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Öğe Graphical Passwords as Browser Extension: Implementation and Usability Study(SPRINGER-VERLAG BERLIN, 2009) Bicakci, Kemal; Yuceel, Mustafa; Erdeniz, Burak; Gurbaslar, Hakan; Atalay, Nart BedinToday, most Internet applications still establish user authentication with traditional text based passwords. Designing a secure as well as a user-friendly password-based method has been on the agenda of security researchers for a long time. On one hand, there are password manager programs which facilitate generating site-specific strong passwords from a single user password to eliminate the memory burden due to multiple passwords. On the other hand, there are studies exploring the viability of graphical passwords as a more secure and user-friendly alternative. In this paper, we present GPEX, a password manager program implemented as a web browser plug-in to enable using graphical passwords to secure Internet applications without any need to change their authentication interface. Experimental results show that GPEX has security and usability advantages over other password manager plug-ins. specifically; we find that with the visual interface of GPEX, users have a more complete and accurate mental model of the system and incorrect login attempts causing security exposures can easily be avoided.Öğe Simulating Probability Learning and Probabilistic Reversal Learning Using the Attention-Gated Reinforcement Learning (AGREL) Model(IEEE, 2010) Erdeniz, Burak; Atalay, Nart BedinIn a probability learning task, participants estimate the probabilistic reward contingencies, and this task has been used extensively to study instrumental conditioning with partial reinforcement. In the probabilistic reversal learning task, the probabilistic reward contingencies are reversed between options in the middle of the experiment to measure how well people adapt to new contingency situations. In this work, we used the attention-gated reinforcement learning (AGREL) model (Roelfsema & Van Ooyen, 2005) to simulate how people learn the probabilistic relationship between stimulus-reward pairs in probability and reversal learning tasks. AGREL algorithm put forward two important aspects of a learning phenomenon together in a neural network scheme: (1) the effect of unexpected outcomes on learning and (2) the effect of top-down (selective) attention on updating weights. Contrary to its importance in the learning literature, AGREL has not yet been tested with these well known learning tasks. The results of the first simulation showed that in a binary choice probability learning experiment an AGREL model can simulate different learning strategies, such as probability matching and maximizing. Secondly, we simulated a probabilistic reversal learning experiment with the same AGREL model, and the results showed that the AGREL model dynamically adapted to new contingency situations. Furthermore, we also evaluated effects of learning rate on the model's adaption to reversal contingency by plotting the interphase dynamics. These results showed that AGREL model simulates the traditional findings observed in probability and reversal learning experiments, and it can be further developed to understand the role of dopamine in learning and it can be used in model-based fMRI research.Öğe Towards Usable Solutions to Graphical Password Hotspot Problem(IEEE, 2009) Bicakci, Kemal; Atalay, Nart Bedin; Yuceel, Mustafa; Gurbaslar, Hakan; Erdeniz, BurakClick based graphical passwords that use background images suffer from hot-spot problem. Previous graphical password schemes based on recognition of images do not have a sufficiently large password space suited for most Internet applications. In this paper, we propose two novel graphical password methods based on recognition of icons to solve the hotspot problem without decreasing the password space. The experiment we have conducted that compares the security and usability of proposed methods with earlier work (i.e. Passpoints) shows that hotspot problem can be eliminated if a small increase in password entrance and confirmation times is tolerable.