Machine Learning-Based Intrusion Detection Systems for Enhancing Cybersecurity
Aezeden Mohamed, Janne Heilala, Nelson Sizwe Madonsela
Abstract
Intrusion detection systems (IDS) that are powered by machine learning have become an important tool for enhancing online security due to their capacity to detect and respond quickly to potential attacks. This is one of the reasons why IDS have grown so popular. In this article, we investigate how machine learning techniques might be applied to the challenge of intrusion detection by making use of the UNSW-NB15 dataset. The dataset, compiled by researchers at the University of New South Wales, includes benign and malicious network activity examples. The dataset known as UNSW-NB15 is utilised throughout this work in order to conduct an in-depth analysis and comparison of a number of different machine learning strategies. These strategies include decision trees, support vector machines (SVM), random forests, and deep learning models. The data collected from the network traffic is first subjected to preprocessing, after which feature engineering methods are utilised to extract the features of interest. In order to evaluate the usefulness of the constructed models, conventional metrics including as accuracy, precision, recall, and F1 score are utilised. The findings provide evidence that a number of different machine learning algorithms can be used to detect the numerous types of assaults that were represented in the UNSW-NB15 dataset. The study also analyses how different feature engineering strategies affect detection accuracy. Finding the best machine learning algorithms and feature engineering techniques to improve cybersecurity using the UNSW-NB15 dataset is an important outcome of this research that will help to move intrusion detection systems forward. The findings can be used to improve IDS, which helps strengthen the defenses of vital systems and networks against new cyber-attacks.