Self-adaptive safe self-healing system based on artificial intelligence

Abstract

The invention provides a self-adaptive safety self-healing system based on artificial intelligence, and aims to improve the safety protection capability and the self-repairing capability of an information system. The system comprises a client Agent module, a secure transmission module, a data analysis and anomaly detection module, a self-healing strategy management module, an execution module and a monitoring display module. The client Agent module collects data such as system logs, network flow and resource use, and predicts resource changes through a long short-term memory (LSTM) network model. And the secure transmission module adopts a TLS protocol and a block chain technology to ensure the integrity and security of the data. And the data analysis and anomaly detection module identifies threat behaviors through an Isolation Forest algorithm and a deep learning technology, and automatically generates a security event report. And the self-healing strategy management module generates a self-adaptive repair scheme based on an AI analysis result, and the system realizes efficient anomaly detection and self-healing repair through intelligent analysis and self-adaptive adjustment, and is suitable for dynamic security protection in a complex network environment.

Description

Technical Field

[0001] The present invention relates to the field of network security and intelligent control technology, and in particular to an adaptive security self-healing system based on artificial intelligence. Background Art

[0002] With the rapid development and widespread application of information technology, network security has become a major global issue. Traditional network security protection measures (such as firewalls, intrusion detection systems, and anti-virus software) mainly rely on rule configuration and passive defense strategies. Although these security systems have achieved certain results in defending against known threats, they cannot quickly adapt to and respond to dynamically changing threat environments due to their reliance on manual configuration and rule updates. Therefore, traditional protection methods often appear to be slow to respond when faced with complex, changeable, and highly intelligent security threats, making it difficult to ensure the security and stability of the system in a timely manner.

[0003] At present, with the development of technologies such as cloud computing, the Internet of Things, and big data, the scale and complexity of information systems are increasing, making the channels and methods of network attacks more diverse and the threat environment more challenging. In this case, a single rule-driven security system can no longer meet the high requirements for real-time and dynamic adaptability. Especially in large enterprises and complex business systems, the attack methods change rapidly, and traditional security measures are difficult to detect new attacks in a short period of time. It may even take hours or days to respond after the threat occurs, resulting in further expansion of the harm of security incidents.

[0004] In order to solve the above problems, artificial intelligence technology has been widely used in the field of network security in recent years. Through machine learning and deep learning models, AI technology can learn the patterns of security threats from massive data, automatically detect abnormal behaviors and generate adaptive protection strategies. However, the existing AI-based security systems still face some problems in practical applications: for example, the system has low adaptability, the repair strategy is too single, and the detection accuracy of unknown threats is insufficient. Therefore, designing an adaptive security system that can automatically analyze and self-repair has become an important research direction in the current field of network security.

[0005] Based on artificial intelligence and self-healing technology, this paper proposes an intelligent adaptive security self-healing system. By introducing AI models and adaptive analysis technology, it can achieve real-time detection, intelligent identification and self-healing of threats. The system can not only reduce manual intervention, but also respond in time when the system encounters unknown threats or abnormal situations, providing automated and real-time security protection. Summary of the invention

[0006] The present invention provides an adaptive security self-healing system based on artificial intelligence, which is designed to cope with complex and evolving network security threats and enhance the system's autonomous security protection and self-healing capabilities. Traditional network security protection methods rely on rule configuration and usually adopt a passive response method. They can only deal with known threats and cannot achieve real-time adaptive adjustment, making it difficult to meet security needs in a dynamic environment. Based on this, the present invention introduces artificial intelligence technology to construct a security system that can adaptively adjust, autonomously analyze and automatically repair.

[0007] The present invention includes the following main modules:

[0008] Client Agent module: This module runs on the target device and is responsible for collecting system-level and application-level data, including system logs, network traffic, resource usage, etc. Based on the long short-term memory network (LSTM) model, this module can predict the change trend of system resources (such as CPU, memory, disk I / O, etc.), and dynamically adjust the frequency and content of data collection according to the prediction results to ensure the accuracy and real-time performance of data collection.

[0009] Secure transmission module: To ensure the security and integrity of data transmission, the present invention adopts TLS protocol to encrypt the transmitted data and combines blockchain technology to further ensure the non-tamperability of the transmitted data.

