Spring of four seasons and the spring of our lives.

User's behavioral biometrics are promising as authentication factors in particular if accuracy is sufficiently guaranteed. They can be used to augment security in combination with other authentication factors. A gesture-based pattern lock system is a good example of such multi-factor authentication, using touch dynamics in a smartphone. However, touch dynamics can be significantly affected by a shape of gestures with regard to the performance and accuracy, and our concern is that user-chosen patterns are likely far from producing such a good shape of gestures. In this poster, we raise this problem and show our experimental study conducted in this regard. We investigate if there is a reproducible correlation between shape and accuracy and if we can derive effective attribute values for user guidance, based on the gesture-based pattern lock system. In more general, we discuss a guided approach to behavioral authentication.

When a user interacts with a computing system to enter a secret password, shoulder surfing attacks are of great concern. To cope with this problem, previous methods presumed limited cognitive capabilities of a human adversary as a deterrent, but there was a pitfall with the assumption. In this paper, we show that human adversaries, even without a recording device, can be more effective at eavesdropping than expected, in particular by employing cognitive strategies and by training themselves. Our novel approach called covert attentional shoulder surfing indeed can break the well known PIN entry method previously evaluated to be secure against shoulder surfing. Another contribution in this paper is the formal modeling approach by adapting the predictive human performance modeling tool for security analysis and improvement. We also devise a defense technique in the modeling paradigm to deteriorate severely the perceptual performance of the adversaries while preserving that of the user. To the best of our knowledge, this is the first work to model and defend the new form of attack through human performance modeling. Real attack experiments and user studies are also conducted.

Wireless industrial sensor networks are necessary for industrial applications, so that wireless sensor nodes sense around themselves and detect anomaly events in the harsh industrial environments. Due to the harshness, anomaly events such as adversarial intrusions may result in harmful and disastrous situations for industrial applications but it is difficult to detect them over wireless medium. Intrusion detection is an essential requirement for security, but as far as we know, there have not been such studies for wireless industrial sensor networks in the literature. The previous intrusion detection methods proposed for wireless sensor networks consider networks rather in general senses and restrict capabilities to specific attacks only. In this paper, we first study intrusion detection for wireless industrial sensor networks, through various experiments and design of a hierarchical framework. We classify and select better methodologies against various intrusions. Subsequently, we find novel results on the previous methodologies. We also propose a new hierarchical framework for intrusion detection as well as data processing. Throughout the experiments on the proposed framework, we stress the significance of one-hop clustering, which was neglected in the previous studies. Finally, we construct required logical protocols in the hierarchical framework; hierarchical intrusion detection and prevention protocols.