Analysis of Accuracy of Supervised Machine Learning Algorithms in Detecting Denial of Service Attacks

摘要

Intrusion Detection Systems are considered to be one of the primary methods for security attack detection. It is very challenging to design and implement intrusion detection systems that can detect the newer variants of security attacks with greater accuracy. This paper focuses on the detection of the Denial of Service Attacks in particular by DoS attack tools like Goldeneye, Slow HTTP test, Slow Loris, and Hulk. Further, this paper also focuses on the detection of one of the most important Web Application security attack due to the Heartbleed vulnerability. We have used the supervised machine learning algorithms like Support Vector Machine, Decision Tree, K-Nearest Neighbor for analyzing the accuracy of these models in classifying multi-class problems. One of the highlights of this paper is that the CICIDS2017 attack dataset has been used for evaluating the accuracy of various classification models. This research work holds significance as this focuses on the classification of attacks into five categories rather than a binary classification problem which is the focus of majority of the research works.