Securing Industrial Internet of Things Against Botnet Attacks Using Hybrid Deep Learning Approach

Abstract

Industrial Internet of Things (IIoT) formation of a richer ecosystem of intelligent, interconnected devices while enabling new levels of digital innovation has transformed and revolutionized global manufacturing and industry 4.0. Conversely, the general distributed nature of IIoT, Industrial 5 G, underlying IoT sensing devices, IT/OT convergence, Edge Computing, and Time Sensitive Networking makes it an impressive and potential target for cyber-attackers. Multi-variant persistent and sophisticated bot attacks are considered catastrophic for connected IIoTs. Besides, botnet attack detection is highly complex and decisive. Thus, efficient and timely detection of IIoT botnets is a dire need of the day. We propose a hybrid intelligent Deep Learning (DL) mechanism to secure IIoT infrastructure from lethal and sophisticated multi-variant botnet attacks. The proposed mechanism has been rigorously evaluated with the latest dataset, standard and extended performance evaluation metrics, and current DL benchmark algorithms. Besides, cross-validation of our results is also performed to show overall performance clearly. The proposed mechanisms outperform accurately identifying multi-variant sophisticated bot attacks by achieving a 99.94% detection rate. Besides, our proposed technique attains 0.066(ms) time, which also shows promising results in terms of speed efficiency.