Monitoring and early warning system and method for set top box

Abstract

The invention discloses a monitoring and early warning system and method for a set top box. The monitoring and early warning system comprises an operation state monitoring module, a network security monitoring module, a user behavior analysis module, an early warning decision module, an early warning pushing module and a privacy protection processing module. By monitoring the running state of the set top box in real time, the fault can be found and early warned at the initial stage of the fault, the user and maintenance personnel are helped to quickly position and solve the problem, and the influence of the equipment fault on the use of the user is reduced. The network connection state is monitored in real time, network security threats are effectively identified and pre-warned, personal information security of a user is protected, and the set top box is prevented from being utilized to perform malicious network activities. By analyzing the use behavior of the user, personalized early warning information can be provided for the abnormal use condition, the user experience is improved, and the legitimate rights and interests of the user are guaranteed. A plurality of early warning pushing modes such as television screen popup windows, mobile phone short messages and e-mails are adopted, so that a user can obtain early warning information in time and take corresponding measures.

Description

Technical Field

[0001] The present invention relates to the technical field of set-top boxes, and in particular to a monitoring and early warning system and method for set-top boxes. Background Art

[0002] With the popularization of digital television and network technology, set-top boxes, as key devices for receiving and processing multimedia information in the home, are playing an increasingly important role in people's lives. However, existing set-top boxes lack effective monitoring and early warning mechanisms during operation, and there are many problems:

[0003] 1. Operational failures are difficult to detect in time:

[0004] Existing technologies mainly rely on user feedback or regular manual inspections to detect equipment failures. For example, Chinese patent CN202410545780.1 discloses an operation control method for an IPTV set-top box, which solves the problem of program crashes by restarting the operating system. However, this method can only handle software anomalies and cannot monitor hardware parameters such as chip temperature and memory usage in real time. Although patent CN118764609A can collect device status information, it does not integrate hardware sensor data, resulting in hardware failures (such as chip overheating) that cannot be identified in the early stages. In addition, existing technologies generally lack the ability to deeply analyze system logs and are unable to mine potential software anomaly trends through logs.

[0005] 2. Hidden dangers in network security:

[0006] As an important node in the home network, the set-top box faces security threats such as malware intrusion and port scanning. For example, in 2024, the new malware Android.Vo1d has infected 1.3 million Android set-top boxes worldwide, and remote control is achieved by implanting system files. However, existing technologies, such as the network security protection measures of the Guangxi Radio and Television Bureau, are mainly aimed at the overall architecture of the radio and television network, and no dedicated intrusion detection system (IDS) and firewall are designed for set-top box devices. Although non-commercial network security audit systems can monitor network traffic, they are mainly used in public places and cannot adapt to the privacy protection needs in home environments. In addition, existing technologies lack the ability to analyze abnormal traffic in real time, for example, they cannot identify malicious communications based on covert channels.

[0007] 3. Lack of personalized usage monitoring:

[0008] The existing technology's analysis of user behavior remains limited to basic dimensions such as viewing time and program type. For example, a survey by the State Administration of Radio and Television showed that traditional user behavior analysis mainly relies on sampling surveys and viewing data statistics, and is unable to establish a dynamic user behavior model. Although the existing patent CN119652619A proposes risk behavior detection based on DLP technology, this technology is mainly aimed at enterprise IM communication scenarios and does not take into account the operating habits of set-top box users (such as remote control button frequency, on-demand preferences). In addition, the existing technology lacks the ability to provide real-time early warning for abnormal behavior, such as the inability to identify behaviors such as minors watching adult content for a long time or unauthorized users logging in. Summary of the Invention

[0009] The object of the present invention is to provide a monitoring and early warning system and method for a set-top box to solve the problems raised in the above background technology.

