面向网络安全入侵检测的Bi-LSTM算法设计

Design of Bi-LSTM Algorithm for Network Security Intrusion Detection

于继江¹

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作者信息

1. 中国食品药品检定研究院,北京 102629
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摘要

传统的网络安全入侵检测系统在面对复杂多变的网络攻击时,往往存在误报率高及准确率低的问题.为了改善这种情况,结合循环神经网络(RNN)和卷积神经网络(CNN)的优点,并将长短期记忆(LSTM)网络模型进行改进,设计一种面向网络安全入侵检测的双向长短期记忆(Bi-LSTM)算法.经过实验验证,该算法在网络安全入侵检测中取得了显著的效果.在5种不同类型的攻击方式下,CNN-BiLSTM模型的准确率分别为94.8％、90.2％、96％、90.5％和93.7％,误报率分别为5.98％、7.2％、5.23％、6.84％和6.49％.这些数据表明,设计的Bi-LSTM网络在安全入侵检测中具有较高的准确率和较低的误报率,具有一定的应用价值和研究价值.

Abstract

Traditional network security intrusion detection systems often have high false alarm rate and low accuracy when fa-cing complex and ever-changing network attacks.In order to improve this situation,this study combines the advantages of re-current neural network(RNN)and convolutional neural network(CNN),and improves the long short-term memory(LSTM)model to design a bidirectional long short-term memory(Bi-LSTM)algorithm for network security intrusion detection.After experimental verification,this algorithm achieves significant results in network security intrusion detection.Under five different types of attack methods,the accuracy of the CNN-Bi-LSTM model reaches 94.8％,90.2％,96％,90.5％and 93.7％,re-spectively,and the false alarm rates are 5.98％,7.2％,5.23％,6.84％and 6.49％,respectively.These data indicate that the designed Bi-LSTM has high accuracy and low false alarm rate in network security intrusion detection,so it has certain ap-plication value and research value.

关键词

深度学习/卷积神经网络/入侵检测/循环神经网络/长短期记忆网络

Key words

deep learning/convolutional neural network/intrusion detection/recurrent neural network/long short-term memo-ry network

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出版年

2024

微型电脑应用

上海市微型电脑应用学会

微型电脑应用

CSTPCD

影响因子：0.359

ISSN：1007-757X

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