Intrusion detection system using deep learning methodologies

Abstract

Intrusion Detection Systems (IDS) are the backbone that helps secure organizations and individuals from malicious internet traffic. Deep-Learning is another field of computer science that enables us to build productive Artificial Intelligence (AI) models that can be applied in a variety of fields. In this paper, we discuss the CSE-CIC-IDS2018 dataset for internet intrusion detection and provide a detailed study and analysis of various deep learning approaches that could be used to make a secure intrusion detection system. We test the accuracy of these algorithms and their effectiveness in detecting the malicious traffic for multiclass classification of the traffic in 14 different classes including benign traffic and malicious traffic. The outcome of which is to obtain a model framework based upon deep learning to build a usable model for an intelligent IDS that could potentially be used for real time data traffic security.