Device for autonomous detection of cyber threats

Abstract

A device for the autonomous detection of cyber threats, consisting of: a housing that encloses a multitude of interconnected hardware components; a network interface unit configured to receive and send data packets from one or more communication networks; a data acquisition unit that is operationally connected to the network interface unit and configured to capture packet-level data, metadata, and system event logs; a preprocessing processor configured to analyze captured data, decodecode protocols, reconstruct communication flows, and generate structured data representations; a feature extraction processor that is operationally coupled with the preprocessing processor and is configured to calculate statistical, temporal and entropy-based features from the structured data representations; a storage unit consisting of volatile memory for real-time processing and non-volatile memory for storing historical data and learned patterns; an inference processor that is operationally coupled with the feature extraction processor and the storage unit, wherein the inference processor is configured to execute a variety of trained models to identify anomalous behavior based on deviations from stored patterns; a classification unit that is operationally coupled with the inference processor and configured to assign detected anomalies to one or more threat categories based on calculated confidence values; a response control unit configured to generate and transmit remedial actions, including blocking network traffic, isolating network segments, and terminating suspicious processes; and a control processor configured to coordinate the data flow between the network interface unit, the data acquisition unit, the preprocessing processor, the feature extraction processor, the inference processor, the classification unit, the response control unit, and the storage unit, with the device operating autonomously to detect and respond to cyber threats in real time.

Description

Application area of ​​the invention

[0001] The present invention relates to the field of cybersecurity and network protection systems, in particular a device for the autonomous detection of cyber threats, implemented as a hardware-integrated machine. The invention further relates to embedded processing systems configured for real-time monitoring, data analysis, anomaly detection, and automated defense against cyber threats in digital communication networks and networked computing environments. Background of the invention

[0002] The exponential growth of networked digital systems, cloud infrastructures, and IoT devices has significantly expanded the attack surface for cyber threats. Traditional cybersecurity mechanisms, such as signature-based intrusion detection systems, firewalls, and rule-based monitoring tools, have inherent limitations, including delayed response times, the inability to detect zero-day attacks, and reliance on predefined threat signatures. These systems often operate in a fragmented manner and require manual correlation and analysis, resulting in delayed countermeasures and increased vulnerability. Furthermore, existing solutions are typically software-centric and lack tight integration with dedicated hardware structures that enable high-speed, real-time analysis across multiple data streams.Given the increasing complexity of cyber threats, including polymorphic malware, advanced persistent threats (APTs) and coordinated multi-vector attacks, there is an urgent need for an autonomous, device-based solution that enables adaptive learning, real-time threat detection and integrated response without constant human monitoring.

[0003] The field of cyber threat detection has evolved significantly in recent decades due to the rapid expansion of digital infrastructures, cloud computing environments, and networked devices. Early cybersecurity solutions relied primarily on perimeter defense mechanisms such as firewalls and rule-based filtering systems designed to block unauthorized access based on predefined policies. While these approaches provided basic protection, they lacked the ability to analyze internal system behavior or detect sophisticated threats that circumvent perimeter controls. As cyberattacks became more complex, intrusion detection systems (IDS) and intrusion prevention systems (IPS) were introduced to monitor network traffic and identify malicious activity.These systems operate either as network-based IDS (NIDS) or host-based IDS (HIDS) and analyze data packets, system logs, and user activity to detect anomalies or known attack signatures.

[0004] Traditional intrusion detection systems (IDS) rely primarily on signatures and utilize predefined patterns of known attacks. While effective against previously identified threats, these systems have a critical limitation: they cannot detect unknown or zero-day attacks because these do not match existing signatures. Furthermore, attackers are constantly evolving their techniques, employing polymorphic malware and obfuscation strategies that render static signatures ineffective. Consequently, signature-based systems often fail to detect advanced persistent threats (APTs) and multi-stage attacks that exhibit subtle behavioral variations instead of explicit signatures. In addition to these detection limitations, traditional IDS systems suffer from high false positive rates. Legitimate activity is incorrectly classified as malicious, overloading security analysts and reducing operational efficiency.This phenomenon, known as alarm fatigue, can lead to critical threats being overlooked due to the many warning messages in the system.

