Blockchain-Based Authentication and Explainable AI for Securing Consumer IoT Applications

被引:14
|
作者
Kumar, Randhir [1 ]
Javeed, Danish [2 ]
Aljuhani, Ahamed [3 ]
Jolfaei, Alireza [4 ]
Kumar, Prabhat [5 ]
Islam, A. K. M. Najmul [5 ]
机构
[1] SRM Univ AP, Dept Comp Sci & Engn, Amaravati 522240, India
[2] Northeastern Univ, Software Coll, Shenyang 110169, Peoples R China
[3] Univ Tabuk, Dept Informat Technol, Tabuk 71491, Saudi Arabia
[4] Flinders Univ S Australia, Coll Sci & Engn, Adelaide, SA 5042, Australia
[5] LUT Univ, LUT Sch Engn Sci, Dept Software Engn, Lappeenranta 53850, Finland
关键词
Internet of Things; Authentication; Blockchains; Servers; Artificial intelligence; Computer crime; Security; Blockchain; consumer applications; deep learning; explainable AI; authentication and key agreement; SMART; INTERNET; SCHEME; THINGS;
D O I
10.1109/TCE.2023.3320157
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
The consumer Internet of Things (IoT) applications in particular smart cities are mostly equipped with Internet-connected networked devices to improve city operations by giving access to a massive amount of valuable information. However, these smart devices in a smart city environment mostly use public channels to access and share data among different participants. This has introduced a great interest in using authentication and key agreement (AKA) mechanisms and intrusion detection systems (IDS) based on artificial intelligence (AI) techniques. However, most of the AKA mechanisms have high computation and communication costs and cannot be trusted completely. On the other hand, the AI-based IDS are treated as blackbox by the security analyst due to their inability to explain the reasons behind the decision. In this direction, we have integrated blockchain-based AKA mechanism with explainable artificial intelligence (XAI) for securing smart city-based consumer applications. Specifically, first, the participating entities communicate with each other in a secure manner to exchange data using a blockchain-based AKA mechanism. On the other hand, we have used SHapley Additive exPlanations (SHAP) mechanism to explain and interpret the prominent features that are most influential in the decision. The practical implementation of the proposed framework proves the efficiency over other recent state-of-the-art techniques.
引用
收藏
页码:1145 / 1154
页数:10
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