Machine Learning Techniques for Classifying Network Anomalies and Intrusions
Zhida Li, Ana Laura Gonzalez Rios, Guangyu Xu, Ljiljana Trajković · 2019
Using machine learning techniques to detect network intrusions is an important topic in cybersecurity. A variety of machine learning models have been designed to help detect malicious intentions of network users. We employ two deep learning recurrent neural networks with a variable number of hidden layers: Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU). We also evaluate the recently proposed Broad Learning System (BLS) and its extensions. The models are trained and tested using Border Gateway Protocol (BGP) datasets that contain routing records collected from Réseaux IP Européens (RIPE) and BCNET as well as the NLS-KDD dataset containing network connection records. The algorithms are compared based on accuracy and F-Score.