[0005] To address these limitations, anomaly-based detection systems have been introduced. These systems define normal system behavior and flag deviations as potential threats. While anomaly detection improves the identification of unknown attacks, it presents its own set of challenges. These systems require extensive training data to accurately model normal behavior, and even minor deviations from legitimate usage patterns can trigger false positives. Furthermore, defining a comprehensive baseline in dynamic environments such as cloud infrastructures or IoT networks is inherently difficult, leading to inconsistencies in detection accuracy. Encrypted network traffic further complicates anomaly detection, as encryption restricts the visibility of packet contents, creating vulnerabilities that attackers can exploit.

[0006] With the advancement of artificial intelligence and machine learning, modern cybersecurity systems are increasingly relying on AI-powered detection mechanisms. Machine learning-based intrusion detection systems (IDS) utilize techniques such as decision trees, support vector machines, neural networks, and clustering to identify patterns in large datasets and uncover anomalies that indicate cyber threats. These systems offer enhanced detection capabilities by analyzing complex relationships within the data and adapting to evolving attack patterns. Despite these advancements, however, AI-based systems also have significant drawbacks. One of the biggest challenges is their reliance on large, high-quality datasets for training. In real-world scenarios, datasets are often unbalanced, incomplete, or biased, which can impair model performance and lead to inaccurate predictions.Furthermore, machine learning models are vulnerable to attacks in which attackers manipulate the input data to deceive the model and evade detection.

[0007] Another crucial limitation of AI-powered threat detection systems is their lack of interpretability. Many advanced models, particularly deep learning architectures, operate as "black boxes," making it difficult for security analysts to understand the reasons behind specific threat classifications. This lack of transparency can erode trust in automated systems and complicate regulatory compliance. Furthermore, the computational complexity associated with machine learning poses a significant challenge for real-time deployment. High-performance models often require substantial computing power, memory, and specialized hardware such as GPUs, which may not be feasible in resource-constrained environments like edge devices or distributed networks. This results in increased latency, which is detrimental in scenarios where immediate threat detection and response are critical.

[0008] Security Information and Event Management (SIEM) systems and Security Orchestration, Automation and Response (SOAR) platforms were designed to aggregate and correlate data from various sources, providing a centralized view of security events. While these systems improve visibility and facilitate incident response, they are often limited by their reliance on predefined correlation rules and manual configuration. Furthermore, integrating disparate data sources introduces complexity in data normalization and interoperability, resulting in inefficiencies in real-time analysis. Additionally, SIEM systems can generate large volumes of alerts, many of which may be redundant or irrelevant. This increases the workload for security teams and slows down response times.

[0009] Another emerging approach to detecting cyber threats is the use of honeypots and decoy technologies. These are designed to lure attackers into controlled environments for monitoring and analysis. While effective at understanding attacker behavior and identifying new attack vectors, these systems are inherently reactive and limited in scope, relying on attackers' interaction with the decoy systems. Furthermore, maintaining and updating honeypot environments requires ongoing effort and expertise, making them less suitable for large-scale deployments.

[0010] Despite the integration of various detection techniques, existing cybersecurity solutions often operate in a fragmented manner, lacking seamless coordination between components such as intrusion detection systems (IDS), firewalls, endpoint detection systems, and threat intelligence platforms. This fragmentation leads to delays in threat analysis and response, as data must be manually analyzed and interpreted across different systems. Furthermore, increasing network traffic and the growing number of connected devices present scaling challenges, as traditional and AI-based systems struggle to process large datasets in real time without loss of accuracy.

