Abnormal behavior detection and prediction method based on artificial intelligence in big data environment

Abstract

The invention relates to a network security abnormal behavior prediction and identification method in a big data environment. The method comprises a data acquisition and preprocessing module, an abnormal behavior detection module, an abnormal behavior prediction module, a model optimization and updating module and an abnormal behavior classification and response module. Network traffic data are collected and preprocessed, the network traffic data are converted into feature vectors suitable for an artificial intelligence model, and real-time detection and prediction of abnormal behaviors in network traffic are realized by using deep learning and time sequence prediction technologies. By combining the self-supervised learning method and the online learning method, mass data can be adaptively processed in a big data environment, and accurate and rapid abnormal behavior detection and early warning can be provided. And abnormal behaviors are classified in time and corresponding protection measures are triggered, so that the intelligence and automation level of network security protection is effectively improved. The method has high efficiency, accuracy, real-time performance and adaptivity, network security events can be greatly reduced, and the security of a network environment is guaranteed.

Description

Technical Field

[0001] This invention relates to the field of network security, specifically to a method for predicting and identifying abnormal network security behavior in a big data environment. In particular, it addresses the automated monitoring and behavior prediction of massive network traffic data in a big data environment, using artificial intelligence technology to detect potential security threats and improve the intelligence and automation level of network security protection. Background Technology

[0002] With the rapid development of information technology, cybersecurity issues are becoming increasingly serious, and traditional cybersecurity protection methods are struggling to cope with massive network traffic and complex attack behaviors. Especially in big data environments, the sheer volume of network traffic data exacerbates the problems of low detection efficiency and poor accuracy associated with traditional security detection methods. Therefore, how to achieve efficient and accurate anomaly detection and prediction in big data environments has become an important topic in cybersecurity research.

[0003] In recent years, artificial intelligence (AI) technologies, especially machine learning and deep learning, have been increasingly widely applied in data analysis, demonstrating significant advantages in anomaly detection, pattern recognition, and prediction. However, existing AI-based anomaly detection methods typically rely on feature engineering and suffer from high computational complexity and poor real-time performance when processing large-scale data. Therefore, how to efficiently utilize AI technologies for cybersecurity protection in big data environments remains a pressing technical challenge. Summary of the Invention

[0004] The main objective of this invention is to provide an intelligent and efficient method for detecting and predicting abnormal network behavior by combining deep learning and time series prediction techniques. This method can not only detect existing abnormal behaviors in the network in real time, but also predict potential abnormal behaviors in advance, providing early warnings for network protection and ensuring the proactiveness and accuracy of network security protection.

[0005] To achieve the above objectives, the technical solution of the present invention includes the following core steps: Data Acquisition and Preprocessing: 1.1 Data Acquisition This invention first uses traffic monitoring devices (such as IDS / IPS systems, routers, etc.) installed at network nodes to collect various traffic data in the network in real time. This data includes, but is not limited to, the following: Network packets (such as TCP / IP packets, UDP packets, ICMP packets, etc.) Network connection information (source IP, destination IP, port, protocol type, etc.) Network traffic statistics (traffic rate, packet loss rate, etc.) User behavior data (login / logout records, data access records, etc.) 1.2 Data Preprocessing: The collected raw data undergoes data cleaning, noise reduction, format conversion, and standardization. Because network traffic data often contains significant amounts of noise and irrelevant information, feature extraction is essential to extract valuable data for subsequent analysis and modeling. Common data preprocessing operations include: Noise reduction: Use techniques such as low-pass filters and statistical methods to remove interference from abnormal fluctuations.

[0006] Standardization processing: Standardize various numerical characteristics of network traffic (such as traffic rate, packet size, etc.) to make them conform to the input requirements of machine learning models.

[0007] Feature extraction: Using methods such as time-domain analysis and frequency-domain analysis, important features related to network behavior are extracted. For example, statistical features such as the mean, variance, and peak value of traffic fluctuations.

[0008] Abnormal behavior detection: 2.1 Training and Application of Deep Learning Models: This invention employs deep learning algorithms, particularly Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and Long Short-Term Memory Networks (LSTM), to detect anomalous behavior in network traffic. These models can automatically learn the characteristics of network traffic and identify potential abnormal patterns.

[0009] Convolutional Neural Networks (CNNs): Used to process spatiotemporal features in network traffic, effectively extracting complex local features such as packet size, latency, and transmission frequency.

[0010] Recurrent Neural Networks (RNNs) and LSTMs: These models are well-suited for processing time-series data, capturing the temporal trends of network traffic and identifying early signs of attacks and abnormal fluctuations.

[0011] 2.2 Classification of Abnormal Behaviors: Based on anomaly detection, a deep learning model is used to classify the detected anomalous behaviors. Anomalous behaviors may include, but are not limited to: Denial-of-Service (DDoS) attack Port scanning Intrusion Attempt Malware propagation Data breach These classification results help the system automatically formulate response strategies based on the type of attack.

[0012] Abnormal behavior prediction: 3.1 Time Series Forecasting Model: Building upon anomalous behavior detection, this invention introduces time series analysis techniques to predict future network traffic using ARIMA models or deep learning-based prediction methods (such as LSTM networks). The primary purpose of this prediction is to identify potential anomalous behaviors in advance.

