Machine Learning for Intrusion Detection and Network Performance

Abstract

Security mechanisms constitute a vital part towards the design of a computer network in modern-day organisations. In particular, the implementation of the principle of layered security to harden the network against attacks requires the introduction of checkpoints into the connectivity of components, which inevitably has an adverse impact on network performance. Moreover, advanced intrusion detection systems (IDSs) could be effectively utilised at the checkpoints of the computer network, leading to the analysis and determination of ‘optimal’ security versus performance trade-offs. To this end, a novel quantitative method is proposed for the evaluation and prediction of the aforementioned trade-offs supported by Machine Learning Algorithms (MLAs), such as Random Forest (RF) classifier, Logistic Regression (LR) and Naïve Bayes (NB) algorithms for Network Intrusion Detection Systems (NIDSs). In this context, a minimisation is employed in order to reduce the high dimensionality of datasets using Feature Selection (FS) for the dataset. Moreover, highly weighted features are used to keep false-negative (FN) low and increase the accuracy of MLAs towards the establishment of ‘optimal’ performance versus security tradeoffs. Typical numerical experiments are carried out indicating that the RF classifier is the best MLA, incorporating a subset of 19 selected features and identifying different types of attacks correctly with 99.9% of accuracy.