[0010] To achieve the above-mentioned object, the present invention provides the following technical solutions: a monitoring and early warning system for a set-top box, comprising an operation status monitoring module, a network security monitoring module, a user behavior analysis module, an early warning decision module, an early warning push module, and a privacy protection processing module;

[0011] The operating status monitoring module is used to monitor the hardware parameters and software operating status of the set-top box in real time;

[0012] The network security monitoring module is used to monitor the network connection status of the set-top box in real time, analyze network traffic, and use intrusion detection systems and firewall technologies to detect network security threats;

[0013] The user behavior analysis module is used to collect user behavior data on set-top boxes, establish user behavior models through data mining and machine learning algorithms, and identify abnormal usage behaviors;

[0014] The warning decision module is used to integrate the data of each module and determine whether to trigger a warning based on the preset warning rules. The warning rules can be customized;

[0015] The warning push module is used to push warning information to users through TV screen pop-up windows, mobile phone text messages, and emails when the warning decision module triggers a warning;

[0016] The privacy protection processing module is used to encrypt, store and transmit the collected user behavior data and network connection data to prevent data leakage; at the same time, it desensitizes user sensitive information to ensure user privacy security.

[0017] Preferably, the hardware parameters in the operation status monitoring module include chip temperature, memory usage, and CPU load, and the software operation status includes the operation status of the application and system log; and sensor technology is used to obtain hardware data, and software operation information is obtained through system interface calls.

[0018] Preferably, the network security monitoring module detects whether there are network security threats such as abnormal traffic, port scanning, and malware propagation; the intrusion detection system is specifically based on the Snort rule library, which can match and identify known attack patterns in real time; and the firewall technology restricts unnecessary network access and prevents potential attacks by configuring rules.

[0019] Preferably, the data mining and machine learning algorithms in the user behavior analysis module are specifically used to represent the linear relationship between the independent variable x and the dependent variable y. For single variable linear regression, the function is assumed to be:

[0020] h θ (x) = θ0 + θ1x,

[0021] Among them, θ0 and θ1 are the parameters of the model, h θ (x) is the predicted value;

[0022] Cost function: used to measure the difference between the predicted value of the hypothesis function and the true value. The commonly used cost function is the mean square error, and the formula is:

[0023]

[0024] Where m is the number of training samples, (x (i) -y (i) ) is the i-th training sample;

[0025] And build a decision tree, including information gain and information entropy, which are used to select the optimal partitioning attribute during the construction of the decision tree; perform information gain, and the calculation formula of information gain is:

[0026]

[0027] Among them, H(D) is the information entropy of the data set D, H(D v ) is a subset D of D with attribute a value v v Information entropy is the number of values ​​of attribute a, V is the number of values ​​of attribute a; Information entropy is used to measure the uncertainty of data, and the formula is:

[0028]

[0029] Among them, p i is the probability of belonging to the i-th class in the dataset.

[0030] Preferably, the early warning decision module is specifically the data of the comprehensive operation status monitoring module, the network security monitoring module and the user behavior analysis module, and determines whether to trigger an early warning based on the preset early warning rules; the early warning rules can be customized according to user needs and device characteristics. For example, when the CPU load of the set-top box is continuously too high, or a network threat of malware propagation is detected, or the user behavior model identifies abnormal login behavior, the early warning decision module will trigger an early warning and take corresponding security protection measures; the preset early warning rules are specifically based on a specific combination of thresholds and conditions, detecting abnormal network traffic from unknown sources exceeding a preset threshold, or abnormal login behavior identified by the user behavior model exceeding a certain number of times.

[0031] Preferably, the warning push module specifically sends warning information to users through multiple channels when the warning decision module triggers a warning, ensuring that users can be informed of potential security risks in a timely manner; the warning push module supports multiple push methods such as TV screen pop-ups, mobile phone text messages and emails, and users can choose the appropriate receiving method according to their own needs; in addition, the warning push module also has an intelligent push function, which can automatically adjust the format and content of the push information according to the receiving preferences and urgency set by the user, ensuring the accuracy and effectiveness of the warning information.

[0032] Preferably, the privacy protection processing module also has an intelligent privacy processing mechanism, which automatically adjusts the monitoring scope and the push content of the warning information according to the privacy level set by the user, so as to achieve a balance between privacy protection and monitoring needs.

