Stacked Ensemble Learning Based Approach for Anomaly Detection in IoT Environment

Abstract

Machine learning based anomaly detection systems have gained prominence in field of Internet of Things (IoT) due to their effectiveness in dealing with security and privacy issues. Internet of things has manifold applications possible with aid of effective integration of sensors, databases, machines and services at work. Due to increasing use of IoT-based architectures, attacks and anomaly detection has become a crucial part for functioning of IoT. The basic goal of an anomaly detection system is to verify whether the behavior of the system is normal or unfaithful actions are being taken by system. Anomaly detection systems are used for detection of attacks and anomalies right from denial-of-service to malicious operations that may cause disruption to IoT-based systems. A variety of machine learning algorithms have been used for the purpose of anomaly and attack detection. In this paper, we analyzed different machine learning algorithms and compared it against our proposed Stacked ensemble learning model. Evaluation metrics used for comparison of various machine learning algorithms against our proposed stacked ensemble learning model include F1 score, precision, accuracy, recall, and area under ROC curve. The proposed system is found to have an accuracy of 99.8% that is superior in comparison to most traditional machine learning algorithms. The proposed stacked ensemble learning model could be effectively used for improving the existing anomaly detection system.