Machine Learning Based Intrusion Detection

Intrusion refers to any malicious activity done in order to access confidential data. An intrusion detection system (IDS) detects these attacks and, on detection, it reports them to the administrator. It does so either by comparing the new activity with the past activities or by analyzing the network performance. This system forms a part of the vast security module and works with several other such sub-modules in order to make sure that these unwanted intrusions do not go unreported. The system that has been implemented in this paper is an anomaly-based Intrusion Detection System (IDS). The primary purpose of this implementation is to develop an efficient system in order to detect any external or internal unauthenticated activity. Several models have been experimented with in order to find one that suits the system the best and gives a good enough accuracy. The models that have been experimented with include Logistic Regressor, Random Forest Classifier, K Nearest Neighbor classifier, XGBoost Classifier, Gaussian Naive Bayes Classifier and a Multi-Layer Perceptron Classifier (MLP). Further, the accuracy of each of these models was calculated, and a comparative analysis was done between the performance of these models. The model that performed the best in this particular use case was the Random Forest Classifier giving an accuracy of 99.8% and a macro average F1-Score of 0.98.