Security and Privacy in IoT Using Machine Learning and Blockchain

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 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, 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.

Designing ECG monitoring healthcare system with federated transfer learning and explainable AI

“…As discussed earlier that it may cause privacy concerns among users and data owners. Thereafter, traditional centralized healthcare applications find limited applicability due to privacy concerns [39,40,41]. To address the privacy issues in machine learning, researchers have been working on Federated learning (FL) and Transfer learning (TF).…”

A Multicriteria Decision Making Taxonomy of IOT Security Challenging Factors

“…Granjal et al [16] have identified and analyzed the existing security threats of various protocol designed for IoT. Whereas, several other studies likewise [17][18][19][20] have addressed and evaluated the key management and cryptographic algorithms that is suitable for IoT paradigm. Sicari et al [21] have identified researchers' effort in order to address the confidentiality, privacy, access control and security with middleware for IoT systems.…”

IoT-based Access Management Supported by AI and Blockchains

“…Narrowing down the scope to BC and IoT systems, one sees research attempts to close the gaps of IoT systems by removing the centralized control as well as attack the problem of provenance, non-repudiation, and authenticity in IoT data streams with the help of BC [9], [18], [22], [23]. Other studies [31] attempt to close security and privacy IoT gaps with the help of the BC to ensure the reliability and availability of the data.…”