AIS, Automatic Identification System, is an application of cyber-physical systems (CPS) to smart transportation at sea. Being primarily used for collision avoidance and traffic monitoring by ship captains and maritime authorities, AIS is a mandatory installation for over 300,000 vessels worldwide since 2002. Other promoted benefits are accident investigation, aids to navigation and search and rescue (SAR) operations. In this paper, we present a unique security evaluation of AIS, by introducing threats affecting both the implementation in online providers and the protocol specification. Using a novel software-based AIS transmitter that we designed, we show that our findings affect all transponders deployed globally on vessels and other maritime stations like lighthouses, buoys, AIS gateways, vessel traffic services and aircraft involved in SAR operations. Our concerns have been acknowledged by online providers and international standards organizations, and we are currently and actively working together to improve the overall security.
Abstract.Recently, social networks such as Facebook have experienced a huge surge in popularity. The amount of personal information stored on these sites calls for appropriate security precautions to protect this data.In this paper, we describe how we are able to take advantage of a common weakness, namely the fact that an attacker can query popular social networks for registered e-mail addresses on a large scale. Starting with a list of about 10.4 million email addresses, we were able to automatically identify more than 1.2 million user profiles associated with these addresses. By automatically crawling and correlating these profiles, we collect detailed personal information about each user, which we use for automated profiling (i.e., to enrich the information available from each user). Having access to such information would allow an attacker to launch sophisticated, targeted attacks, or to improve the efficiency of spam campaigns. We have contacted the most popular providers, who acknowledged the threat and are currently implementing our proposed countermeasures. Facebook and XING, in particular, have recently fixed the problem.
Abstract. Social networks are some of the largest and fastest growing online services today. Facebook, for example, has been ranked as the second most visited site on the Internet, and has been reporting growth rates as high as 3% per week. One of the key features of social networks is the support they provide for finding new friends. For example, social network sites may try to automatically identify which users know each other in order to propose friendship recommendations.Clearly, most social network sites are critical with respect to user's security and privacy due to the large amount of information available on them, as well as their very large user base. Previous research has shown that users of online social networks tend to exhibit a higher degree of trust in friend requests and messages sent by other users. Even though the problem of unsolicited messages in social networks (i.e., spam) has already been studied in detail, to date, reverse social engineering attacks in social networks have not received any attention. In a reverse social engineering attack, the attacker does not initiate contact with the victim. Rather, the victim is tricked into contacting the attacker herself. As a result, a high degree of trust is established between the victim and the attacker as the victim is the entity that established the relationship.In this paper, we present the first user study on reverse social engineering attacks in social networks. That is, we discuss and show how attackers, in practice, can abuse some of the friend-finding features that online social networks provide with the aim of launching reverse social engineering attacks. Our results demonstrate that reverse social engineering attacks are feasible and effective in practice.
In this paper we present soundsquatting, a previously unreported type of domain squatting which we uncovered during analysis of cybersquatting domains. In soundsquatting, an attacker takes advantage of homophones, i.e., words that sound alike, and registers homophoneincluding variants of popular domain names. We explain why soundsquatting is different from existing domain-squatting attacks, and describe a tool for the automatic generation of soundsquatting domains. Using our tool, we discover that attackers are already aware of the principles of soundsquatting and are monetizing them in various unethical and illegal ways. In addition, we register our own soundsquatting domains and study the population of users who reach our monitors, recording a monthly average of more than 1,700 non-bot page requests. Lastly, we show how sound-dependent users are particularly vulnerable to soundsquatting through the abuse of text-to-speech software.
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