Machine Learning for Detecting Anomalies and Intrusions in Communication Networks

Abstract

Cyber attacks are becoming more sophisticated and, hence, more difficult to detect. Using efficient and effective machine learning techniques to detect network anomalies and intrusions is an important aspect of cyber security. A variety of machine learning models have been employed to help detect malicious intentions of network users. In this paper, we evaluate performance of recurrent neural networks (Long Short-Term Memory and Gated Recurrent Unit) and Broad Learning System with its extensions to classify known network intrusions. We propose two BLS-based algorithms with and without incremental learning. The algorithms may be used to develop generalized models by using various subsets of input data and expanding the network structure. The models are trained and tested using Border Gateway Protocol routing records as well as network connection records from the NSL-KDD and Canadian Institute of Cybersecurity datasets. Performance of the models is evaluated based on selected features, accuracy, F-Score, and training time.