Maritime port infrastructures rely on the use of information systems for collaboration, while a vital part of collaborating is to provide protection to these systems. Attack graph analysis and risk assessment provide information that can be used to protect the assets of a network from cyber-attacks. Furthermore, attack graphs provide functionality that can be used to identify vulnerabilities in a network and how these can be exploited by potential attackers. Existing attack graph generation methods are inadequate in satisfying certain requirements necessary in a dynamic supply chain risk management environment, since they do not consider variables that assist in exploring specific network parts that satisfy certain criteria, such as the entry and target points, the propagation length and the location and capability of the potential attacker. In this paper, we present a cyber-attack path discovery method that is used as a component of a maritime risk management system. The method uses constraints and Depth-first search to effectively generate attack graphs that the administrator is interested in. To support our method and to show its effectiveness we have evaluated it using real data from a maritime supply chain

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.

Lumpfish (Cyclopterus lumpusL.) are increasingly being used as cleaner fish to control parasitic sea lice, one of the most important threats to salmon farming. However, lumpfish cannot survive feeding solely on sea lice, and their mortality in salmon net‐pens can be high, which has welfare, ethical and economic implications. The industry is under increasing pressure to improve the welfare of lumpfish, but little guidance exists on how this can be achieved. We undertook a knowledge gap and prioritisation exercise using a Delphi approach with participants from the fish farming sector, animal welfare, academia and regulators to assess consensus on the main challenges and potential solutions for improving lumpfish welfare. Consensus among participants on the utility of 5 behavioural and 12 physical welfare indicators was high (87–89%), reliable (Cronbach's alpha = 0.79, 95CI = 0.69–0.92) and independent of participant background. Participants highlighted fin erosion and body damage as the most useful and practical operational welfare indicators, and blood parameters and behavioural indicators as the least practical. Species profiling revealed profound differences between Atlantic salmon and lumpfish in relation to behaviour, habitat preferences, nutritional needs and response to stress, suggesting that applying a common set of welfare standards to both species cohabiting in salmon net‐pens may not work well for lumpfish. Our study offers 16 practical solutions for improving the welfare of lumpfish and illustrates the merits of the Delphi approach for achieving consensus among stakeholders on welfare needs, targeting research where is most needed and generating workable solutions.