Machine Learning Based Intrusion Detection System using Supervised and Unsupervised Learning
DOI:
https://doi.org/10.15662/IJEETR.2026.0802004Keywords:
Intrusion Detection System, Machine Learning, Supervised Learning, Unsupervised Learning, Anomaly Detection, CybersecurityAbstract
An Intrusion Detection System (IDS) can monitor and sift through data traffic to identify and protect the network from unauthorized or dangerous activities. In this paper, we propose a hybrid approach to Intrusion Detection Systems that utilizes both supervised and unsupervised machine learning models. Supervised learning models use labelled data to train the models to classify traffic, while unsupervised learning models identify anomalies and perform outlier detection without any labelled data. Examples of supervised learning models include Decision Tree, Random Forest, Support Vector Machine, and K-Nearest Neighbours. Examples of unsupervised learning models include K-Means Clustering, Autoencoders, and DBSCAN. Our work use the cleansed and pre-processed NSL-KDD and UNSW-NB15 to develop and evaluate machine learning models to achieve the greatest data accuracy. Our approach hybrid Intrusion Detection System improves the detection of both known and unknown intrusions, decreases the false positive and false negative rates and increases the level of protection offered by IoT and corporate networks. Yes, intrusive detection and preventative systems can learn and continue to evolve over time as new data enters the system, deepening the data accuracy as time goes by. This enhanced system calibres will guarantee a proactive data protection and threat counteraction system to net and wireless net environments.
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