中圖分類號(hào)： TN03；TP393 文獻(xiàn)標(biāo)識(shí)碼： A DOI：10.16157/j.issn.0258-7998.211394 中文引用格式： 郭衛(wèi)霞,，張偉,，楊國(guó)玉. 基于深度級(jí)聯(lián)網(wǎng)絡(luò)的入侵檢測(cè)算法研究[J].電子技術(shù)應(yīng)用，2021,，47(11)：68-72. 英文引用格式： Guo Weixia,，Zhang Wei,，Yang Guoyu. Research on intrusion detection algorithm based on deep cascade network[J]. Application of Electronic Technique，2021,，47(11)：68-72.
Research on intrusion detection algorithm based on deep cascade network
Guo Weixia,，Zhang Wei，Yang Guoyu
China Datang Corporation Science and Technology Research Institute,，Beijing 100043,，China
Abstract： Aiming at the problem that traditional machine learning algorithms are difficult to effectively extract features from massive multi-source heterogeneous network traffic data, and the classification effect is poor, an intrusion detection algorithm based on deep cascaded network is proposed, which uses the ability of neural network to automatically learn features. Convolutional neural network(CNN) is combined with long short-term memory network(LSTM) to extract the spatial and temporal characteristics of traffic data at the same time. And softmax is used for classification to improve the detection performance and generalization ability of the model. Finally, the algorithm is verified on the KDDCUP99 data set. The experimental results show that the intrusion detection model has a higher detection rate than SVM, DBN and other algorithms, with an accuracy rate of 95.39% and a false alarm rate of only 0.96%, which effectively improves intrusion detection classification performance.
Key words : intrusion detection；feature extraction,；convolutional neural network(CNN),；long short-term memory(LSTM)
