IoT-CAD: A Comprehensive Digital Forensics Dataset for AI-based Cyberattack Attribution Detection Methods in IoT Environments

Tracing and identifying attack characteristics, known as Cyberattack Attribution Detection (CAD), is in its early stages. It requires utilising deep learning (DL) techniques to scan multiple devices and identify cyberattacks and their attributes effectively in IoT environments. Training and validation of these techniques require comprehensive datasets generated from heterogeneous data sources. However, there is a lack of high-quality and diverse IoT-based datasets involving cyberattack attributes. This dataset aims to collect traces from Windows and Linux operating systems, encompassing a wide range of sources, including memory information, hard drives, processes, system calls, and network traffic. It incorporates traces from many IoT devices and realistic attack scenarios to ensure its relevance and applicability to real-world situations. After collecting, processing and analysing the dataset, it is evaluated using Machine Learning (ML), Digital Forensics (DF), and Explainable AI (X-AI) techniques.