Volume 17, Number 6

Hybrid Anomaly Detection Mechanism for IOT Networks

  Authors

Harish Kumar Saini, Monika Poriye, Kurukshetra University, India

  Abstract

The Internet of Things (IoT) is the fastest-growing collection of physical entities embedded with technologies to sense and exchange information with other connected devices over the Internet.Since IoT systems are resource-constrained and ad hoc, they are an obvious target for cyberattacks. IoT system security thus requires continual observation and analysis. The application of machine learning (ML) to IoT security holds particular promise for identifying any anomalies in the system's typical operation. In this paper, we propose to design a Random Forest-Support Vector Machine (RF-SVM) based Anomaly detection framework for IoT. The RF classifier is applied for selecting the optimal features from the extracted traffic data. It includes removing the outliers, redundant data, and choosing the best features with high weight values. Then, SVM is applied for classifying the extracted features and detecting the anomalies. The fitness function is derived in terms of true positives, false positives, and false negatives. From the detected anomalies, the attack type is then determined, and a corresponding warning is sent to the monitoring nodes.In the experimental results, it is shown that the proposed RF-SVM classifier attains increased detection accuracy with reduced detection overhead and packet drops.

  Keywords

IoT, Machine Learning, Ensemble, Anomaly detection