A Machine Learning Approach for Uniform Intrusion Detection

Intrusion Detection Systems are vital for computer networks as they protect against attacks that lead to privacy breaches and data leaks. Over the years, researchers have formulated intrusion detection systems (IDS) using machine learning and/or deep learning to detect network anomalies and identify four main attacks namely, Denial of Service (DoS), Probe, Remote to Local (R2L) and User to Root (U2R). However, the existing models are efficient in detecting just few of the aforementioned attacks while having inadequate detection rates for the rest. This deficiency makes it difficult to choose an appropriate IDS model when a user does not know what attacks to expect. Thus, there is a need for an IDS model that can detect, with uniform efficiency, all the four main classes of network intrusions. This research is aimed at exploring a machine learning approach to an intrusion detection model that can detect DoS, Probe, R2L and U2R attack classes with uniform and high efficiency. A multilayer perceptron was trained in an ensemble with J48 decision tree. The resultant ensemble learning model achieved over 85% detection rates for each of DoS, probe, R2L, and U2R attacks.