Deep learning enabled intrusion detection system for Industrial IOT environment

The prevalence of security vulnerabilities in Internet of Things (IoT) applications poses a serious threat to enterprise systems, necessitating sophisticated and reliable defense solutions to counter emerging and evolving threats. For the Industrial Internet of Things (IIoT), stakeholders require trustworthy and sustainable systems that can prevent the loss of human life during critical operations. The impact of multi-variant persistent and sophisticated bot attacks on connected IIoTs is potentially catastrophic, and their detection presents a highly complex and critical challenge. Therefore, there is a pressing need for efficient and timely detection of IIoT botnet attacks. This research paper proposes a robust deep learning model named AttackNet for the detection and classification of different botnet attacks in IIoT based on adaptive based CNN-GRU model. The model is extensively evaluated using the latest dataset and standard performance evaluation metrics, demonstrating its capacity to protect IIoT networks against sophisticated cyber-attacks with a testing accuracy of 99.75%, a loss of 0.0063, precision and recall score of 99.75% and 99.74% respectively. Our proposed model demonstrates superior accuracy, particularly within the N_BaIoT dataset. It achieves an outstanding accuracy of 99.75% across ten classes, surpassing state-of-the-art techniques by a substantial margin ranging from 3.2% to 16.07%. Moreover, the proposed model outperforms state-of-the-art anomaly detection systems in IIoT based on a real-time IoT device dataset in terms of detecting and classifying botnet attacks accurately.