Advanced Persistent Threat Datasets for Industrial IoT : A Survey

Industrial Internet of Things (IIoT) environments are characterized by several interconnected devices, legacy systems, and critical infrastructure. Advanced Persistent Threat (APT) attacks are typically sophisticated, covert, and intended to be persistent over time. Understanding APT attacks in the context of IIoT is critical to develop robust intrusion detection and prevention systems for protecting critical infrastructure and securing sensitive data. Machine learning techniques have proven to be very effective in design of these robust defense systems. However, good datasets are essential for building effective machine learning models. In this research, a thorough examination of current datasets related to IIoT network traffic flows along with characteristics of APT attacks has been presented. The APT lifecycle in IIoT has been identified and representativeness of real world attacks in publicly available datasets has been examined.