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ANOMALY BASED INTRUSION DETECTION SYSTEM USING MACHINE LEARNING TECHNIQUES

Ahana Aslin M, Dr. Meena kowshalya A

Abstract


Detection of Cyberattacks / anomalies in a network to build an efficient Intrusion Detection System (IDS) is very important. An Intrusion Detection System (IDS) is a system that tracks network traffic to detect suspicious activities and issues alerts when such activities are discovered. The aim is to monitor and analyze data to detect any intrusion in the system or network. Machine learning techniques are capable of identifying known as well as unknown 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%.


Keywords


Intrusion Detection System, Machine Learning, Network Attacks

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References


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DOI: https://doi.org/10.37591/jons.v10i2.919

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