Comparison of Machine Learning Algorithms for Detection of Network Intrusions

Detecting, analyzing, and defending against network intrusions is an important topic in cyber security. Various detection systems have been designed using machine learning techniques that help detect malicious intentions of network users. We apply Recurrent Neural Networks (RNNs) and Broad Learning System (BLS) machine learning algorithms to classify known network intrusions. The developed models are trained and tested using the NSL-KDD dataset containing information about both intrusion and regular network connections. The algorithms are used to classify various types of intrusion classes and regular data and are compared based on accuracy and F-Score. Comparison results indicate that the BLS algorithm shows comparable performance with shorter training time.