P2TIF: A Blockchain and Deep Learning Framework for Privacy-Preserved Threat Intelligence in Industrial IoT

Abstract

The industrial Internet of Things (IIoT) is a fast-growing network of Internet-connected sensing and actuating devices aimed to enhance manufacturing and industrial operations. This interconnection generates a high volume of data over the IIoT network and raises serious security (e.g., the rapid evolution of hacking techniques), privacy (e.g., adversaries performing data poisoning and inference attacks), and scalability issues. To mitigate the aforementioned challenges, this article presents, a new privacy-preserved threat intelligence framework (P2TIF) to protect confidential information and to identify cyber-threats in IIoT environments. There are two major elements in the proposed P2TIF framework. First, a scalable blockchain module that enables secure communication of IIoT data and prevents data poisoning attacks. Second, a deep learning module that transforms actual data into a new format and protects data from inference attacks using a deep variational autoencoder (DVAE) technique. The encoded data are then employed by a threat detection system using attention-based deep gated recurrent neural network (A-DGRNN) to recognize malicious patterns in IIoT environments. The proposed framework is validated using two different network data sources, i.e., ToN-IoT and IoT-Botnet. Security analysis and experimental results revealed the high efficiency and scalability of the proposed P2TIF framework.