An Edge-Based Blockchain-Enabled Framework for Preventing Insider Attacks in Internet of Things (IoT)

Abstract

The IoT offers enormous potentials thanks to its Widespread adoption by many industries, individuals, and governments, leading explosive growth and remarkable breakthroughs that have made it a technology with seemingly boundless applications. However, the far-reaching IoT applications cum its characteristic heterogeneity and ubiquity come with a huge price for more security vulnerabilities, making the deployed IoT systems increasingly susceptible to, and prime targets of many different physical and cyber-attacks including insider attacks, thereby growing the overall security risks to the systems. This research, which focuses on addressing insider attacks on IoT, studies the likelihood of malicious insiders' activities compromising some of the security triad of Confidentiality, Integrity and Availability (CIA) of a supposedly secure IoT system with implemented security mechanisms. To further establish the vulnerability of the IoT systems to the insider attack being investigated in our research, we first produced a research output that emphasized the need for multi-layer security of the overall system and proposed the implementation of security mechanisms on components at all layers of the IoT system to safeguard the system and ensure its CIA. Those conventional measures however do not safeguard against insider attacks, as found by our experimental investigation of a working IoT system prototype. The outcome of the investigation therefore necessitates our proposed solution to the problem, which leverages the integration of distributed edge computing with decentralized Ethereum blockchain technology to provide countermeasures that preserve the Integrity of the IoT system data and improve effectiveness of the system. We employed the power of Ethereum smart contracts to perform integrity checks on the system data logically and take risk management decisions. We considered the industry use case of Downstream Petroleum sector for application of our solution. The solution was evaluated using datasets from different experimental settings and showed up to 86% accuracy rate.