IoTForge Pro: A Security Testbed for Generating Intrusion Dataset for Industrial IoT
Pradeep Kumar, Suvrajit Mullick, Rajdeep Das, Ayushman Nandi, Indrajit Banerjee
Abstract
The necessity for strong security measures to fend off cyberattacks has increased due to the growing use of industrial Internet of Things (IIoT) technologies. This research introduces IoTForge Pro, a comprehensive security testbed designed to generate a diverse and extensive intrusion dataset for IIoT environments. The testbed simulates various IIoT scenarios, incorporating network topologies and communication protocols to create realistic attack vectors and normal traffic patterns. The generated dataset, named ForgeIIOT, includes various attack types, such as denial-of-service, man-in-the-middle, ransomware, wildcard abuse, and malware-based intrusions, providing a valuable resource for developing and evaluating intrusion detection systems (IDSs). Additionally, we apply advanced machine learning techniques to analyze the ForgeIIOT dataset, demonstrating the effectiveness of different models in identifying and classifying various types of cyberattacks. Our experimental results highlight the potential of machine learning algorithms in enhancing the security of IIoT systems by accurately detecting anomalies and malicious activities. This research contributes to the field by offering a rich dataset and a robust framework for testing and improving IDS for IIoT, ultimately aiming to strengthen the cybersecurity posture of industrial networks.