DNN-BASED INTELLIGENT INTRUSION DETECTION SYSTEM

Abstract

An intrusion detection system (IDS) that can quickly and automatically identify and categorize cyberattacks at the network and host levels is being developed using machine learning techniques. Nevertheless, no study to date has provided a thorough examination of how different machine learning algorithms perform over a range of publicly accessible datasets. In order to create a flexible and efficient intrusion detection system (IDS) that can identify and categorize unexpected and surprising cyberattacks, this study investigates deep neural networks (DNNs), a sort of deep learning model. The constant evolution of attacks and changes in network behavior need the evaluation of numerous datasets produced over time using both static and dynamic methodologies. Lastly, we provide Scale-Hybrid-IDS-AlertNet (SHIA), a highly scalable and hybrid DNNs framework that can be utilized in real-time to efficiently monitor network traffic and host-level events in order to preemptively notify potential cyberattacks.