The six papers in this special issue focus on availability, reliability, and security. Some of the topics covered include prevention of identity theft, biometric technology and authentication, and security considerations for RF identification. Guest editors: Ravi Sandhu, A Min Tjoa, Edgar Weippl. (more…)
Our article “Verification, Validation, and Evaluation in Information Security Risk Management” (Authors: Stefan Fenz and Andreas Ekelhart) got accepted at IEEE Security & Privacy. Check out the preprint at the IEEE Digital Library.
Abstract:
Over the last four decades, various information security risk management (ISRM) approaches have emerged. However, there is a lack of sound verification, validation, and evaluation methods for these approaches. While restrictions, such as the impossibility of measuring exact values for probabilities and follow-up costs, obviously exist, verification, validation, and evaluation of research is essential in any field, and ISRM is no exception. Individual approaches exist, but so far there is no systematic overview of the available methods. In this article we survey verification, validation and evaluation methods referenced in ISRM literature and discuss in which ISRM phases the methods should be applied. The selection of appropriate methods is demonstrated with a potential real-world example. This systematic analysis draws conclusions on the current status of ISRM verification, validation and evaluation and can serve as a reference for researchers and users of ISRM approaches who aim to establish trust in their results.
Markus Huber will work this summer on his research in Social Networking Privacy and Security at Carnegie Mellon University with Alessandro Acquisti.
Abstract. In the ongoing arms race between spammers and the multi-million dollar anti-spam industry, the number of unsolicited e-mail messages (better known as “spam”) and phishing has increased heavily in the last decade. In this paper, we show that our novel friend-in-the-middle attack on social networking sites (SNSs) can be used to harvest social data in an automated fashion. This social data can then be exploited for large-scale attacks such as context-aware spam and social-phishing. We prove the feasibility of our attack exemplarily on Facebook and identify possible consequences based on a mathematical model and simulations. Alarmingly, all major SNSs are vulnerable to our attack as they fail to secure the network layer appropriately.