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We introduce DigitalSeal, a transaction authentication tool that works in both online and offline use scenarios. DigitalSeal is a digital scanner that reads transaction information sent by an issuing entity of the DigitalSeal reader for authentication, and the information is encoded using a specially crafted bar-code. DigitalSeal views various pieces of transaction information for users to verify and proceed with transaction authentication. DigitalSeal is generic, and is capable of reading information viewed on paper, computer monitors (similarly, kiosk monitors), and mobile phones.

Cyberbullying has emerged as a serious societal and public health problem that demands accurate methods for the detection of cyberbullying instances in an effort to mitigate the consequences. While techniques to automatically detect cyberbullying incidents have been developed, the scalability and timeliness of existing cyberbullying detection approaches have largely been ignored. We address this gap by formulating cyberbullying detection as a sequential hypothesis testing problem. Based on this formulation, we propose a novel

Face and object recognition in uncontrolled scenarios due to pose and illumination variations, low resolution, etc. is a challenging research area. Here we propose a novel descriptor, Aligned Discriminative Pose Robust ( ADPR) descriptor, for matching faces and objects across pose which is also robust to resolution and illumination variations. We generate virtual intermediate pose subspaces from training examples at a few poses and compute the alignment matrices of those subspaces with the frontal subspace.

In this paper, we present Discriminant Correlation Analysis (DCA), a feature level fusion technique that incorporates the class associations in correlation analysis of the feature sets. DCA performs an effective feature fusion by maximizing the pair-wise correlations across the two feature sets, and at the same time, eliminating the between-class correlations and restricting the correlations to be within classes.

The transformation of traditional energy networks to smart grids can assist in revolutionizing the energy industry in terms of reliability, performance and manageability. However, increased connectivity of power grid assets for bidirectional communications presents severe security vulnerabilities. In this paper, we investigate Chi-square detector and cosine similarity matching approaches for attack detection in smart grids where Kalman filter estimation is used to measure any deviation from actual measurements.

We propose a simple yet effective method for visualizing and classifying malware using image processing techniques. Malware binaries are visualized as gray-scale images, with the observation that for many malware families, the images belonging to the same family appear very similar in layout and texture. Motivated by this visual similarity, a classification method using standard image features is proposed. Neither disassembly nor code execution is required for classification.

There is an alarming increase in the amount of malware that is generated today. However, several studies have shown that most of these new malware are just variants of existing ones. Fast detection of these variants plays an effective role in thwarting new attacks. In this paper, we propose a novel approach to detect malware variants using a sparse representation framework. Exploiting the fact that most malware variants have small differences in their structure, we model a new/unknown malware sample as a sparse linear combination of other malware in the training set.

Abstract—While personal information privacy is threatened by online social networks, researchers are seeking for privacy pro- tection tools and methods to assist online social network users. In this paper, we propose a Trust-Aware Privacy Evaluation frame- work, called TAPE, aiming to address this problem. Under the TAPE framework we investigate how to quantitatively evaluate the privacy risk, as a function of people’s awarenesses of privacy risks as well as whether people can be trusted by their friends to protect others’ personal information.