A Paradigm for DoS Attack Disclosure using Machine Learning Techniques

Abstract

Cybersecurity is one of the main concerns of governments, businesses, and even individuals. This is because a vast number of attacks are their core assets. One of the most dangerous attacks is the Denial of Service (DoS) attack, whose primary goal is to make resources unavailable to legitimate users. In general, the Intrusion Detection and Prevention Systems (IDPS) hinder the DoS attack, using advanced techniques. Using machine learning techniques, this study will develop a detection model to detect DoS attacks. Utilizing the NSL-KDD dataset, the suggested DoS attack detection model was investigated using Naive Bayes, K-nearest neighbor, Decision Tree, and Support Vector Machine algorithms. The Accuracy, Recall, Precision, and Matthews Correlation Coefficients (MCC) metrics are used to compare these four techniques. In general, all techniques are performing well with the proposed model. However, The Decision Tree technique has outperformed all the other techniques in all four metrics, while the Naive Bayes technique showed the lowest performance.