Multilayered Echo-State Machine: A Novel Architecture for Efficient Intrusion Detection

Abstract

Computers and other smart gadgets have become of a paramount importance in today’s transactions. Connected to the Internet, those devices offer the possibility to benefit from a myriad of electronic services, including social networking, banking, trade marketing, education and so on. Such activities are producing huge volume of information transiting with high velocity each day. Parallel to that, we have witnessed an epidemic increase in the number and the sophistication of cyberattacks, as they became more persistent and highly structured. In this context, modern intrusion detection systems are to be modeled so as to issue high detection rates in a tiny period of time in order to mitigate the risks. This paper is built on recurrent neural network with multilayered echo-state machine (ML-ESM) to model an intrusion detection. We assess our model on three publicly available data sets, namely, the DARPA KDD’99, NSL-KDD a reformed version of the latter, and UNSW NB 15. Performance metrics for both binary classification and multilabel classification are calculated and compared with those of some existing machine learning techniques and the recent state-of-the-art intrusion detection systems. Results indicate that the ML-ESM wins the challenge in both achieving a higher accuracy and considerably optimizing the processing time.

Keywords

• Intrusion detection
• recurrent neural networks
• multilayered echo-state machine