[0011] In addition to technical limitations, operational challenges further impair the effectiveness of existing cyber threat detection systems. These include the need for qualified personnel to manage and interpret system outputs, the complexity of system configuration and maintenance, and the high costs of deploying and operating advanced security infrastructures. Given the ever-increasing complexity and scale of cyber threats, it is clear that existing solutions are insufficient to guarantee fully autonomous, adaptive, and real-time threat detection.

[0012] Therefore, there is a need for a structurally integrated device that overcomes the aforementioned limitations by combining hardware-accelerated processing, adaptive intelligence, real-time analytics, and autonomous response mechanisms within a unified architecture. Such a device would enable the efficient detection of known and unknown threats, reduce false alarms, improve scalability, and minimize reliance on human intervention. This would close the critical gaps in existing cybersecurity technologies. Summary of the invention

[0013] The present invention describes a device for the autonomous detection of cyber threats. It consists of a structurally integrated hardware machine configured for the continuous monitoring, behavioral analysis, anomaly detection, and automated defense against cyber threats. The device comprises a multi-layered architecture within a secure enclosure. This architecture includes a data acquisition module, a feature extraction engine, an adaptive inference engine, a threat classification unit, and a response control module. The device is also equipped with hardware-accelerated processing units, storage subsystems, and secure cryptographic modules to ensure high processing speed and data integrity.The invention utilizes a combination of machine learning models, graph-based behavioral analysis, and temporal correlation methods to detect anomalies and predict potential threats. The device operates autonomously by continuously updating its internal models based on observed data patterns, thus enabling the detection of both known and unknown threats. Furthermore, the device features communication interfaces for integration with external systems and supports distributed deployment across network nodes for scalable threat data exchange.

[0014] The present invention aims to provide a device for the autonomous detection of cyber threats, structurally integrated as a self-contained machine, capable of continuously monitoring, analyzing, and defending against cyber threats in real time without requiring constant human intervention. A further aim of the invention is to develop a hardware-based system that combines high-speed data acquisition, feature extraction, and adaptive inference in a unified architecture to enable the efficient processing of large volumes of network traffic and system data. Another aim of the invention is to provide an intelligent detection mechanism that, using behavioral analysis, machine learning models, and dynamic pattern recognition techniques, can identify both known and unknown threats, including zero-day attacks and advanced persistent threats (APTs).

[0015] A further objective of the invention is to reduce false alarms and improve detection accuracy through the integration of multi-layered analysis frameworks, including statistical, temporal, and graph-based correlation models, thereby increasing reliability in complex and dynamic network environments. Another objective of the invention is to enable real-time response and defense by integrating a response orchestration module into the device. This module is configured to autonomously execute predefined and adaptive countermeasures such as network isolation, traffic blocking, and process termination. A further objective of the invention is to ensure data integrity, confidentiality, and secure operation through the integration of cryptographic modules and a hardware root of trust into the device architecture.

[0016] A further objective of the invention is to provide a scalable and modular device structure that supports integration into diverse network infrastructures, including cloud, edge, and IoT environments, while simultaneously enabling seamless expansion of processing and communication capacities. Another objective of the invention is to enable continuous learning and adaptation through the integration of online learning mechanisms that update internal models based on evolving threat patterns, thereby maintaining effectiveness against new cyber threats. A further objective of the invention is to minimize computational latency and increase processing efficiency through the use of hardware acceleration and optimized data pipelines.

[0017] A further objective of the invention is to facilitate the distributed deployment and shared exchange of threat information between multiple device instances, thereby improving overall detection coverage and resilience against coordinated attacks. Finally, an objective of the invention is to reduce operational complexity and reliance on qualified cybersecurity personnel by providing an autonomous, self-configuring, and self-optimizing system that enables robust and reliable detection of cyber threats across a wide range of operational scenarios. BRIEF DESCRIPTION OF THE IMAGE

[0018] These and other features, aspects and advantages of the present invention will be better understood if the following detailed description is read with reference to the accompanying drawing, in which the same symbols represent the same parts: Fig. Figure 1 shows a block diagram of a device for autonomous detection of cyber threats.

