An Optimized Approach to Deep Learning for Botnet Detection and Classification for Cybersecurity in Internet of Things Environment

The recent development of the Internet of Things (IoTs) resulted in the growth of IoT-based DDoS attacks. The detection of Botnet in IoT systems implements advanced cybersecurity measures to detect and reduce malevolent botnets in interconnected devices. Anomaly detection models evaluate transmission patterns, network traffic, and device behaviour to detect deviations from usual activities. Machine learning (ML) techniques detect patterns signalling botnet activity, namely sudden traffic increase, unusual command and control patterns, or irregular device behaviour. In addition, intrusion detection systems (IDSs) and signature-based techniques are applied to recognize known malware signatures related to botnets. Various ML and deep learning (DL) techniques have been developed to detect botnet attacks in IoT systems. To overcome security issues in an IoT environment, this article designs a gorilla troops optimizer with DL-enabled botnet attack detection and classification (GTODL-BADC) technique. The GTODL-BADC technique follows feature selection (FS) with optimal DL-based classification for accomplishing security in an IoT environment. For data preprocessing, the min-max data normalization approach is primarily used. The GTODL-BADC technique uses the GTO algorithm to select features and elect optimal feature subsets. Moreover, the multi-head attention-based long short-term memory (MHA-LSTM) technique was applied for botnet detection. Finally, the tree seed algorithm (TSA) was used to select the optimum hyperparameter for the MHA-LSTM method. The experimental validation of the GTODL-BADC technique can be tested on a benchmark dataset. The simulation results highlighted that the GTODL-BADC technique demonstrates promising performance in the botnet detection process.