INTRODUCTION

Internet of Things (IoT) technology has been utilized in many areas. However, IoT devices are prone to various physical, network and application layer attacks which may lead to business interruptions, privacy violations or even physical injuries. So, it is very important in current to have an effective Machine Learning model which can be utilized to detect any intrusions. Moreover, these ML models need to be explainable so that admins can take action. Here the proposed research demonstrates that classification algorithms and low-dimensional features can effectively distinguish normal IoT device traffic from different attack traffic. This project envisions the generic explainable model which can be deployed to all IoT networks with whose help attacks can be detected and prevented.