Network malicious behavior detection using bidirectional LSTM

Abstract

With the rapid development of the Internet, the methods of cyber attack have become more complex and the damage to the world has become increasingly greater. Therefore, timely detection of malicious behavior on the Internet has become an important security issue today. This paper proposes an intrusion detection system based on deep learning, applies bidirectional long short term memory architecture to the system, and uses the UNSW-NB15 data set for training and testing. Experimental tests show that the intrusion detection system can effectively detect the known or unknown malicious behavior of the network under the current network environment.