We consider families F(∆) consisting of complex (n − 1)-dimensional projective algebraic compactifications of ∆-regular affine hypersurfaces Zf defined by Laurent polynomials f with a fixed n-dimensional Newton polyhedron ∆ in n-dimensional algebraic torus T = (C ∗ ) n. If the family F(∆) defined

consistently fails to detect new malware. In this paper, we propose a new method to detect unknown malware families. This model is based on the frequency of the appearance of opcode sequences. Furthermore, we describe a technique to mine the relevance of each opcode and assess the frequency of each opcode

single-class learning to detect unknown malware families. This method is based on examining the frequencies of the appearance of opcode sequences to build a machine-learning classifier using only one set of labelled instances within a specific class of either malware or legitimate software. We performed

more than 1,200 malware samples that cover the majority of existing Android malware families, ranging from their debut in August 2010 to recent ones in October 2011. In addition, we systematically characterize them from various aspects, including their installation methods, activation mech

evolved virus to its machinelevel code. In this paper, we validate the notion of evolution in viruses on a well-known virus family, called Bagle. The results of our proof-of-concept study show that we have successfully evolved new viruses–previously unknown and known-variants of Bagle–starting from a

Malicious programs spy on users ’ behavior and compromise their privacy. Even software from reputable vendors, such as Google Desktop and Sony DRM media player, may perform undesirable actions. Unfortunately, existing techniques for detecting malware and analyzing unknown code samples

We present here a simple and fast method allowing the isolation of DNA binding sites for transcription factors from families of coregulated genes, with results illustrated in Saccharomyces cerevisiae. Although conceptually simple, the algorithm proved efficient for extracting, from most

to develop effective detection techniques and removal tools. Currently, malware analysis is mostly a manual process that is tedious and time-intensive. To mitigate this problem, a number of analysis tools have been proposed that automatically extract the behavior of an unknown program by executing it in a

of their variants severely undermine the effectiveness of traditional defenses which typically use signature based techniques and are unable to detect the previously unknown malicious executables. The variants of malware families share typical behavioral patterns reflecting their origin and purpose. The behavioral

families share typical behavioral patterns reflecting its origin and purpose. We aim to exploit these shared patterns for classification of malware and propose a method for learning and discrimination of malware behavior. Our method proceeds in three stages: (a) behavior of collected malware is monitored