A decisive approach to intrusion detection system using machine learning model

Abstract

In the current times, cyber-attacks are becoming more sophisticated and modern; this has increased the threat in precisely detecting intrusion. Inability to restrict the intrusions can degrade the validity of security administrations and loss of data confidentiality, integrity, and availability. Hence, Detection is an important step in avoiding such attacks; once an issue is detected properly, effective countermeasures can be deployed. Intrusion Detection Systems (IDS) plays a very crucial role and help to detect incoming attacks. Network-based IDS is an important tool used to protect the computer network against malicious attacks and threats. An application of the Bayesian Information Gain concept for feature selection and Deep Recurrent neural network (Deep RNN) to model building is proposed in this paper to increase the efficiency of a network intrusion detection system. The Bayesian Information Gain concept is used to select important features, which have high predictive power. Deep RNN classifier successfully plays out the intrusion detection system measure utilizing the hidden layers dependent on the weight and bias-related with the classifier. Appropriately, the Adam optimization algorithm to build the precision of the model ideally tunes the weights and bias.

Human-Centered Efficient Explanation on Intrusion Detection Prediction

“…Research on and implementation of AI systems are critical in light of the benefits of lowering the labor that people must undertake in a variety of industries. Many studies on designing an intrusion prevention system in combination with an AI model have been conducted in order to develop and improve this environment [1][2][3]. Most works aim to improve the performance of the system by building various machine learning and deep learning models and building new access systems.…”