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The IoT is widely used in a number of industries and generates large amounts of data. The data are processed, computed, and stored through distributed computing for analytical purposes. This invokes serious security and privacy concerns, and presents scalability issues. This paper describes a secure P2P and group communication supportive edge computing framework for IIoT systems, a consortium blockchain, and IPFS-based immutable data storage system, and an intelligent threat detection model to protect confidential data and identify cyber-attacks. Secure communications were ensured using a hybrid security scheme that included modified ECC, PUF, and Lagrange interpolation. We utilized a modified PoV consensus algorithm to resolve latency issues due to overhead and point of failure errors during block mining. The threat intelligence model used an autoencoder to transform data into a new format which was then fed into an RNN-DL to identify cyber-attacks. The model detected normal and anomalous activity, and then identified the category of detected malicious activity. We evaluated the framework according to various metrics and compared it with ECC, PoV, and ML-based classifiers. The results showed that the proposed system demonstrated a higher efficiency and improved scalability than conventional frameworks.
Hosen et al. (Tue,) studied this question.