Hybrid Metaheuristics With Machine Learning Based Botnet Detection in Cloud Assisted Internet of Things Environment

Abstract

Botnet detection in a cloud-aided Internet of Things (IoT) environment is a tedious process, meanwhile, IoT gadgets are extremely vulnerable to attacks due to poor security practices and limited computing resources. In the cloud-aided IoT environment, Botnet can be identified by monitoring network traffic and analyzing it for signs of malicious activity. It can be performed by using intrusion detection systems, machine learning (ML) algorithms, and other security tools that are devised for identifying known botnet behaviors and signatures. Therefore, this study presents a Hybrid Metaheuristics with Machine Learning based Botnet Detection (HMMLB-BND) method in the Cloud Aided IoT environment. The projected HMMLB-BND technique focuses on the detection and classification of Botnet attacks in the cloud-based IoT environment. In the presented HMMLB-BND technique, modified firefly optimization (MFFO) algorithm for feature selection purposes. The HMMLB-BND algorithm uses a hybrid convolutional neural network (CNN)-quasi-recurrent neural network (QRNN) module for botnet detection. For the optimal hyperparameter tuning process, the chaotic butterfly optimization algorithm (CBOA) is employed. A series of simulations were made on the N-BaIoT dataset and the experimental outcomes stated the significance of the HMMLB-BND technique over other existing approaches.

Keywords

• Deep learning
• cloud computing
• Internet of Things
• cybersecurity
• botnet detection