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

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.