Isolating botnet attacks using Bootstrap Aggregating Surflex-PSIM Classifier in IoT

Abstract

In an Internet of Things (IoT) environment, any object, which is equipped with sensor node and other electronic devices can involve in the communication over wireless network. Hence, this environment is highly vulnerable to botnet attack. Nevertheless, the challenge prevailed in detection of botnet attack due to its unique structurally repetitive nature, performing dissimilar activities that are non-linear, and an invisible nature by deleting the history. Even though existing mechanisms have taken action against the botnet attack proactively, it failed to capture the frequent abnormal activities of botnet attackers due to frequent monitoring. Moreover, when the number of devices in the IoT environment has increased, existing mechanisms has missed more number of botnets due to functional complexity. Therefore, to overwhelm the issues in detecting the botnet attack, our work has proposed a Bootstrap Aggregating Surflex-PSIM Classifier. It gathers data from several sensor nodes, which are then preprocessed using Linear Random Euler complex-valued Filter (LRECF). Accordingly, the linearized data is subjected to the training phase comprising of Random Poison Forest (RPF) to predict accurately the botnet creating Distributed Denial of Service (DDoS) and Spam attacks within less time. After being trained, similar botnets are clustered using surflex-PSIM that isolates the botnet attacked clusters based on automatic trained characteristics pocket value. Thus, with the aid our proposed classifier, botnet is detected and isolated with high accuracy at reduced time, thereby ensures system reliability with enhanced system performance.