Retraction Notice: Neural Network based Intrusion Detection system for critical infrastructure
Anita Gehlot, Abhishek Joshi · 2022 IEEE 2nd Mysore Sub Section International Conference (MysuruCon) · 2022
Security is crucial in the internet age since the number of users increases quickly. One of the major problems of the current day is cybersecurity. The rapid increase and widespread usage of Internet over the past ten years have made system security weaknesses a crucial issue. An intrusion detection system to monitor unusual attacks across network security as well as unlawful access is the need of the hour in many critical infrastructures. Recently, a number of IDS experiments have been conducted. And to be aware of how machine learning techniques are now being used to handle the intrusion detection problem. IDS is frequently used to quickly and automatically identify and detect cyberattacks at the network and host stages. Deep learning techniques have significantly improved the performance of numerous applications of practical safety tools. It is regarded as the finest option for employing them for backpropagation methods in highly dimensional data to find complicated architecture. DL is indeed a subset of machine learning that deals with artificial intelligence. A technology called artificial intelligence makes it simple for machines to mimic people. Machine learning is a method for developing data algorithms, and deep learning is a subset of ML that draws inspiration from the architecture of the human mind. A neural network made of artificial neurons is this network structure. In this study we have proposed Neural network-based system for identifying intrusions in critical infrastructure. We have evaluated them based on significant metrics.