The document discusses the Deep Learning Threat Intelligence Framework (DLTIF) designed for cybersecurity in IoT-enabled maritime transportation systems. It details how DLTIF utilizes artificial intelligence to enhance threat detection and identification, increasing accuracy to 99% while addressing the limitations of existing cyber threat intelligence solutions. The framework comprises three key components: a deep feature extractor, cyber threat intelligence-driven detection, and attack type identification, collectively aimed at improving security in the maritime industry.

1. DLTIF: Deep Learning
Threat Intelligence Modeling and
Identification Framework in IoT
Maritime Transportation Systems
Abstract
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 Intelligen
security strategy that uses artificial intelligence models to understand cyber
attacks and can protect data of IoT
most of the existing CTI-
relevant threat information, and has low detection and high false alarm rate.
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
enabled MTS, the smart maritime objects, infrastructure associated with
ate 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,
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,
-based solutions uses manual analysis to extract
elevant threat information, and has low detection and high false alarm rate.
Driven Cyber
Threat Intelligence Modeling and
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
enabled MTS, the smart maritime objects, infrastructure associated with
ate 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,
ce (CTI) is an effective
security strategy that uses artificial intelligence models to understand cyber-
enabled MTS proficiently. Unsurprisingly,
based solutions uses manual analysis to extract
elevant threat information, and has low detection and high false alarm rate.

2. 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.