The Surfing Attacks Secured Password Authentication System

People enjoy the convenience of on-line services, but online environments may bring many risks. We propose a virtual password concept involving a small amount of human computing to secure users’ passwords in on-line environments. We adopt user determined randomized linear generation functions to secure users’ passwords based on the fact that a server has more information than any adversary does. We analyze how the proposed scheme defends against phishing, key logger, and shoulder-surfing attacks. To the best of our knowledge, our virtual password mechanism is the first one which is able to defend against all three attacks together. In this work, we discussed how to prevent users’ passwords from being stolen by adversaries. We proposed a virtual password concept involving a small amount of human computing to secure users’ passwords in on-line environments. We also implemented the system to do some tests and survey feedback indicates the feasibility of such a system. In this paper, we discuss how to prevent users’ passwords from being stolen by adversaries in online environments and automated teller machines. We propose differentiated virtual password mechanisms in which a user has the freedom to choose a virtual password scheme ranging from weak security to strong security, where a virtual password requires a small amount of human computing to secure users’ passwords. Among the schemes, we have a default method (i.e., traditional password scheme), system recommended functions, user-specified functions, user-specified programs, and so on. A function/program is used to implement the virtual password concept with a tradeoff of security for complexity requiring a small amount of human computing

The role of design and training in artifact expertise: The case of the abacus and visual attention

Previous accounts of how people develop expertise have focused on how deliberate practice transforms the cognitive and perceptual representations and processes that give rise to expertise. However, the likelihood of developing expertise with a particular tool may also depend on the degree to which that tool fits pre-existing perceptual and cognitive abilities. The present studies explored whether the abacus – a descendent of the first human computing devices – may have evolved to exploit general biases in human visual attention, or whether developing expertise with the abacus requires learning special strategies for allocating visual attention to the abacus. To address this question, we administered a series of visual search tasks to abacus experts and subjects who had little to no abacus experience, in which search targets and distractors were overlaid atop abacus “beads.” Across three studies, we found that both experts and naïve subjects were faster to detect targets in semantically-relevant components of the abacus, suggesting that abacus training is not required to exhibit attentional biases toward these components of the abacus. This finding suggests that the attentional biases that scaffold numerical processing of the abacus may emerge from general properties of visual attention that are exploited by the design of the abacus itself.

The role of design and training in artifact expertise: The case of the abacus and visual attention

Previous accounts of how people develop expertise have focused on how deliberate practice transforms the cognitive and perceptual representations and processes that give rise to expertise. However, the likelihood of developing expertise with a particular tool may also depend on the degree to which that tool fits pre-existing perceptual and cognitive abilities. The present studies explored whether the abacus – a descendent of the first human computing devices – may have evolved to exploit general biases in human visual attention, or whether developing expertise with the abacus requires learning special strategies for allocating visual attention to the abacus. To address this question, we administered a series of visual search tasks to abacus experts and subjects who had little to no abacus experience, in which search targets and distractors were overlaid atop abacus “beads.” Across three studies, we found that both experts and naïve subjects were faster to detect targets in semantically-relevant components of the abacus, suggesting that abacus training is not required to exhibit attentional biases toward these components of the abacus. This finding suggests that the attentional biases that scaffold numerical processing of the abacus may emerge from general properties of visual attention that are exploited by the design of the abacus itself.