Securing Industrial Internet of Things Against Botnet Attacks Using Hybrid Deep Learning Approach
Tooba Hasan, Jahanzaib Malik, Iram Bibi, Wali Ullah Khan, Fahd N. Al‐Wesabi, Kapal Dev, Gaojian Huang
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.