Implementation of machine learning algorithms on cicids-2017 dataset for intrusion detection using WEKA

Abstract

For protecting and securing the network, with Intrusion Detection Systems through hidden intrusion has become a popular and important issue in the network security domain. Detection of attacks is the first step to secure any system. In this paper, the main focus is on seven different attacks, including Brute Force attack, Heartbleed/Denial-of-service (DoS), Web Attack, Infiltration, Botnet, Port Scan and Distributed Denial of Service (DDoS). We rely on features derived from CICIDS-2017 Dataset for these attacks. By using various subset based feature selection techniques performance of attack has been identified for many features. Using these techniques, it has been determined the appropriate group of attributes for finding every attack with related classification algorithms. Simulations of these techniques present that unwanted feature can be removed from attack detection techniques and find the most valuable set of attributes for a definite classification algorithm with discretization and without discretization, which improve the performance of IDS.