[0010] Data analysis and anomaly detection module: This module uses the IsolationForest algorithm and deep learning technology for anomaly detection, and identifies potential threat behaviors and abnormal patterns through automatic analysis of client data. The AI ​​model embedded in the system can continuously learn and fine-tune threat patterns to ensure sensitivity and adaptability to emerging threat types.

[0011] Self-healing strategy management module: This module is used to formulate and execute system self-healing strategies. According to the abnormal detection results generated by the AI ​​model, it automatically triggers corresponding repair measures, including service restart, traffic isolation, traffic scheduling, and resource restriction. This module supports customized and adaptive strategy adjustment, and can dynamically optimize the repair plan according to the real-time security situation, reducing the need for manual intervention.

[0012] Execution module: The execution module uses pre-set policies and automation tools such as Ansible to implement policies, and supports custom execution windows (such as off-peak business periods). In addition, this module allows administrators to manually control the execution time of policies, achieving flexibility and controllability of policies.

[0013] Monitoring and display module: The system integrates a real-time monitoring screen to display key system indicators and security events. Administrators can monitor the system in real time, manually trigger self-healing operations, and view detailed security event logs through an intuitive interactive interface.

[0014] Through the coordinated operation of the above modules, the present invention realizes real-time detection and rapid response to network threats, and has high adaptive and self-healing capabilities. The system has significant advantages in improving system stability, reducing manual operation burden, and shortening problem response time.

[0015] The beneficial effects of the present invention are: DETAILED DESCRIPTION

[0016] The present invention builds an adaptive security self-healing system based on AI technology, the purpose of which is to automatically detect, analyze and quickly perform repair operations when the system is threatened or abnormal, so as to ensure the security and stability of the system. The overall architecture of the system includes client agent, security transmission, data analysis and self-healing modules, and all modules work together to achieve comprehensive self-healing functions.

[0017] 1. System structure and functional modules

[0018] The system architecture of the present invention includes the following modules:

[0019] Client Agent module: Deploy a lightweight Agent on the target machine to collect data such as system logs, network traffic, and resource usage. The Agent performs resource prediction based on the long short-term memory network (LSTM) and adjusts the data collection frequency. The formula is as follows:

[0020] i t =σ(W ix x t +b i +W ih h t-1 +b i )

[0021] f t =σ(W fx x t +b f +W fh h t-1 +b f )

[0022] o t =σ(W ox x t +b o +W oh h t-1 +b o )

[0023] h t =o t ×tanh(c t )

[0024] Here, W ix , W ih , b i Represents the input weights and bias terms in LSTM, and achieves adaptive data collection by predicting system resource usage trends.

[0025] Secure transmission module: Uses a transmission mechanism based on the TLS protocol to encrypt communication data to ensure the security and integrity of data transmission.

[0026] Fault recovery and self-repair module: Use daemon process for monitoring to ensure that the client agent can automatically recover when a crash occurs, so as to maintain the stable operation of the monitoring system.

[0027] Data analysis module: Uses the IsolationForest algorithm to detect anomalies and uses deep learning models (such as CNN) to extract features from network traffic to automatically identify and classify potential threats. The IsolationForest anomaly score calculation formula is as follows:

[0028]

[0029] 2. Specific implementation steps

[0030] Data collection and processing: The client agent runs on the target machine and collects data and transmits it to the central control node at the disposal layer. Data collection types include CPU usage, network load, etc. Data cleaning and standardization are performed during preprocessing.

[0031] Anomaly detection and behavior analysis: The data analysis module performs anomaly detection on system data. Convolutional neural network (CNN) is used to identify network attack patterns. The convolution operation formula is as follows:

[0032]

[0033] Self-healing strategy generation and execution: The self-healing strategy manager generates repair solutions based on AI analysis results, including patch application, service restart, etc. The autoencoder is used to reconstruct normal behavior and calculate errors to determine abnormal behavior.

[0034] Example

[0035] Example 1: In response to the peak load of system resources, the client agent automatically adjusts the collection frequency according to the LSTM prediction results. If the load is predicted to be too high, the agent will reduce the collection frequency and reduce the amount of data transmission to keep the system running smoothly.

[0036] Example 2: When an abnormal increase in the response time of a service is detected, the self-healing policy manager generates a service restart operation according to a set threshold. The execution module uses Ansible to implement the service restart operation to ensure rapid business recovery.