[0033] An early warning method of a monitoring and early warning system for a set-top box comprises the following steps:

[0034] Step 1: Monitor the hardware parameters and software operating status of the set-top box in real time through the operating status monitoring module to obtain the real-time operating data of the set-top box;

[0035] Step 2: Use the network security monitoring module to monitor the network connection status of the set-top box in real time, analyze network traffic, and use intrusion detection systems and firewall technologies to detect network security threats and ensure the network security of the set-top box;

[0036] Step 3: Collect user behavior data on set-top boxes through the user behavior analysis module, apply data mining and machine learning algorithms to build a user behavior model, identify abnormal usage behavior, and promptly discover potential security risks;

[0037] Step 4: The early warning decision module integrates the data from each module and determines whether to trigger an early warning based on the preset early warning rules. When an abnormal situation is detected, the early warning decision module will trigger an early warning and initiate corresponding safety protection measures.

[0038] Step 5: When the early warning decision module triggers an alert, the early warning push module sends early warning information to users through multiple channels to ensure that users are informed of potential security risks in a timely manner. At the same time, the format and content of the push information are automatically adjusted according to the user's set receiving preferences and urgency, improving the accuracy and effectiveness of the early warning information.

[0039] Step 6: The privacy protection processing module encrypts the storage and transmission of collected user behavior data and network connection data to prevent data leakage; at the same time, it desensitizes user sensitive information to ensure user privacy security; in addition, according to the privacy level set by the user, it automatically adjusts the monitoring scope and the push content of warning information to achieve a balance between privacy protection and monitoring needs.

[0040] Compared with the prior art, the present invention has the following beneficial effects:

[0041] 1. Timely detection and resolution of equipment problems: By real-time monitoring of the operating status of the set-top box, it is possible to detect and warn of faults in the early stages of their occurrence, helping users and maintenance personnel to quickly locate and resolve problems, reducing the impact of equipment failures on user use and lowering the risk of equipment damage.

[0042] 2. Enhanced network security protection: Real-time monitoring of network connection status, effective identification and early warning of network security threats, protection of users' personal information security, and prevention of set-top boxes from being exploited for malicious network activities.

[0043] 3. Provide personalized usage monitoring: By analyzing user usage behavior, it can provide personalized early warning information for abnormal usage, improve user experience, and protect users' legitimate rights and interests.

[0044] 4. Diversified warning push methods: Use TV screen pop-up windows, mobile phone text messages, and emails to push warnings to ensure that users can obtain warning information in a timely manner and take corresponding measures. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 It is a system principle diagram of the present invention;

[0046] Figure 2 It is a flow chart of the method of the present invention. DETAILED DESCRIPTION

[0047] The following will be combined with the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0048] See also Figure 1-2 , the present invention provides a monitoring and early warning system and method for a set-top box, including an operation status monitoring module, a network security monitoring module, a user behavior analysis module, an early warning decision module, an early warning push module and a privacy protection processing module;

[0049] The operation status monitoring module is used to monitor the hardware parameters and software operation status of the set-top box in real time;

[0050] The network security monitoring module is used to monitor the network connection status of the set-top box in real time, analyze network traffic, and use intrusion detection systems and firewall technologies to detect network security threats;

[0051] The user behavior analysis module is used to collect user behavior data on set-top boxes, establish user behavior models through data mining and machine learning algorithms, and identify abnormal usage behaviors;

[0052] The early warning decision module is used to integrate the data of each module and determine whether to trigger an early warning based on the preset early warning rules. The early warning rules can be customized.

[0053] The warning push module is used to push warning information to users through TV screen pop-up windows, mobile phone text messages, and emails when the warning decision module triggers a warning;

[0054] The privacy protection processing module is used to encrypt, store and transmit the collected user behavior data and network connection data to prevent data leakage; at the same time, it desensitizes user sensitive information to ensure user privacy security.

[0055] The hardware parameters in the operation status monitoring module include chip temperature, memory usage, and CPU load. The software operation status includes the operation status of the application and system logs. Sensor technology is used to obtain hardware data, and software operation information is obtained through system interface calls.

[0056] The network security monitoring module detects network security threats such as abnormal traffic, port scanning, and malware propagation; the intrusion detection system is based on the Snort rule library and can match and identify known attack patterns in real time; the firewall technology restricts unnecessary network access and prevents potential attacks by configuring rules.