[0019] Furthermore, those skilled in the art will recognize that the elements in the drawing are simplified and not necessarily drawn to scale. For example, the flowcharts illustrate the process by highlighting the main steps to facilitate understanding of the present disclosure. With regard to the construction of the device, one or more components may be represented in the drawing by conventional symbols. The drawing may show only the specific details relevant to understanding the embodiments of the present disclosure, so as not to clutter the drawing with details that are already apparent to those skilled in the art from the description contained herein. Detailed description of the invention

[0020] To facilitate understanding of the principles of the invention, reference is made below to the embodiment shown in the drawing, which is described using specific terms. It is understood, however, that this does not limit the scope of protection of the invention. Rather, modifications and further developments of the depicted system, as well as further applications of the inventive principles shown therein, are conceivable, insofar as they would normally occur to a person skilled in the art in the field of the invention.

[0021] It will be clear to those skilled in the art that the foregoing general description and the following detailed description are exemplary and explanatory of the invention and are not to be understood as a limitation thereof.

[0022] References to “an aspect”, “another aspect”, or similar phrases in this description mean that a particular feature, structure, or property described in connection with the embodiment is included in at least one embodiment of the present disclosure. Therefore, phrases such as “in one embodiment”, “in another embodiment”, and similar expressions in this description may, but do not necessarily, all refer to the same embodiment.

[0023] The terms "includes," "comprehensive," or similar expressions denote non-exclusive inclusion. Thus, a procedure or method containing a list of steps does not only include those steps but may also include further steps not explicitly listed or inherent in the procedure or method. Likewise, the statement "includes..." for one or more devices, subsystems, elements, structures, or components, without further limitations, does not preclude the existence of other devices, subsystems, elements, structures, or components.

[0024] Unless otherwise defined, all technical and scientific terms used herein have the same meanings generally known to those skilled in the art in the field to which this invention belongs. The systems, methods, and examples described herein serve only for illustration and are not to be understood as limiting.

[0025] Embodiments of the present disclosure are described in detail below with reference to the attached drawing.

[0026] Fig.Figure 1 shows a block diagram of a device for autonomous detection of cyber threats.System 100 comprises: a housing (102) with several interconnected hardware components; a network interface unit (104) for receiving and sending data packets from one or more communication networks; a data acquisition unit (106) connected to the network interface unit, which captures packet-level data, metadata, and system event logs; a preprocessing processor (108) for parsing the captured data, decoding protocols, reconstructing communication flows, and generating structured data representations; a feature extraction processor (110) connected to the preprocessing processor, which computes statistical, temporal, and entropy-based features from the structured data representations; and a storage unit (112) with volatile memory for real-time processing and non-volatile memory for storing historical data and learned patterns.An inference processor (114), operationally connected to the feature extraction processor and the storage unit, is configured to execute multiple trained models to identify anomalous behaviors based on deviations from stored patterns. A classification unit (116), operationally connected to the inference processor, is configured to assign detected anomalies to one or more threat categories based on calculated confidence levels. A response control unit (118) is configured to generate and transmit remediation measures such as blocking network traffic, isolating network segments, and terminating suspicious processes.A control processor (120) configured to coordinate the data flow between the network interface unit, the data acquisition unit, the preprocessing processor, the feature extraction processor, the inference processor, the classification unit, the response control unit and the storage unit, with the device operating autonomously to detect and respond to cyber threats in real time.

[0027] In one embodiment, the inference processor comprises a plurality of parallel processing cores configured to perform temporal analysis on sequential data streams, wherein the sequential data streams comprise temporally ordered packet flows and event logs, and wherein the inference processor updates internal parameters using continuous input data without requiring external retraining.

[0028] In one embodiment, the feature extraction processor is configured to generate graph-based representations of communication relationships between network elements, where nodes represent devices or processes and edges represent communication interactions, and where the inference processor is configured to analyze structural variations in the graph-based representations to identify coordinated or multi-stage attacks.

