A Novel Forecastive Anomaly Based Botnet Revelation Framework for Competing Concerns in Internet of Things

Abstract

With internet, billions and millions of devices in Internet of Things (IoT) are interconnected and are communicated with other devices through messaging bots. The messaging bots are sometimes controlled by the attackers so as to carry out several malicious activities. Thus bots become a serious cyber security hazard for the IoT devices. For this reason, it is crucial to detect the existence of malicious bots and other anomalies in the network. Thus to tackle with these bots and anomalies a Novel Forecastive Anomaly based Botnet Revelation Framework is designed in our proposed work. The approach works as a two way progression, i.e. first is the Instance Creation and the second is Cataloging. As an alternative to machine learning algorithm, in our work, an Ensemble based Stream Mining is being used to generate several instances with less memory and time. Once the instances are created, Graph Structure Based Detection of Anomaly (GSBDA) is initiated based on features derived by the stream mining algorithm to detect the presence of hazardous anomalies. In addition, the second phase utilizes a KNN (K Nearest neighbor) algorithm, a type of instance based learning algorithm. It is used to identify the Botnet accurately by observing the network flows.