Chapters authored
AI-Powered Security for IoT: A Blockchain Enabled Device Twin Approach
Application domains in embedded systems such as Industrial Internet of Things (IIoTs) involve smart, mobile, and interconnected devices that operate in large numbers (devices swarms). These devices process and exchange safety, privacy, and mission-critical information. Thus, message exchanges, task collaborations, and service deliveries necessitate the communicating devices to trust each other. In this regard, it is essential to have a suitable device verification technique that scales to device swarms and establishes trust among collaborating devices. However, state-of-the-art device swarm attestation schemes assume a single external verifier and do not offer resiliency. In addition, in a swarm of self-organizing IoT networks, each member device independently changes its position (i.e., continuously entering and leaving the network). Thus, it becomes very challenging for the trusted external verifier to track these mobile devices, which further exacerbates the problem of authentication, identification, and management of swarm members. We present a novel AI-powered self-healing decentralized attestation that distributes attestation among devices for systems that work in swarms. Decentralization decreases delay and overcomes the problem of a single point of failure. To ensure swarm security, interoperability, and management, we use a reusable digital identity for each physical system (IoT node), allowing authentication and authorization of access. Each device is leveraged with an ML model, where verifications are carried out on its device twin, that is, the digital representations of the attestable properties of the member device. After each attestation, our system quickly extracts information about swarm members and establishes a chained relationship (chains of trusted blocks) with one another. This chain comprises devices with benign software configurations. We evaluate performance and demonstrate if the execution overhead is negligible. We also analyze security and show that the proposed technique is very effective and robust against various attacks.
Part of the book: Online Identity - An Essential Guide