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Abstract. Smart phones are now being used to store users' identities and sensitive information/data. Therefore, it is important to authenticate legitimate users of a smart phone and to block imposters. In this paper, we demonstrate that keystroke dynamics of a smart phone user can be translated into a viable feature set for accurate user identification. To this end, we collect and analyze keystroke data of 25 diverse smart phone users. Based on this analysis, we select six distinguishing keystroke features that can be used for user identification. We show that these keystroke features for different users are diffused and therefore a fuzzy classifier is well-suited to cluster and classify them. We then optimize the front-end fuzzy classifier using Particle Swarm Optimizer (PSO) and Genetic Algorithm (GA) as back-end dynamic optimizers to adapt to variations in usage patterns. Finally, we provide a novel keystroke dynamics based PIN verification mode to ensure information security on smart phones. The results of our experiments show that the proposed user identification system has an average error rate of 2% after the detection mode and the error rate of rejecting legitimate users is dropped to zero after the PIN verification mode. We also compare error rates (in terms of detecting both legitimate users and imposters) of our proposed classifier with 5 existing state-of-the-art techniques for user identification on desktop computers. Our results show that the proposed technique consistently and considerably outperforms existing schemes.

Abstract. Embedded malware is a recently discovered security threat that allows malcode to be hidden inside a benign file. It has been shown that embedded malware is not detected by commercial antivirus software even when the malware signature is present in the antivirus database. In this paper, we present a novel anomaly detection scheme to detect embedded malware. We first analyze byte sequences in benign files to show that benign files' data generally exhibit a 1-st order dependence structure. Consequently, conditional n-grams provide a more meaningful representation of a file's statistical properties than traditional n-grams. To capture and leverage this correlation structure for embedded malware detection, we model the conditional distributions as Markov n-grams. For embedded malware detection, we use an information-theoretic measure, called entropy rate, to quantify changes in Markov n-gram distributions observed in a file. We show that the entropy rate of Markov n-grams gets significantly perturbed at malcode embedding locations, and therefore can act as a robust feature for embedded malware detection. We evaluate the proposed Markov n-gram detector on a comprehensive malware dataset consisting of more than 37, 000 malware samples and 1, 800 benign samples of six well-known filetypes. We show that the Markov n-gram detector provides better detection and false positive rates than the only existing embedded malware detection scheme.

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This is the unspecified version of the paper.This version of the publication may differ from the final published version. by the large number of algorithms, techniques, and protocols that have been developed to save energy, and thereby extend the lifetime of the network. However, in the context of WSN's routing and dissemination, Connected Dominating Set (CDS) principle has emerged as the most popular method for energy-efficient topology control (TC) in WSN's. In a CDS-based topology control technique, a virtual backbone is formed which allows communication between any arbitrary pair of nodes in the network. In this paper, we present a CDS based topology control protocol -A1 -which forms an energy efficient virtual backbone. In our simulations, we compare the performance of A1 with three prominent CDS-based protocols namely Energy-efficient CDS (EECDS), CDS Rule K and A3. The results demonstrate that A1 performs consistently better in terms of message overhead and other selected metrics. Moreover, the A1 protocol not only achieves better connectivity under topology maintenance but also provides better sensing coverage when compared with the other protocols. Permanent repository link

Wireless video sensor networks are anticipated to be deployed to monitor remote geographical areas. To save energy in bit transmissions/receptions over a video sensor network, the captured video content needs to be encoded before its transmission to the base station. However, video encoding is an inherently complex operation that can cause a major energy drain at battery-constrained sensors. Thus a systematic evaluation of different video encoding options is required to allow a designer to choose the most energy-efficient compression technique for a given video sensing application scenario. In this paper, we empirically evaluate the energy efficiencies of predictive and distributed video coding paradigms for deployment on real-life sensor motes. For predictive video coding, our results show that despite its higher compression efficiency, inter video coding always depletes much more energy than intra coding. Therefore, we propose to use image compression based intra coding to improve energy efficiency in the predictive video coding paradigm. For distributed video coding, our results show that the Wyner-Ziv encoder has consistently better energy efficiency than the PRISM encoder. We propose minor modifications to PRISM and Wyner-Ziv encoders which significantly reduce the energy consumption of these encoders. For all the video encoding configurations evaluated in this paper, our results reveal the counter-intuitive and important finding that the major source of energy drain in WSNs is local computations performed for video compression and not video transmission.