Cyber Security Intruder Detection Using Deep Learning Approach

Abstract

Intrusion detection systems (IDS) are among the most promising approaches for securing data and networks; through the years, numerous categorization algorithms have been utilized in IDS. In recent years, as the alarming increase in computer connectivity and the substantial number of applications associated with computer technology have increased, the challenge of cyber security is constantly rising. A proper system of protection for numerous cyber-attacks is also required. This is how incoherence and attacks in a computer network are detected and IDS developed, which could play a possible role in cyber security. The authors used the CICIDS2017 dataset to meet this objective. It is the 2017 set of the Canadian Cyber Security Institute. The authors propose an IDS based on the deep learning technique to increase safety. The purpose was to use a neural network classifier to predict the network and web attacks.