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Anomaly Based Intrusion Detection Using Machine Learning Techniques

M. Ahana Aslin, A. Meena Kowshalya


Detection of Cyberattacks/anomalies in a network to build an efficient Intrusion Detection System (IDS) is very important. A system called an intrusion detection system (IDS) monitors network traffic in order to find suspicious activity and sends out signals when it is noticed. Monitoring and data analysis are designed with the objective of finding any network or system intrusions. Machine learning methods can anticipate both known and unidentified attacks. This project implements an Intrusion Detection Tree machine learning based security model to detect anomalies in the system. Decision Tree is used for classification purpose. This in turn reduced the amount of data required for analysis there by reducing computational complexity. Experiment results using Python yielded an accuracy of 99.5%.


Intrusion detection system, machine learning, data pre-processing, classification, decision tree, anomaly attacks

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