[0057] The data mining and machine learning algorithms in the user behavior analysis module are specifically used to represent the linear relationship between the independent variable x and the dependent variable y. For single-variable linear regression, the function is assumed to be:

[0058] h θ (x) = θ0 + θ1x,

[0059] Among them, θ0 and θ1 are the parameters of the model, h θ(x) is the predicted value;

[0060] Cost function: used to measure the difference between the predicted value of the hypothesis function and the true value. The commonly used cost function is the mean square error, and the formula is:

[0061]

[0062] Where m is the number of training samples, (x (i) -y (i) ) is the i-th training sample;

[0063] And build a decision tree, including information gain and information entropy, which are used to select the optimal partitioning attribute during the construction of the decision tree; perform information gain, and the calculation formula of information gain is:

[0064]

[0065] Among them, H(D) is the information entropy of the data set D, H(D v ) is a subset D of D with attribute a value v v Information entropy is the number of values ​​of attribute a, V is the number of values ​​of attribute a; Information entropy is used to measure the uncertainty of data, and the formula is:

[0066]

[0067] Among them, p i is the probability of belonging to the i-th class in the dataset.

[0068] The early warning decision module specifically uses the data from the comprehensive operation status monitoring module, network security monitoring module and user behavior analysis module to determine whether to trigger an early warning based on the preset early warning rules. The early warning rules can be customized according to user needs and device characteristics. For example, when the CPU load of the set-top box is continuously too high, or a network threat of malware propagation is detected, or the user behavior model identifies abnormal login behavior, the early warning decision module will trigger an early warning and take corresponding security protection measures. The preset early warning rules are specifically based on specific thresholds and condition combinations, detecting abnormal network traffic from unknown sources exceeding the preset threshold, or abnormal login behavior identified by the user behavior model exceeding a certain number of times.

[0069] Specifically, when the early warning decision module triggers an early warning, the early warning push module sends early warning information to users through multiple channels to ensure that users can be informed of potential security risks in a timely manner; the early warning push module supports multiple push methods such as TV screen pop-ups, mobile phone text messages and emails, and users can choose the appropriate receiving method according to their own needs; in addition, the early warning push module also has an intelligent push function, which can automatically adjust the format and content of the push information according to the receiving preferences and urgency set by the user, to ensure the accuracy and effectiveness of the early warning information.

[0070] The privacy protection processing module also has an intelligent privacy processing mechanism, which automatically adjusts the monitoring scope and the push content of warning information according to the privacy level set by the user, achieving a balance between privacy protection and monitoring needs.

[0071] An early warning method of a monitoring and early warning system for a set-top box comprises the following steps:

[0072] Step 1: Monitor the hardware parameters and software operating status of the set-top box in real time through the operating status monitoring module to obtain the real-time operating data of the set-top box;

[0073] Step 2: Use the network security monitoring module to monitor the network connection status of the set-top box in real time, analyze network traffic, and use intrusion detection systems and firewall technologies to detect network security threats and ensure the network security of the set-top box;

[0074] Step 3: Collect user behavior data on set-top boxes through the user behavior analysis module, apply data mining and machine learning algorithms to build a user behavior model, identify abnormal usage behavior, and promptly discover potential security risks;

[0075] Step 4: The early warning decision module integrates the data from each module and determines whether to trigger an early warning based on the preset early warning rules. When an abnormal situation is detected, the early warning decision module will trigger an early warning and initiate corresponding safety protection measures.

[0076] Step 5: When the early warning decision module triggers an alert, the early warning push module sends early warning information to users through multiple channels to ensure that users are informed of potential security risks in a timely manner. At the same time, the format and content of the push information are automatically adjusted according to the user's set receiving preferences and urgency, improving the accuracy and effectiveness of the early warning information.

[0077] Step 6: The privacy protection processing module encrypts the storage and transmission of collected user behavior data and network connection data to prevent data leakage; at the same time, it desensitizes user sensitive information to ensure user privacy security; in addition, according to the privacy level set by the user, it automatically adjusts the monitoring scope and the push content of warning information to achieve a balance between privacy protection and monitoring needs.

