Deep Learning Approach For Intelligent Intrusion Detection System

Abstract

This paper focuses on preventing cyber attacks, which are common on any device connected to the internet. In order to create an intrusion detection system (IDS) that can recognize and differentiate cyber attacks at the network and host levels in a timely and automated manner, machine learning techniques are widely used. A deep neural network (DNN) is a form of deep learning model being researched for use in developing a scalable and efficient intrusion detection system (IDS) capable of detecting and classifying unexpected and unpredictable cyber attacks. Since network behavior is constantly changing and attacks are evolving at a rapid pace, it is critical to analyze various datasets that have been produced over time using both static and dynamic approaches. This type of research helps in the discovery of the most effective detection algorithm.