[0013] ARIMA model: Predicts future traffic trends by fitting historical network traffic data.

[0014] LSTM prediction model: Based on past traffic data, LSTM can effectively capture patterns over long periods of time, predict short-term changes in network traffic, and identify potential attacks in advance.

[0015] Model optimization and updates: 4.1 Self-supervised learning: As the network environment constantly changes, this invention utilizes a self-supervised learning mechanism to enable the model to incrementally learn based on new data. Whenever new network traffic data arrives, the system automatically updates the model to ensure it remains up-to-date.

[0016] 4.2 Model Optimization: By using techniques such as transfer learning and adversarial training, the system can continuously optimize existing deep learning models, thereby improving their detection accuracy and response speed.

[0017] Abnormal behavior response mechanism: 5.1 Multi-level response mechanism: The system will automatically select different protective responses for different types of abnormal behavior. Common response measures include: IP blocking: Block discovered malicious IP addresses to prevent further attacks.

[0018] Traffic filtering: Filters suspicious traffic in real time to reduce network load.

[0019] Alert notification: Send security alerts to administrators in a timely manner, and provide detailed attack information and potential impact analysis.

[0020] 5.2 Behavioral isolation: For severe attacks, isolation measures can be taken to separate the affected network areas from other areas and prevent the attack from spreading.

[0021] Innovation A combined approach based on deep learning and time series analysis: This invention combines deep learning with time series prediction technology, enabling both real-time detection of current anomalous behavior and prediction of future potential threats. Compared to traditional rule-based detection methods, this approach offers greater adaptability and flexibility, effectively addressing unknown types of attacks.

[0022] Adaptive model update mechanism: Through self-supervised learning and transfer learning, this invention can continuously optimize and update the model, enabling it to maintain efficient detection and prediction capabilities when facing complex and ever-changing network environments.

[0023] Tiered response and multi-layered protection: The multi-level response mechanism provided by this invention can provide customized protection based on different types of attacks, thereby improving the intelligence and accuracy of network security protection. Beneficial effects

[0024] Improve detection accuracy: By utilizing deep learning and time series analysis techniques, this invention enables high-precision detection and prediction of abnormal behavior in complex network environments.

[0025] Enhance cybersecurity protection capabilities: Predicting potential attacks in the network in advance and taking different protective measures according to the attack type can significantly improve the initiative and response speed of network protection.

[0026] Highly adaptable: As the network environment changes, the model of this invention can be automatically updated and optimized to ensure that it can cope with various new network security threats.

[0027] Automation and Intelligence: The system can automatically complete data collection, anomaly detection, behavior prediction, and response measures without human intervention, greatly reducing the cost of manual monitoring and the risk of misoperation.

Claims

1. An artificial intelligence based abnormal behavior detection and prediction method, characterized in that, The method comprises the following steps: Data collection: Collecting network traffic data; Data preprocessing: Denoising, feature extraction and standardization processing of the collected network traffic data to generate feature vectors for model training; Abnormal behavior detection: Using deep learning models to analyze the processed data to detect possible abnormal behaviors; Abnormal behavior prediction: Based on historical data, using time series prediction models to predict future network traffic and identify potential abnormal behaviors in advance; Model optimization and update: Optimizing and adjusting the model based on new data feedback to adapt to changes in different network environments; Abnormal behavior classification and response: Classifying the detected abnormal behaviors and triggering corresponding protection mechanisms.

2. The method of claim 1, wherein, The deep learning model uses one or more combinations of convolutional neural networks (CNN), recurrent neural networks (RNN) or long short-term memory networks (LSTM).

3. The method of claim 1, wherein, The time series prediction model uses ARIMA (Autoregressive Integrated Moving Average) model or deep learning-based variant model.

4. The method of claim 1, wherein, The model optimization and update step includes using self-supervised learning method to perform incremental learning with unlabeled data, continuously improving the detection accuracy and adaptability of the model.

5. The method of claim 1, wherein, The abnormal behavior classification includes classifying different types of attack behaviors and triggering corresponding defense measures according to the category, including but not limited to IP blocking, traffic filtering and alarm notification, etc.

6. The method of claim 1, wherein, The data preprocessing includes data denoising, standardization, feature selection and feature engineering, etc. to improve data quality and model training effect.

7. The method of claim 1, wherein, The data collection module collects real-time data through network traffic monitoring devices and supports large-scale parallel processing to adapt to the needs of big data environment.

8. The method of claim 1, wherein, The abnormal behavior detection module and the prediction module have a feedback mechanism, and the detection results are fed back to the prediction model to further optimize the prediction accuracy.

9. The method of claim 1, wherein, The protection mechanism includes hierarchical response to abnormal behaviors of different severity to achieve flexible and accurate network security protection.

10. An artificial intelligence based abnormal behavior detection and prediction system characterized in that, It includes: Data collection module for real-time collection of network traffic data; Data preprocessing module for processing the collected network traffic data to generate feature vectors; Abnormal behavior detection module for detecting abnormal behaviors in the network; Abnormal behavior prediction module for predicting possible future abnormal behaviors; Model optimization and update module for continuously optimizing and updating the detection and prediction model based on feedback data; Abnormal behavior classification and response module for classifying abnormal behaviors and triggering protection responses.