[0078] Example:

[0079] In this embodiment, the set-top box monitoring and early warning system and method are applied to an actual home network environment. The home network includes a smart TV, a set-top box, and multiple other smart devices connected to the home network. The set-top box is connected to a home router via a wireless network and is connected to the Internet.

[0080] In practice, the operating status monitoring module monitors the set-top box's hardware parameters, such as chip temperature, memory usage, and CPU load, as well as software status, such as application performance and system logs, in real time. If an anomaly is detected, such as a persistently high CPU load, the early warning decision module triggers an immediate alert. The early warning push module then sends a pop-up alert to the user's TV screen, alerting them to potential overheating or performance bottlenecks.

[0081] The network security monitoring module also monitors the set-top box's network connection status in real time, analyzes network traffic, and uses an intrusion detection system based on the Snort rule base and firewall technology to detect network security threats such as abnormal traffic, port scanning, or malware propagation. If abnormal network traffic from unknown sources exceeds a preset threshold, the early warning decision module will also trigger an alert and send a warning message to the user via text message, reminding them to pay attention to network security and take timely measures to prevent potential network attacks.

[0082] Furthermore, the user behavior analysis module collects behavioral data on set-top box users, such as the types of programs watched and the duration of viewing, and applies data mining and machine learning algorithms to build user behavior models. When the user behavior model identifies abnormal login behavior, such as unauthorized access or unusual login times, the early warning decision module triggers an alert and sends a detailed alert report to the user via email, including the time, location, and potential risk level of the abnormal login, helping users promptly identify and address potential security risks.

[0083] Throughout the monitoring and warning process, the privacy protection module encrypts the storage and transmission of collected user behavior and network connection data to prevent data leakage. It also desensitizes sensitive user information to ensure user privacy. Furthermore, based on the user's set privacy level, the module automatically adjusts the monitoring scope and the content of warning information pushed, achieving a balance between privacy protection and monitoring needs.

[0084] In summary, the set-top box monitoring and early warning system and method of this embodiment can promptly detect and resolve device problems, enhance network security, provide personalized usage monitoring, and ensure that users receive timely warning information through a variety of early warning push methods. At the same time, it emphasizes user privacy protection, achieving a balance between privacy protection and monitoring needs.

[0085] Although the present invention has been described in detail with reference to the aforementioned embodiments, it is still possible for those skilled in the art to modify the technical solutions described in the aforementioned embodiments, or to make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A monitoring and early warning system for a set-top box, characterized in that: It includes operation status monitoring module, network security monitoring module, user behavior analysis module, early warning decision module, early warning push module and privacy protection processing module; The operating status monitoring module is used to monitor the hardware parameters and software operating status of the set-top box in real time; The network security monitoring module is used to monitor the network connection status of the set-top box in real time, analyze network traffic, and use intrusion detection systems and firewall technologies to detect network security threats; The user behavior analysis module is used to collect user behavior data on set-top boxes, establish user behavior models through data mining and machine learning algorithms, and identify abnormal usage behaviors; The warning decision module is used to integrate the data of each module and determine whether to trigger a warning based on the preset warning rules. The warning rules can be customized; The warning push module is used to push warning information to users through TV screen pop-up windows, mobile phone text messages, and emails when the warning decision module triggers a warning; The privacy protection processing module is used to encrypt, store and transmit the collected user behavior data and network connection data to prevent data leakage; at the same time, it desensitizes user sensitive information to ensure user privacy security.

2. The monitoring and early warning system for a set-top box according to claim 1, characterized in that: The hardware parameters in the operation status monitoring module include chip temperature, memory usage, and CPU load, and the software operation status includes the operation status of the application and system log; and sensor technology is used to obtain hardware data, and software operation information is obtained through system interface calls.

3. The monitoring and early warning system for a set-top box according to claim 1, characterized in that: The network security monitoring module detects whether there are network security threats such as abnormal traffic, port scanning, and malware propagation; the intrusion detection system is specifically based on the Snort rule library, which can match and identify known attack patterns in real time; the firewall technology restricts unnecessary network access and prevents potential attacks by configuring rules.

