Security and Privacy in IoT Using Machine Learning and Blockchain: Threats and Countermeasures

【Author】 Waheed, Nazar; He, Xiangjian; Ikram, Muhammad; Usman, Muhammad; Hashmi, Saad Sajid

【Source】ACM COMPUTING SURVEYS

【影响因子】14.324

【Abstract】Security and privacy of users have become significant concerns due to the involvement of the Internet of Things (IoT) devices in numerous applications. Cyber threats are growing at an explosive pace snaking the existing security and privacy measures inadequate. Hence, everyone on the Internet is a product for hackers. Consequently, Machine Learning (ML) algorithms are used to produce accurate outputs from large complex databases, where the generated outputs can be used to predict and detect vulnerabilities in IoT-based systems. Furthermore, Blockchain (BC) techniques are becoming popular in modern IoT applications to solve security and privacy issues. Several studies have been conducted on either ML algorithms or BC techniques. However, these studies target either security or privacy issues using ML algorithms or BC techniques, thus posing a need for a combined survey on efforts made in recent years addressing both security and privacy issues using ML algorithms and BC techniques. In this article, we provide a summary of research efforts made in the past few years, from 2008 to 2019, addressing security and privacy issues using ML algorithms and BC techniques in the IoT domain. First, we discuss and categorize various security and privacy threats reported in the past 12 years in the IoT domain. We then classify the literature on security and privacy efforts based on ML algorithms and BC techniques in the IoT domain. Finally, we identify and illuminate several challenges and future research directions using ML algorithms and BC techniques to address security and privacy issues in the IoT domain.

【Keywords】Blockchain; cybersecurity; Internet of Things; machine learning

【发表时间】2021 FEB

【收录时间】2022-01-02

【文献类型】综述

【主题类别】

区块链应用-实体经济-物联网