Massive online social networks with hundreds of millions of active users are increasingly being used by Cyber criminals to spread malicious software (malware) to exploit vulnerabilities on the machines of users for personal gain. Twitter is particularly susceptible to such activity as, with its 140 character limit, it is common for people to include URLs in their tweets to link to more detailed information, evidence, news reports and so on. URLs are often shortened so the endpoint is not obvious before a person clicks the link. Cyber criminals can exploit this to propagate malicious URLs on Twitter, for which the endpoint is a malicious server that performs unwanted actions on the person's machine. This is known as a drive-by-download. In this paper we develop a machine classification system to distinguish between malicious and benign URLs within seconds of the URL being clicked (i.e. 'real-time'). We train the classifier using machine activity logs created while interacting with URLs extracted from Twitter data collected during a large global event-the Superbowl-and test it using data from another large sporting event-the Cricket World Cup. The results show that machine activity logs produce precision performances of up to 0.975 on training data from the first event and 0.747 on a test data from a second event. Furthermore, we examine the properties of the learned model to explain the relationship between machine activity and malicious software behaviour, and build a learning curve for the classifier to illustrate that very small samples of training data can be used with only a small detriment to performance.

The adoption of the global positioning system (GPS) within the marine industry has revolutionized the marine operations by condensing the navigation of a vessel into an integrated bridge system (IBS). An IBS acts as the main command and control of a vessel as it interconnects various digital devices used for navigation in open seas and is also connected to other on-board systems of a vessel e.g., navigation and control, propulsion and machinery management system, cargo management system and safety management system, core infra structure systems, administrative and crew welfare systems, etc. Additionally, it also provides a gateway to the Internet, thus, leaving not only an IBS vulnerable but also all the on-board systems vulnerable to cyber-attacks. We, in this study, have collected historical evidences about various vulnerable digital components in an IBS to better understand the security and privacy challenges associated with the vulnerable IBS components. Our study is the first of its kind that involves collection and review of 59 historical accidents reported in literature and has highlighted various vulnerability patterns, their causes and consequences, with geographical as well as temporal relationships for different vulnerable IBS components. The vulnerabilities of IBS components were reportedly exploited using various cyber-attack techniques e.g., jamming, spoofing, hijacking, etc. This review paper also forms a baseline for future work on vulnerabilities of IBS and maritime cyber security.

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In this paper we describe how our previously proposed role model agent mechanism for norm emergence can be applied to artificial agent societies with network topologies that are changing dynamically. Dynamically changing network topologies account for agents joining and leaving the network and the links that are created and removed between agents in a society. In order to construct a dynamically changing network we have adopted a model representing agents as particles colliding in a social space. We demonstrate that the role model agent mechanism for norm emergence works on top of dynamically created network topologies that represent social relationship structures.

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