[0029] In one embodiment, the preprocessing processor further includes logic configured to normalize heterogeneous data formats received by multiple network protocols, including Transmission Control Protocol, User Datagram Protocol, and application layer protocols, thereby enabling consistent feature generation across different communication environments.

[0030] In one embodiment, the classification unit is configured to calculate probability values ​​using a threshold-based decision logic, with multiple thresholds corresponding to different severity levels, and the classification unit dynamically adjusts the thresholds based on the historical recognition accuracy stored in memory.

[0031] In one embodiment, the response control unit is operationally connected to external network control systems and terminals and is configured to send encrypted control signals to enforce corrective actions, the encrypted control signals being generated using a cryptographic processor embedded in the device.

[0032] In an embodiment further comprising a security processor configured to establish a hardware trust anchor, wherein the security processor verifies the integrity of the firmware and the stored data during system initialization and operation, thereby preventing unauthorized modification of the operating parameters.

[0033] In one embodiment, the storage unit is structured in a hierarchical configuration, comprising a cache memory for immediate processing, a main memory for active data storage, and a persistent memory for long-term archiving, with the hierarchical configuration reducing latency when accessing frequently used data.

[0034] In one embodiment, the network interface unit comprises a variety of physical and wireless communication ports, including optical communication interfaces and high-frequency transceivers, enabling the use of the device in wired, wireless, and hybrid network environments.

[0035] In one embodiment, the control processor is further configured to enable communication between multiple instances of the device, with each instance exchanging detected threat information and learned patterns via secure communication channels, thus enabling distributed threat analysis and coordinated detection across network nodes.

[0036] The present invention relates to a device for the autonomous detection of cyber threats. This device is implemented as a tightly integrated hardware system whose coordinated operation of multiple processors and units enables the continuous monitoring, analysis, and defense against cyber threats. The operation of the device is controlled by a control processor that orchestrates the data flow and computational sequencing via a network interface, a data acquisition unit, a preprocessing processor, a feature extraction processor, an inference processor, a classification unit, a response control unit, and a storage unit. The workflow executed by the device begins with the high-speed acquisition of network traffic and system data. Subsequently, the raw data is progressively transformed into structured and analyzable representations, ultimately leading to threat detection and automated response.

[0037] Incoming data packets and system logs are initially received via the network interface unit, which supports multiple communication protocols and interfaces. The data acquisition unit continuously captures packet headers, payload metadata, flow identifiers, timestamps, and system events. The captured data is then forwarded to the preprocessing processor, which performs multi-stage analysis and normalization. The preprocessing processor reconstructs communication sessions by grouping packets based on source and destination identifiers, port numbers, and temporal proximity to data flows. It then decodes the logs to extract relevant attributes from various communication layers, including the transport and application layers.Data normalization serves to convert heterogeneous protocol formats into a uniform internal representation, thus enabling consistent further processing regardless of the source protocol.

[0038] After preprocessing, the structured data is passed to the feature extraction processor, which applies a series of transformation techniques to generate multidimensional feature vectors. These features include statistical attributes such as packet rate, byte distribution, arrival time variance, and payload entropy. Temporal features are calculated by analyzing sequential dependencies within communication flows, including burst patterns, session durations, and interaction periodicity. Additionally, the feature extraction processor creates relational representations in the form of dynamic graphs, where each node corresponds to a network unit or ongoing process, and edges represent communication or interaction events. Edge weights are assigned based on the frequency, duration, and data volume of data exchanges between nodes, thus encoding the intensity of behavior.

[0039] The inference processor receives feature vectors and graph representations and applies several trained computational models to identify anomalous patterns. The method used by the inference processor comprises a hybrid analysis approach that combines statistical anomaly analysis, temporal modeling, and structural graph evaluation. For statistical anomaly detection, the processor compares incoming feature vectors with base distributions stored in memory and calculates deviation values ​​using distance measures and probability estimates. For temporal analysis, the processor processes sequential data streams to identify irregular transitions or unexpected sequences in communication behavior and thus detect time-dependent attack patterns such as coordinated scanning or staggered intrusion attempts.

