Open Access

ARTICLE

A Novel Intrusion Detection Algorithm Based on Long Short Term Memory Network

Xinda Hao¹, Jianmin Zhou^2,*, Xueqi Shen¹, Yu Yang¹

1 School of Cyberspace Security, Beijing University of Posts and Telecommunications, Beijing, 100876, China
2 College of New Media, Beijing Institute of Graphic Communication, Beijing, 102600, China

* Corresponding Author: Jianmin Zhou. Email:

Journal of Quantum Computing 2020, 2(2), 97-104. https://doi.org/10.32604/jqc.2020.010819

Received 04 May 2020; Accepted 28 August 2020; Issue published 19 October 2020

Abstract

In recent years, machine learning technology has been widely used for timely network attack detection and classification. However, due to the large number of network traffic and the complex and variable nature of malicious attacks, many challenges have arisen in the field of network intrusion detection. Aiming at the problem that massive and high-dimensional data in cloud computing networks will have a negative impact on anomaly detection, this paper proposes a Bi-LSTM method based on attention mechanism, which learns by transmitting IDS data to multiple hidden layers. Abstract information and high-dimensional feature representation in network data messages are used to improve the accuracy of intrusion detection. In the experiment, we use the public data set KDD-Cup 99 for verification. The experimental results show that the model can effectively detect unpredictable malicious behaviors under the current network environment, improve detection accuracy and reduce false positive rate compared with traditional intrusion detection methods.

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Keywords

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