An Artificial Neural Network Technique for Prediction of Cyber-Attack using Intrusion Detection System

Abstract

An intrusion detection system, often known as IDS, is a piece of equipment or a piece of software that monitors a network or collection of devices in order to search for indications of possible intrusion. The frequency of cyber assaults has grown in recent years, and with it, the damage they do to society. The study of cyber security and the avoidance of cyber assaults, such as the use of intrusion detection as a defensive mechanism, is therefore needed. The internet services are widely used. Services based on computers, the internet, and other forms of technology are all considered part of the cyber world. The cyber world has advanced greatly thanks to new protocols and technologies. Cyber security is a major issue for every service that operates online. Network and host-based intrusion detection systems (NIDS/HIDS) are the backbones of any cyber security infrastructure. The NSL-KDD dataset is often used in algorithm research and verification and is widely employed in both the study and development of intrusion detection systems. In this study, we provide a neural network approach to intrusion detection system threat prediction. In this paper, the Python Spyder software is used for the simulation.