Intrusion Detection using Machine learning

The fast development in the use of computer networks raises concerns about network availability, integrity, and confidentiality. This requires network managers to use various types of intrusion detection systems (IDS) to monitor network traffic for unauthorized and malicious activity. An intrusion is a malicious breach of security policy. As a result, an intrusion detection system monitors network traffic flowing through computer systems to look for malicious actions and recognized dangers, providing alarms when it detects them. Mining approaches can be highly useful in the development of an intrusion detection system. In order to detect intrusions, network traffic might be categorized as normal or anomalous. After examining over twenty-five studies, we chose the top three classification algorithms in our paper: Logistic Regression, Naive Bayes, and SVM. This study compares the top three classification algorithms based on their performance criteria to determine the best suited method available