PPSS: A privacy-preserving secure framework using blockchain-enabled federated deep learning for Industrial IoTs
参考中译:PPS：基于区块链的工业物联网联合深度学习隐私保护安全框架


          

刊名：Pervasive and Mobile Computing
作者：Djallel Hamouda（Labstic Laboratory, Department of Computer Science, Guelma University）
Mohamed Amine Ferrag（Technology Innovation Institute）
Nadjette Benhamida（Labstic Laboratory, Department of Computer Science, Guelma University）
Hamid Seridi（Labstic Laboratory, Department of Computer Science, Guelma University）
刊号：738LB177
ISSN：1574-1192
出版年：2023
年卷期：2023, vol.88
页码：101738-1--101738-21
总页数：21
分类号：TP3
关键词：Federated learning；Intrusion detection；Privacy preserving；Proof of learning；Security；Industrial internet of things (IIoT)
参考中译：联合学习；入侵检测；隐私保护；学习证明；安全；工业物联网(IIoT)
语种：eng
文摘：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 multiparty 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).
参考中译：工业4.0/5.0对新兴技术的日益依赖大大增加了网络威胁和数据隐私问题的范围。近年来，基于联邦学习(FL)的入侵检测系统在不暴露数据隐私问题的情况下，促进了对资源受限、异构工业系统中大规模网络攻击的检测。然而，后者的固有特性导致了联邦的可信验证和共识、模型上传的不可靠性和隐私保护等问题。为了应对这些挑战，本文提出了一种新的隐私保护安全框架PPS，该框架基于区块链使能FL的使用，提高了隐私、可验证性和透明度。PPSS框架采用允许的区块链系统来保护多方计算，并基于一种名为联合深度学习证明(PoFDL)的轻量级、节能的共识协议来激励跨竖井FL。具体地说，我们设计了两个联邦阶段来进行全局模型聚合。第一阶段使用随机梯度下降的差分私有训练(DP-SGD)来加强客户端更新的隐私保护；第二阶段使用PoFDL协议来证明并向区块链添加新的包含模型的块。我们使用一个新的网络安全数据集(Edge-IIoT数据集)从检测率、精确度、准确度、计算量和能量开销等方面研究了所提出的PPSS框架的性能。实验结果表明，在非独立同分布(Non-IID)和独立同分布(IID)两种分布模式下，PPSS框架系统能够检测到高分类性能的工业IIoT攻击。



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