Security Privacy in IoT Using Machine Learning Blockchain: Threats Countermeasures
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Security and privacy have become significant concerns due to the involvement of the Internet of Things (IoT) devices in different applications. Cyber threats are growing at an explosive pace making 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. The generated outputs can be used to predict and detect vulnerabilities in IoT-based systems. Furthermore, Blockchain (BC) technique is becoming popular in modern IoT applications to deal with 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 paper, we have provided a summary of research efforts made in the past few years 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 in the IoT domain that were reported in the past few years. Secondly, we classify the literature on security and privacy efforts based on ML algorithms and BC techniques in the IoT domain. In the end, various challenges and future research directions using ML algorithms and BC techniques to address security and privacy issues in the IoT domain are identified and discussed.
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