Machine Learning Based Intrusion Detection System

In today's digital landscape, the increasing frequency and sophistication of cyberattacks have highlighted the necessity for robust intrusion detection systems (IDS) to safe- guard sensitive data and critical infrastructure. Traditional rule- based intrusion detection methods have proven to be insufficient in handling the evolving nature of attacks. As a result, machine learning techniques have gained prominence due to their ability to adapt and learn from data, thereby enhancing detection accuracy and efficiency. This paper aims to provide a comprehensive overview of machine learning-based intrusion detection systems, including their significance, challenges, methodologies, and future directions.