Modern information society depends on reliable functionality of information systems infrastructure, while at the same time the number of cyber-attacks has been increasing over the years and damages have been caused. Furthermore, graphs can be used to show paths than can be exploited by attackers to intrude into systems and gain unauthorized access through vulnerability exploitation. This paper presents a method that builds attack graphs using data supplied from the maritime supply chain infrastructure. The method delivers all possible paths that can be exploited to gain access. Then, a recommendation system is utilized to make predictions about future attack steps within the network. We show that recommender systems can be used in cyber defense by predicting attacks. The goal of this paper is to identify attack paths and show how a recommendation method can be used to classify future cyber-attacks in terms of risk management. The proposed method has been experimentally evaluated and validated, with the results showing that it is both practical and effective.

The growing complexity and the heterogeneity of critical infrastructures (CIs) in multicultural maritime and logistics networks challenge existing methods and tools to dynamically respond to the frequent change of information and to the lack of efficiently sharing security knowledge over the supply chain. This fosters a semantic gap, which causes disintegration in the supply-chain workflow and attracts cyber-attackers attention. This paper proposes a knowledge management methodology and an associated tool for the maritime logistics and supply chain (MLoSC), which aims to enable the sharing of supply chain knowledge and suggests ways for identifying cyber threats over CIs. The methodology is illustrated via an indicative service (the vehicle transport service), examined in the context of three prominent maritime use cases. The proposed methodology is used to develop a knowledge base for the MLoSC using semantic web technologies.

Modern port infrastructures have become highly dependent on the operation of complex, dynamic ICT-based maritime supply chains. This makes them open and vulnerable to the rapidly changing ICT threat landscape and many ports are not yet fully prepared for that. Furthermore, these supply chains represent highly interrelated cyber ecosystem, in which a plethora of distributed ICT systems of various business partners interact with each other. Due to these interrelations, isolated threats and vulnerabilities within a system of a single business partner may propagate and have cascading effects on multiple other systems, thus resulting in a large-scale impact on the whole supply chain. In this context, this article proposes a novel evidence-driven risk assessment methodology, i.e., the MITIGATE methodology, to analyze the risk level of the whole maritime supply chain. This methodology builds upon publicly available information, well-defined mathematical approaches and best practices to automatically identify and assess vulnerabilities and potential threats of the involved cyber assets. As a major benefit, the methodology provides a constantly updated risk evaluation not only of all cyber assets within each business partner in the supply chain but also of the cyber interconnections among those business partners. Additionally, the whole process is based on qualitative risk scales, which makes the assessment as well as the results more intuitive. The main goal of the MITIGATE methodology is to support the port authorities as well as the risk officers of all involved business partners.

In recent years maritime logistics infrastructures are the global links among societies and economies. This challenges adversaries to intrude on the cyber-dependent ICTs by performing high-level intelligent techniques. A potential cyber-attack on such infrastructures can cause tremendous damages starting from supply chain service disruption ending up with threatening the whole human welfare. Current risk management policies embed significant limitations in terms of capturing the specific security requirements of ICTs and control/monitoring devices, such as IoT platforms, satellites and time installations, which are primary functioning for the provision of Maritime Logistics and Supply Chain (MLoSC) services. This work presents a novel risk assessment methodology capable of addressing the security particularities and specificities of the complex nature of SCADA infrastructures and Cyber-Physical Systems (CPSs) of the Maritime Logistics Industry. The methodology identifies asset vulnerabilities and threats to estimate the cyber-risks and their cascading effects within the supply chain, introducing a set of subsequent security assessment services. The utilization of these services is demonstrated via a critical, real-life SCADA scenario indicating how they can facilitate supply chain operators in comprehending the threat landscape of their infrastructures and guide them how to adopt optimal mitigation strategies to counter or eliminate their cyber-risks.

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Cyber Supply Chain (CSC) security requires a secure integrated network among the sub-systems of the inbound and outbound chains. Adversaries are deploying various penetration and manipulation attacks on an CSC integrated network's node. The different levels of integrations and inherent system complexities pose potential vulnerabilities and attacks that may cascade to other parts of the supply chain system. Thus, it has become imperative to implement systematic threats analyses and predication within the CSC domain to improve the overall security posture. This paper presents a unique approach that advances the current state of the art on CSC threat analysis and prediction by combining work from three areas: Cyber Threat Intelligence (CTI ), Ontologies, and Machine Learning (ML). The outcome of our work shows that the conceptualization of cybersecurity using ontological theory provides clear mechanisms for understanding the correlation between the CSC security domain and enables the mapping of the ML prediction with 80% accuracy of potential cyberattacks and possible countermeasures.

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