[0037] Example 3: When IsolationForest detects access behavior of a malicious IP, the system automatically triggers a traffic isolation policy to block the malicious IP and generates a report to send to the administrator.

[0038] Formulas and Algorithms

[0039] Convolutional neural network feature extraction: CNN is used to extract features from network traffic data, and its convolution formula is:

[0040]

[0041] Autoencoder: used to reconstruct normal data and calculate the reconstruction error. When the error exceeds the threshold, an abnormal alarm is triggered. Its loss function is as follows:

[0042]

[0043] IsolationForest: used to detect abnormal behavior, the formula is:

[0044]

[0045] The above implementation methods and steps demonstrate how the adaptive security self-healing system based on artificial intelligence technology of the present invention can achieve automatic detection and self-repair.

[0046] The present invention covers any substitution, modification, equivalent method and scheme made on the essence and scope of the present invention. In order to make the public have a thorough understanding of the present invention, specific details are described in detail in the following preferred embodiments of the present invention, but those skilled in the art can fully understand the present invention without the description of these details. In addition, in order to avoid unnecessary confusion about the essence of the present invention, well-known methods, processes, procedures, components and circuits are not described in detail.

[0047] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. An adaptive security self-healing system based on artificial intelligence, characterized in that: Includes the following modules: The client agent module is used to collect system logs, network traffic, resource usage and other data on the target device, and predict system resource changes based on the long short-term memory network (LSTM) model, so as to adaptively adjust the collection frequency; Secure transmission module, used to encrypt and transmit data through TLS protocol and blockchain technology to ensure the integrity and non-tamperability of data transmission; Data analysis and anomaly detection module, which is used to detect anomalies through the IsolationForest algorithm and deep learning models, identify potential threats and generate security incident reports; Self-healing policy management module, which is used to automatically generate and execute self-healing policies based on AI analysis results, including service restart, traffic isolation, and resource restrictions; An execution module, which is used to perform self-healing operations using automated tools according to preset policies or manual instructions; The monitoring and display module is used to display system status and security events in real time and supports administrator interactive control.

2. The system according to claim 1, characterized in that The client agent module predicts the resource usage on the target device through the long short-term memory network (LSTM) model, including CPU, memory, disk I / O and network load, and dynamically adjusts the data collection frequency according to the prediction results to optimize the efficiency of system resource utilization.

3. The system according to claim 1, characterized in that The data analysis and anomaly detection module further includes a convolutional neural network (CNN) model, which is used to extract spatial features from network traffic and identify network attack patterns; and combined with an automatic encoder to judge anomalies through reconstruction errors, thereby enhancing the accurate identification of threat patterns.

4. The system according to claim 1, characterized in that The self-healing strategy management module has an adaptive adjustment function and can dynamically adjust the repair strategy according to the real-time security situation, including automatic patch application, service restart, traffic isolation and bandwidth limitation, so as to improve the accuracy and immediacy of the repair measures.

5. The system according to claim 1, characterized in that The execution module is based on automation tools such as Ansible, supports configuration of execution windows during business off-peak periods and customized policy execution times, and allows administrators to manually adjust execution priorities.

6. The system according to claim 1, characterized in that The monitoring and display module includes a real-time monitoring screen for displaying key system indicators and security event status, and supports administrators to manually trigger or cancel self-healing operations and view detailed security event logs and repair strategies.

7. The system according to claim 1, characterized in that The secure transmission module implements end-to-end encryption through the TLS protocol and combines blockchain technology to ensure the integrity and non-tamperability of data during transmission.

8. The system according to claim 1, characterized in that The data analysis and anomaly detection module uses the IsolationForest algorithm to detect abnormal behaviors, and uses a deep learning model to classify abnormal events, automatically generate security event reports and update the anomaly detection model.

9. The system according to claim 1, characterized in that When generating a self-healing strategy, the self-healing strategy management module can realize adaptive optimization of the strategy according to the system analysis result, avoid repeated repair or false triggering, and further reduce manual intervention.

10. The system according to claim 1, characterized in that The self-healing strategy management module has a historical event learning function and can be fine-tuned and optimized according to the execution effect of previous repair strategies to achieve more accurate abnormal response and repair.

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