[0040] In parallel, graph-based analysis evaluates structural changes in the relational representation of network elements. The inference processor calculates metrics such as node centrality, clustering coefficients, and connectivity density to detect unusual communication topologies that might indicate coordinated attacks or lateral movement within a network. Subgraph matching procedures are used to identify patterns resembling known malicious behaviors, while emerging or rapidly evolving subgraphs that deviate from established norms undergo anomaly assessment. The inference processor integrates the results of these various analysis paths to generate an overall anomaly score for each observed element or interaction.

[0041] The system also incorporates adaptive learning, where the inference processor updates its internal parameters based on continuously incoming data. This is achieved through the incremental adjustment of statistical baseline values, the updating of relational graph structures, and the refinement of decision thresholds stored in memory. The memory manages both short-term and long-term data, enabling the system to distinguish between transient anomalies and persistent behavioral changes. The hierarchical memory organization ensures rapid retrieval of frequently used data, thus supporting the requirements of real-time processing.

[0042] The classification unit receives anomaly scores and associated feature data and applies threshold-based decision logic to categorize detected events into predefined or dynamically determined threat classes. The classification process involves mapping anomaly scores to severity levels using adaptive thresholds that are regularly recalibrated based on past detection performance. Confidence scores are calculated for each classification result, allowing the system to prioritize high-risk threats while suppressing low-confidence detections to reduce false positives.

[0043] After classifying a threat, the response center executes an automated countermeasure. This measure determines the appropriate countermeasures based on the type and severity of the detected threat. For example, in the case of anomalous network traffic, the response center generates control signals to block specific communication flows or restrict access from identified sources. In the case of compromised processes, the center initiates termination commands or isolates affected components from the network. The response center communicates with external systems and devices via secure, encrypted channels to ensure the integrity and confidentiality of the control instructions.

[0044] The device also features a security processor that establishes a hardware trust base, protecting all computing processes and stored data from manipulation. The integrity check performed by the security processor includes validating firmware signatures during initialization and continuously monitoring the system state during operation. This prevents unauthorized modifications that could compromise detection accuracy or system reliability.

[0045] In distributed deployment scenarios, multiple device instances communicate with each other via secure channels, exchanging threat intelligence and learned behavior patterns. The control processor synchronizes the shared data, enabling the joint detection of distributed or coordinated attacks across network segments. This process supports the aggregation of insights from multiple devices, thereby improving detection accuracy and providing a more comprehensive view of network behavior.

[0046] The device operates through a continuous cycle of data acquisition, preprocessing, feature extraction, inference, classification, and response, forming a closed, autonomous detection system. The integration of multiple analytical methods into a hardware-optimized architecture ensures high processing speed, low latency, and adaptability to evolving cyber threats. Thanks to its technical design, the device can operate autonomously, reducing reliance on manual intervention while maintaining a high degree of accuracy and robustness in complex and dynamic network environments.

[0047] The autonomous cyber threat detection device is implemented as a compact, self-contained machine. It consists of a structurally reinforced housing made of conductive and thermally optimized materials, ensuring electromagnetic shielding and heat dissipation. The housing contains several interconnected subsystems in a layered configuration. The base layer comprises a high-performance processing unit with a multi-core CPU and a parallel processing accelerator such as a GPU or a Tensor Processing Unit (TPU). This enables the simultaneous processing of large amounts of network data.

[0048] A data acquisition module is connected to multiple network interfaces, including Ethernet ports, wireless transceivers, and optical communication interfaces, and configured to capture packet data, metadata, and system logs from connected devices and networks. The captured data is transferred via an internal high-speed bus to a feature extraction engine, implemented as a combination of firmware and hardware logic circuits. The feature extraction engine performs preprocessing operations, including packet analysis, protocol decoding, flow reconstruction, entropy calculation, and statistical feature generation.

