DLTIF: Deep Learning-Driven Cyber Threat Intelligence Modeling and Identification Framework in IoT-Enabled Maritime Transportation Systems

The recent burgeoning of Internet of Things (IoT) technologies in the maritime industry is successfully digitalizing Maritime Transportation Systems (MTS). In IoT-enabled MTS, the smart maritime objects, infrastructure associated with ship or port communicate wirelessly using an open channel Internet. The intercommunication and incorporation of heterogeneous technologies in IoT-enabled MTS brings opportunities not only for the industries that embrace it, but also for cyber-criminals. Cyber Threat Intelligence (CTI) is an effective security strategy that uses artificial intelligence models to understand cyber-attacks and can protect data of IoT-enabled MTS proficiently. Unsurprisingly, most of the existing CTI-based solutions uses manual analysis to extract relevant threat information, and has low detection and high false alarm rate. Therefore, to tackle aforementioned challenges, an automated framework called DLTIF is developed for modeling cyber threat intelligence and identifying threat types. The proposed DLTIF is based on three schemes: a deep feature extractor (DFE), CTI-driven detection (CTIDD) and CTI-attack type identification (CTIATI). The DFE scheme automatically extracts the hidden patterns of IoT-enabled MTS network and its output is used by CTIDD scheme for threat detection. The CTIATI scheme is designed to identify the exact threat types and to assist security analysts in giving early warning and adopt defensive strategies. The proposed framework has obtained upto 99% accuracy, and outperforms some traditional and recent state-of-the-art approaches.