Joining Federated Learning to Blockchain for Digital Forensics in IoT

Abstract

In present times, the Internet of Things (IoT) is becoming the new era in technology by including smart devices in every aspect of our lives. Smart devices in IoT environments are increasing and storing large amounts of sensitive data, which attracts a lot of cybersecurity threats. With these attacks, digital forensics is needed to conduct investigations to identify when and where the attacks happened and acquire information to identify the persons responsible for the attacks. However, digital forensics in an IoT environment is a challenging area of research due to the multiple locations that contain data, traceability of the collected evidence, ensuring integrity, difficulty accessing data from multiple sources, and transparency in the process of collecting evidence. For this reason, we proposed combining two promising technologies to provide a sufficient solution. We used federated learning to train models locally based on data stored on the IoT devices using a dataset designed to represent attacks on the IoT environment. Afterward, we performed aggregation via blockchain by collecting the parameters from the IoT gateway to make the blockchain lightweight. The results of our framework are promising in terms of consumed gas in the blockchain and an accuracy of over 98% using MLP in the federated learning phase.

An Efficient Attribute-Based Participant Selecting Scheme with Blockchain for Federated Learning in Smart Cities

“…In many applications, blockchain is considered to be the best solution to achieve an incentive mechanism, because it is immutable and auditable and has inherent consensus [27]. Almutairi et al [28] proposed a solution integrating federated learning with a lightweight blockchain, enhancing the performance and reducing the gas consumption while maintaining security against data leaks. Weng et al [29] proposed a value-driven incentive mechanism based on blockchain to force participants to behave correctly.…”