[0049] The processed data is then provided to an adaptive inference engine that incorporates several machine learning models. These include deep neural networks, recurrent neural networks for temporal pattern recognition, and graph-based models for analyzing relational behavior. The inference engine is also configured to dynamically update model parameters using online learning, thereby continuously adapting to evolving threat patterns. A dedicated storage subsystem stores historical data, learned patterns, and threat signatures in a hierarchical memory configuration with volatile memory for real-time processing and non-volatile memory for long-term storage.

[0050] A threat classification unit is operationally linked to the inference engine and configured to categorize detected anomalies into predefined or dynamically generated threat classes based on severity, type, and potential impact. The classification unit uses probabilistic evaluation mechanisms and confidence thresholds to ensure accurate threat identification while minimizing false positives.

[0051] The device also includes a response module configured to initiate automated countermeasures upon threat detection. These measures include isolating affected network segments, blocking malicious IP addresses, terminating suspicious processes, and generating alerts for external monitoring systems. The response module communicates with network control systems and endpoints via secure protocols, ensuring a coordinated and timely response.

[0052] A secure hardware root-of-trust module is integrated into the device to ensure the integrity and authenticity of firmware and data. The device also features a cryptographic engine configured for encryption, decryption, and digital signatures, enabling secure communication and data protection. Furthermore, the device supports distributed deployment, allowing multiple instances to communicate with each other to share threat intelligence and collectively enhance detection capabilities.

[0053] The overall operation of the device is controlled by a control logic module, implemented through a combination of firmware and embedded software, which manages data flow, resource allocation, model updates, and system state monitoring. The device's structural configuration ensures modularity and allows the integration of additional processing units, memory modules, or communication interfaces without significant redesign.

[0054] During operation, the device continuously monitors incoming and outgoing network traffic, extracts relevant characteristics, and analyzes behavioral patterns in real time. As soon as deviations from normal behavior or known malicious patterns are detected, the device independently classifies the threat and initiates appropriate countermeasures. Thanks to its adaptive learning capability, the device continuously improves its detection accuracy, thus offering a robust and scalable solution for modern cybersecurity challenges.

[0055] The device components are implemented as physically realizable hardware elements and integrated into the device. This ensures compliance with activation requirements and avoids abstraction. The housing forms a physical enclosure that supports and protects the internal circuitry. The network interface is implemented as a physical communication interface and includes transceivers, signal conditioning circuits, and interface controllers for processing packetized data over wired or wireless media. The data acquisition unit is implemented as a dedicated hardware circuit that captures, buffers, and records incoming data streams and system signals.The preprocessing processor, feature extraction processor, inference processor, classification unit, and control processor are each implemented as microelectronic processing circuits containing logic gates, arithmetic units, registers, and embedded memory elements on semiconductor substrates, interconnected via physical buses. The memory unit comprises physically separate volatile and non-volatile memory elements, enabling real-time buffering and persistent storage. The response control unit is implemented using hardware control circuitry capable of outputting electrical control signals to network switches, firewalls, and process control interfaces.

[0056] The drawing and the preceding description illustrate embodiments. Those skilled in the art will recognize that one or more of the described elements can be combined to form a single functional element. Alternatively, certain elements can be divided into several functional elements. Elements of one embodiment can be added to another. For example, the process flows described here can be modified and are not limited to the manner described herein. Furthermore, the actions of a flowchart need not be performed in the sequence shown; nor do all actions necessarily need to be carried out. Actions that do not depend on other actions can be performed in parallel with the other actions. The scope of protection of the embodiments is in no way limited by these specific examples. Numerous variations, whether explicitly stated in the description or not, such as...Differences in structure, dimensions, and materials are possible. The scope of protection of the embodiments is at least as comprehensive as described by the following claims.

