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The number of executable malware and the sophistication of their destructive ability has exponentially increased in past couple of years. Malware writers use sophisticated code obfuscation and encryp-tion (a.k.a. packing) techniques to circumvent signatures – derived from the code of the malware for detection – stored in the signatures' database of(More)
In this paper, we present an accurate and realtime PE-Miner framework that automatically extracts distinguishing features from portable executables (PE) to detect zero-day malware without any a priori knowledge about them. The distinguishing features are extracted using the structural information standardized by the Microsoft Windows operating system for(More)
Keywords: Wireless sensor networks Routing protocols Swarm intelligence Ant colony routing Bee-inspired routing a b s t r a c t Swarm intelligence is a relatively novel field. It addresses the study of the collective behaviors of systems made by many components that coordinate using decentralized controls and self-organization. A large part of the research(More)
In this paper we present <i>BeeAdHoc</i>, a new routing algorithm for energy efficient routing in mobile ad hoc networks. The algorithm is inspired by the foraging principles of honey bees. The algorithm mainly utilizes two types of agents, scouts and foragers, for doing routing in mobile ad hoc networks. <i>BeeAdHoc</i> is a reactive source routing(More)
Commercial anti-virus software are unable to provide protection against newly launched (a.k.a "zero-day") malware. In this paper, we propose a novel malware detection technique which is based on the analysis of byte-level file content. The novelty of our approach, compared with existing content based mining schemes, is that it does not memorize specific(More)
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 features' set for accurate user identification. To this(More)
Biomedical datasets pose a unique challenge to machine learning and data mining algorithms for classification because of their high dimensionality, multiple classes, noisy data and missing values. This paper provides a comprehensive evaluation of a set of diverse machine learning schemes on a number of biomedical datasets. To this end, we follow a four step(More)