Digital Forensics in IoT-Enabled Heterogeneous and Intelligent Network Environments
The excessive proliferation of Internet of Things (IoT) ecosystems, which are characterized by the high number of devices, transient data generation, and constantly changing cyber threats have presented serious challenges to digital forensic investigations. To solve these problems, Adaptive Forensic Intelligence Model (AFIM) is suggested as a combined model. AFIM is a multi-modal evidence-gathering mechanism, a blockchain-based secure evidence management mechanism, and an AI-based forensic analytics engine. The model has been experimentally tested on a hybrid dataset of real data of the IoT and simulated cyberattack scenarios. The federated learning framework enables models to be developed in a decentralized manner as well as using adaptive thresholding for anomaly detection “on the fly”, thereby creating scalable and resilient systems to operate in distributed environments.