[0057] The advantages, other benefits, and problem solutions have been described above with reference to specific embodiments. However, the advantages, benefits, problem solutions, and any components that can effect or enhance an advantage, benefit, or solution are not to be construed as critical, necessary, or essential features or components of the claims. REFERENCES 100 A device for the autonomous detection of cyber threats. 102 Housing structure 104 Network interface unit 106 Data acquisition unit 108 Preprocessing processor 110 Feature Extraction Processor 112 storage units 114 Inference processor 116 Classification unit 118 Response control unit 120 control processor

Claims

A device for autonomous detection of cyber threats, comprising: a housing enclosing a variety of interconnected hardware components; a network interface unit configured to receive and send data packets from one or more communication networks; a data acquisition unit operationally coupled to the network interface unit and configured to capture packet-level data, metadata, and system event logs; a preprocessing processor configured to analyze captured data, decode logs, reconstruct communication flows, and generate structured data representations; and a feature extraction processor operationally coupled to the preprocessing processor and configured to compute statistical, temporal, and entropy-based features from the structured data representations.a storage unit consisting of volatile memory for real-time processing and non-volatile memory for storing historical data and learned patterns; an inference processor operationally coupled with the feature extraction processor and the storage unit, the inference processor being configured to execute a variety of trained models to identify anomalous behavior based on deviations from stored patterns; a classification unit operationally coupled with the inference processor and configured to assign detected anomalies to one or more threat categories based on calculated confidence scores; a response control unit configured to generate and transmit remediation measures, including blocking network traffic, isolating network segments, and terminating suspicious processes;and a control processor configured to coordinate the data flow between the network interface unit, the data acquisition unit, the preprocessing processor, the feature extraction processor, the inference processor, the classification unit, the response control unit, and the storage unit, with the device operating autonomously to detect and respond to cyber threats in real time. Device according to claim 1, wherein the inference processor comprises a plurality of parallel processing cores configured to perform temporal sequence analysis on sequential data streams, wherein the sequential data streams comprise temporally ordered packet flows and event logs, and wherein the inference processor updates internal parameters using continuous input data without requiring external retraining. Device according to claim 1, wherein the feature extraction processor is configured to generate graph-based representations of communication relationships between network elements, wherein nodes represent devices or processes and edges represent communication interactions, and wherein the inference processor is configured to analyze structural variations in the graph-based representations in order to identify coordinated or multi-stage attacks. Device according to claim 1, wherein the preprocessing processor further comprises logic configured to normalize heterogeneous data formats received by multiple network protocols, including Transmission Control Protocol, User Datagram Protocol and application layer protocols, thereby enabling uniform feature generation across different communication environments. Device according to claim 1, wherein the classification unit is configured to calculate probability values ​​using a threshold-based decision logic, wherein multiple thresholds correspond to different severity levels and wherein the classification unit dynamically adjusts the thresholds based on the historical recognition accuracy stored in the memory unit. Device according to claim 1, wherein the reaction control unit is operationally connected to external network control systems and terminal devices and is configured to send encrypted control signals for the enforcement of remedial measures, wherein the encrypted control signals are generated by means of a cryptographic processor embedded in the device. Device according to claim 1, further comprising a security processor configured to establish a hardware trust anchor, wherein the security processor verifies the integrity of the firmware and the stored data during system initialization and operation, thereby preventing unauthorized modification of the operating parameters. Device according to claim 1, wherein the storage unit is structured in a hierarchical configuration comprising a cache memory for immediate processing, a main memory for active data storage, and a persistent memory for long-term archiving, wherein the hierarchical configuration reduces latency when accessing frequently used data. Device according to claim 1, wherein the network interface unit comprises a plurality of physical and wireless communication ports, including optical communication interfaces and high-frequency transceivers, enabling the use of the device in wired, wireless and hybrid network environments. Device according to claim 1, wherein the control processor is further configured to enable communication between multiple instances of the device, each instance exchanging detected threat information and learned patterns via secure communication channels, thereby enabling distributed threat analysis and coordinated detection across network nodes.

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