关键词: 行为识别, 入侵检测, 卷积神经网络, 网络异常分类, 双向长短时记忆网络

Abstract: In order to identify and prevent abnormal behavior and malicious intrusion in networks, a detection model based on Convolutional Neural Network (CNN) and Bidirectional Long ShortTerm Memory (BiLSTM) networks was constructed and applied to various types of Intrusion Detection Systems (IDS). Distinguished from traditional detection models, which suffer from reduced performance due to data redundancy, this model initially feeds the features into a CNN to generate feature mappings, effectively reducing the parameters of the recognition network and automatically eliminating redundant and sparse features. Subsequently, the processed features are used as inputs to the BiLSTM network to detect and recognize malicious behavior within the network. Finally, test results on the NSLKDD and KDD CUP99 datasets demonstrate that the proposed model surpasses existing models in terms of both time efficiency and accuracy, confirming its effectiveness in detecting malicious behavior and accurately classifying network anomalies.

Key words: behavior recognition, intrusion detection, convolutional neural network, network anomaly classification, bidirectional long short term memory network