ML-based anomaly detection for IoT Devices

Abstract

Most IoT devices today are not built with security in mind. With Internet of Things devices seeing widespread adoption in areas with high economic importance, there is a need for anomaly detection taking place locally in devices and at odes. Attacks such as deauthentication attacks disruptive because it prevents sensors from receiving instructions and sending responses back. Designing an intrusion detection system that uses machine learning models is an approach that can help improve the detection of anomalies but the machine learning models have to efficient in resources, accurate, and precise.

In this project, two ensemble learning techniques are proposed for use on botnet infected data from IoT devices. The two ensemble techniques used are stacked generalization, a stacking ensemble machine learning technique and AdaBoost, a boosting ensemble machine learning technique. The models created using stacked generalization and AdaBoost made use of a training and test split of 60:40. During the training and testing of both techniques, AdaBoost model demonstrated both excellent accuracy and low latency during training and generating predictions.