计算机技术与发展2025，Vol.35Issue(5)：67-75,9.DOI:10.20165/j.cnki.ISSN1673-629X.2024.0409
基于改进的GAN与DL融合的入侵检测方法
Intrusion Detection Method Based on Improved GAN and DL Fusion
摘要
Abstract
In deep learning-based intrusion detection systems,the issue of data imbalance significantly affects the accuracy of models,leading to poor performance in recognizing minority class attack samples.To address this issue,an improved method called IGAN-CLSTM,which integrates an optimized Generative Adversarial Network(GAN),Convolutional Neural Network(CNN),and Bidirectional Long Short-Term Memory Network(Bi-LSTM),is proposed.Specifically,an optimized GAN approach is first utilized to enhance the quantity of anomalous(attack)samples,thereby balancing the data distribution and improving the model's generalization ability.Subsequently,the advantages of CNN in spatial feature extraction and Bi-LSTM in sequential data processing are combined to fa-cilitate deep feature learning and temporal modeling of complex attack patterns,significantly enhancing the model's recognition capability.Finally,classification is performed using a Fully Connected Network(FCN).To validate the effectiveness of the proposed method,ablation experiments are conducted on two large-scale datasets,UNSW-NB15 and CSE-CIC-IDS2018.The results indicate that while maintaining the same parameters and scale of the deep learning model,the proposed method outperforms other models across multiple evaluation metrics,particularly demonstrating a significant improvement in precision for minority classes in multi-class classification tasks.This showcases its potential and practical value for real-time intrusion detection and provides strong support and reference for further exploration in the field of cybersecurity.关键词
深度学习/入侵检测/生成对抗网络/卷积神经网络/双向长短期记忆网络/全连接网络Key words
deep learning/intrusion detection/generative adversarial networks/convolutional neural network/bidirectional long short-term memory network/fully connected network分类
计算机与自动化引用本文复制引用
李柄军,陈帅良,段晓英,康凯..基于改进的GAN与DL融合的入侵检测方法[J].计算机技术与发展,2025,35(5):67-75,9.基金项目
宁夏自然科学基金(2023AAC03333) （2023AAC03333）
