Machine Learning-Based Intrusion Detection System

Abstract

An explanation of IDSs is given in this work. Recent developments in technology have sparked worries about privacy and security. Network security is becoming more and more crucial as cyber networks and their uses grow. Intrusion detection systems (IDSs) that use machine learning are successful; in particular, the Supervised Model raises detection rates. People may find it challenging to comprehend their choices when faced with complex models. The majority of current model interpretation research is concentrated in domains including biology, computer vision, and natural language processing. Experts in cybersecurity find it difficult to maximize choices based on model evaluations in real life. A framework is proposed to handle these issues.