4. The monitoring and early warning system for a set-top box according to claim 1, characterized in that: The data mining and machine learning algorithms in the user behavior analysis module are specifically used to represent the linear relationship between the independent variable x and the dependent variable y. For single-variable linear regression, the function is assumed to be: h θ (x)=θ0+θ1x, Among them, θ0 and θ1 are the parameters of the model, h θ (x) is the predicted value; Cost function: used to measure the difference between the predicted value of the hypothesis function and the true value. The commonly used cost function is the mean square error, and the formula is: Where m is the number of training samples, (x (i) -y (i) ) is the i-th training sample; And build a decision tree, including information gain and information entropy, which are used to select the optimal partitioning attribute during the construction of the decision tree; perform information gain, and the calculation formula of information gain is: Among them, H(D) is the information entropy of the data set D, H(D v ) is a subset D of D with attribute a value v v Information entropy is the number of values ​​of attribute a, V is the number of values ​​of attribute a; Information entropy is used to measure the uncertainty of data, and the formula is: Among them, p i is the probability of belonging to the i-th class in the dataset.

5. The monitoring and early warning system for a set-top box according to claim 1, characterized in that: The warning decision module specifically collects data from the comprehensive operation status monitoring module, the network security monitoring module and the user behavior analysis module, and determines whether to trigger a warning based on the preset warning rules; Warning rules can be customized according to user needs and device characteristics; the preset warning rules are based on a specific combination of thresholds and conditions, detecting abnormal network traffic from unknown sources exceeding the preset threshold, or abnormal login behavior identified by the user behavior model exceeding a certain number of times.

6. The monitoring and early warning system for a set-top box according to claim 1, characterized in that: Specifically, when the warning decision module triggers a warning, the warning push module sends warning information to the user through multiple channels to ensure that the user can be informed of potential security risks in a timely manner; the warning push module supports multiple push methods such as TV screen pop-ups, mobile phone text messages and emails, and users can choose the appropriate receiving method according to their own needs; in addition, the warning push module also has an intelligent push function, which can automatically adjust the format and content of the push information according to the receiving preferences and urgency set by the user, to ensure the accuracy and effectiveness of the warning information.

7. The monitoring and early warning system and method for a set-top box according to claim 1, characterized in that: The privacy protection processing module also has an intelligent privacy processing mechanism, which automatically adjusts the monitoring scope and the push content of warning information according to the privacy level set by the user, thereby achieving a balance between privacy protection and monitoring needs.

8. The early warning method of a monitoring and early warning system for a set-top box according to any one of claims 1 to 7, characterized in that: The following steps are involved: Step 1: Monitor the hardware parameters and software operating status of the set-top box in real time through the operating status monitoring module to obtain the real-time operating data of the set-top box; Step 2: Use the network security monitoring module to monitor the network connection status of the set-top box in real time, analyze network traffic, and use intrusion detection systems and firewall technologies to detect network security threats and ensure the network security of the set-top box; Step 3: Collect user behavior data on set-top boxes through the user behavior analysis module, apply data mining and machine learning algorithms to build a user behavior model, identify abnormal usage behavior, and promptly discover potential security risks; Step 4: The early warning decision module integrates the data from each module and determines whether to trigger an early warning based on the preset early warning rules; When an abnormal situation is detected, the early warning decision module will trigger an early warning and initiate corresponding safety protection measures; Step 5: When the early warning decision module triggers an alert, the early warning push module sends early warning information to users through multiple channels to ensure that users are informed of potential security risks in a timely manner. At the same time, the format and content of the push information are automatically adjusted according to the user's set receiving preferences and urgency, improving the accuracy and effectiveness of the early warning information. Step 6: The privacy protection processing module encrypts the storage and transmission of collected user behavior data and network connection data to prevent data leakage; at the same time, it desensitizes user sensitive information to ensure user privacy security; In addition, according to the privacy level set by the user, the monitoring scope and the push content of warning information are automatically adjusted to achieve a balance between privacy protection and monitoring needs.

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