Security indicators in web browsers alert users to the presence of a secure connection between their computer and a web server; many studies have shown that such indicators are largely ignored by users in general. In other areas of computer security, research has shown that technical expertise can decrease user susceptibility to attacks.In this work, we examine whether computer or security expertise affects use of web browser security indicators. Our study takes place in the context of web-based single sign-on, in which a user can use credentials from a single identity provider to login to many relying websites; single sign-on is a more complex, and hence more difficult, security task for users. In our study, we used eye trackers and surveyed participants to examine the cues individuals use and those they report using, respectively.Our results show that users with security expertise are more likely to self-report looking at security indicators, and eye-tracking data shows they have longer gaze duration at security indicators than those without security expertise. However, computer expertise alone is not correlated with recorded use of security indicators. In survey questions, neither experts nor novices demonstrate a good understanding of the security consequences of web-based single sign-on.

Abstract-In this paper, we introduce a predictive Q-learning deflection routing (PQDR) algorithm for buffer-less networks. Qlearning, one of the reinforcement learning (RL) algorithms, has been considered for routing in computer networks. The RL-based algorithms have not been widely deployed in computer networks where their inherent random nature is undesired. However, their randomness is sought-after in certain cases such as deflection routing, which may be employed to ameliorate packet loss caused by contention in buffer-less networks.We compare the proposed algorithm with two existing reinforcement learning-based deflection routing algorithms. Simulation results show that the proposed algorithm decreases the burst loss probability in the case of heavy traffic load while it requires fewer deflections. The PQDR algorithm is implemented using the ns-3 network simulator.

Many aspects of data security combine technical and human factors. If a highly secure system is unusable, users will move their data to less secure but more usable systems. Problems with usability are a major contributor to many high-profile security failures today.However, usable security is not well-aligned with traditional usability for various reasons. Security is rarely the primary desired goal of the individual. In fact, security is often orthogonal and sometimes in opposition to the actual goal. Security information is about risk and threats: such communication is often unwelcome. Increasing unwelcome interaction is not a goal of usable design. Since individuals must trust their machines to implement their desired tasks, risk communication itself may undermine the value of the networked interaction. For the individual, discrete technical problems are all understood under the rubric of online security (e.g., privacy from third parties use of personally identifiable information, malware). A broader conception of both security and usability is therefore needed for usable security.The Workshop on Usable Security invites submissions on all aspects of human factors and usability in the context of security and privacy. USEC '13 aims to bring together researchers already engaged in this interdisciplinary effort with other researchers in relevant areas, so we encourage economics, HCI, AI, theoretical computer science, cryptography, psychology and business studies etc. researchers and practitioners to submit original research in this area. We particularly encourage collaborative research from authors in multiple fields.

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