A Comparative Study of Diverse Intrusion Detection Methods using Machine Learning Techniques

Establishing robust and efficient intrusion detection systems (IDS) and Intrusion prevention systems (IPS) are inevitable in today's security world. The major role of IDS is detecting the anomaly in network traffic using effective methods. Machine learning techniques take a vital role in intrusion detection based on anomaly detection. This paper deals with the survey on machine learning techniques used in intrusion detection. The classification algorithms of machine learning like logistic regression, naïve Bayes, KNN, Decision Tree, Random Forest, and SVM are suitable for intrusion detection. This paper analyses the behaviour and properties of the machine learning-based classification techniques which is used for intrusion detection application.