ANOMALY DETECTION IN IOT NETWORKS USING SEMISUPERVISED ML TECHNIQUES

International Journal of Computer Science (IJCS) Published by SK Research Group of Companies (SKRGC)

Abstract

The rapid proliferation of Internet of Things (IoT) networks has led to unprecedented growth in connected devices, enabling smarter environments but also exposing networks to a myriad of security threats. Anomaly detection is a critical aspect of securing IoT networks, as traditional security measures often fail to adapt to the dynamic and heterogeneous nature of these systems. This paper investigates the application of semi-supervised machine learning (ML) techniques for detecting anomalies in IoT networks. Semi-supervised approaches leverage both labeled and unlabeled data, addressing the challenge of limited labeled datasets, which is common in IoT scenarios. We explore various semi-supervised algorithms, including Autoencoders, one-class SVMs, and graph-based models, to detect deviations from normal network behavior. The proposed methodology involves feature extraction from IoT traffic data, preprocessing for noise reduction, and the application of semi-supervised models trained on mixed data. Performance evaluation metrics, such as precision, recall, F1-score, and area under the receiver operating characteristic (ROC) curve, are employed to assess the effectiveness of the models. The results demonstrate that semi-supervised techniques can achieve high detection rates while minimizing false positives, even in the presence of diverse IoT protocols and device behaviors. The study further discusses the scalability of these models to handle large-scale IoT networks and their adaptability to evolving attack patterns. Our findings highlight the potential of semi-supervised ML as a robust solution for proactive anomaly detection in IoT environments, paving the way for more secure and resilient IoT deployments.

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Keywords

Semi-Supervised Learning, Anomaly Detection, IoT Security, Real-Time Monitoring, Scalability.