A Collaborative DNN-Based Low-Latency IDPS for Mission-Critical Smart Factory Networks

Industrial Control Systems (ICSs) have entered an era of modernization enabled by the recent progress in Information Technologies (IT), particularly the Industrial Internet of Things (IIoT). This enables better automation of industrial processes but now exposes the ICSs to cyber-attacks that exploit the IIoT vulnerabilities. Thus, to ensure ICSs security, numerous research works have focused on designing Intrusion Detection and Prevention Systems (IDPSs), and deep learning has recently received considerable attention, as it has the potential to improve detection accuracy. However, most of the proposed deep learning solutions focus only on the model's accuracy without considering latency, which is an essential requirement in many ICSs. The novelty of this paper is the time complexity analysis of Deep Neural Networks (DNNs) and the design of a low latency and robust deep learning-based collaborative IDPS. The proposed architecture employs two classification models. In the first model, a lightweight DNN is used to perform a binary classification, i.e., normal or attack, which ensures rapid intrusion detection. A second model ensures the identification of the type of attacks by performing a multi-class classification of the detected anomaly, which is handled by a robust and complex DNN in order to achieve higher accuracy. This research also proposes intrusion response measures to deal with detected attacks, first after the anomaly detection, and then after the identification of the attack type. An experimental evaluation has been provided using various detection features, datasets, DNN algorithms, and the results demonstrate the effectiveness of the proposed solution.