described in claim 1, characterized in that, The sharing of information through a security event bus for collaborative judgment includes: Aggregating each detection event and determining the severity of intrusion signs, where the severity is judged based on multi-level information collaboration.

6. The method as described in claim 4, characterized in that, The independent analysis of each parameter dimension of the cargo status change includes: For the dimension of cargo position parameters, obtaining the actual position data and comparing it with the baseline model; If the deviation of the actual position data exceeds the preset threshold, determining the abnormal pattern of route deviation; For the dimension of cargo status parameters, obtaining the sensor data and comparing it with the baseline model; If the local change of the sensor data is abnormal, identifying the data injection attack pattern.

7. The method as described in claim 1, characterized in that, The triggering of the cross-level linkage response mechanism includes: Switching to a backup communication channel; Locking the access permissions of suspicious devices; Notifying security management personnel to intervene and handle, where the notification is based on security event bus events.

8. The method as described in claim 3, characterized in that, The deep inspection and analysis of the timestamp continuity and coordinate smoothness in the data packet fields includes: Obtaining the continuous timestamp sequence in the data packet; Determine if there is a deviation in the timestamp sequence; if a deviation exists, mark it as a location spoofing signal. Obtain the coordinate sequence from the data packet; Determine if the coordinate sequence has a jump feature; if it does, then confirm that it is a forged feature.

9. A cargo transportation monitoring system based on the Internet of Things, used to implement the method as described in any one of claims 1-8, characterized in that, The system includes: The data acquisition and monitoring module is used to collect cargo transportation status data and monitor unauthorized physical contact behavior; The inspection and identification module is used to perform in-depth field-level inspections on data packets, identify timestamp deviations, coordinate jump characteristics, and forged sensor data packets of positioning spoofing signals, and transmit the data packets to the cloud platform through an encrypted channel; The independent analysis module is used to establish a baseline model of normal transportation trajectories and changes in cargo status, and to independently analyze the dimensions of cargo status parameters to discover abnormal patterns. The collaborative judgment module is used to share information and make collaborative judgments through the security event bus. When suspicious signs are detected, it triggers a cross-level linkage response mechanism.

10. The method as described in claim 9, characterized in that, The acquisition module specifically includes: Deployment unit for deploying physical tamper-proof sensors and security chips to monitor unauthorized access attempts to the hardware interface; An access attempt unit is used to identify debug port access attempts, which are determined by real-time monitoring of physical contact behavior; The status acquisition unit is used to collect cargo transportation status data, which includes cargo location and environmental parameters collected by sensors. A conversion unit is used to convert detected unauthorized physical contact behavior into detection events, which are then uploaded to the security event bus.

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