一种基于增量网络攻击分析学习的网络安全防御方法和系统
By using incremental network attack analysis and learning, and leveraging deep autoencoders and deep reinforcement learning models to dynamically adjust feature weights and defense strategies, this approach solves the problems of identifying new types of attacks and inflexible responses in existing technologies, thus achieving efficient network security defense.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- JIANGSU SIJI TECH SERVICE CO LTD
- Filing Date
- 2025-03-25
- Publication Date
- 2026-07-17
AI Technical Summary
Existing network security defense technologies are unable to effectively identify new and variant attacks. Traditional machine learning models lack dynamic response capabilities, and deep learning models are not flexible enough in updating when faced with real-time network traffic, resulting in delayed responses.
We employ an incremental network attack analysis and learning approach. By using a deep autoencoder to reduce the dimensionality of traffic features, we construct an initial attack feature library. Then, by combining a sliding time window and a deep reinforcement learning model, we dynamically adjust feature weights and defense strategies, and update defense measures in real time.
It enables rapid identification and response to new types of attacks, enhances the flexibility and adaptability of network defense, reduces false alarm rates, and improves the accuracy and robustness of detection.
Smart Images
Figure CN120200810B_ABST