Deep reinforcement learning for next-generation IoT networks.

The growing reliance of industry 4.0/5.0 on emergent technologies has dramatically increased the scope of cyber threats and data privacy issues. Recently, federated learning (FL) based intrusion detection systems (IDS) promote the detection of large-scale cyber-attacks in resource-constrained and heterogeneous industrial systems without exposing data to privacy issues. However, the inherent characteristics of the latter have led to problems such as a trusted validation and consensus of the federation, unreliability, and privacy protection of model upload. To address these challenges, this paper proposes a novel privacy-preserving secure framework, named PPSS, based on the use of blockchain-enabled FL with improved privacy, verifiability, and transparency. The PPSS framework adopts the permissioned-blockchain system to secure multi-party computation as well as to incentivize cross-silo FL based on a lightweight and energy-efficient consensus protocol named Proof-of-Federated Deep-Learning (PoFDL). Specifically, we design two federated stages for global model aggregation. The first stage uses differentially private training of Stochastic Gradient Descent (DP-SGD) to enforce privacy protection of client updates, while the second stage uses PoFDL protocol to prove and add new model-containing blocks to the blockchain. We study the performance of the proposed PPSS framework using a new cyber security dataset (Edge-IIoT dataset) in terms of detection rate, precision, accuracy, computation, and energy cost. The results demonstrate that the PPSS framework system can detect industrial IIoT attacks with high classification performance under two distribution modes, namely, non-independent and identically distributed (Non-IID) and independent and identically distributed (IID).