Data Exploration in IoT and Edge Environments: A Lightweight Deep Learning Model for Botnet Attack Detection

Remote patient monitoring, improved energy efficiency, besides the automation of mundane household duties are just a few samples of Internet of Things (IoT) has changed several industries. Attacks based on botnets, including distributed denial of service (DDoS), can compromise unprotected IoT devices. The use of deep learning (DL) to enhance privacy has been spurred by the realisation that traditional machine learning models employed to identify these threats violate data privacy. On the other hand, when it comes to cyberattack detection models based on DL, the majority of them fail to take computational complexity into account, making them unsuitable for placement on resource-constrained IoT edge devices. In order to identify botnet attacks on the IoT, this study presents a DL model that is computationally simple. The study uses dimensionality reduction and feature selection to keep computational complexity low and accuracy high. To begin, the botnet dataset’s best characteristics are chosen using an optimised Deep Belief Network (DBN) classical trained with repeated stratified k-fold cross-validation. In order to increase the classification accuracy, the study used Fire Hawk Optimisation (FHO) to fine-tune adjustable parameters. In comparison to earlier efforts, the model improved upon the curve. In smart grids, smart homes, and other contexts with deployed IoT edge devices that are resource constrained, the proposed methodology works well for botnet attack detection.