Behavioral detection differs from appearance detection in that it identifies the actions performed by the malware rather than syntactic markers. Identifying these malicious actions and interpreting their final purpose is a complex reasoning process. This paper draws up a survey of the different reasoning techniques deployed among the behavioral detectors. These detectors have been classified according to a new taxonomy introduced inside the paper. Strongly inspired from the domain of program testing, this taxonomy divides the behavioral detectors into two main families: simulation-based and formal detectors. Inside these families, ramifications are then derived according to the data collection mechanisms, the data interpretation, the adopted model and its generation, and the decision support.

Abstract. We study a corpus of particular Boolean functions: the idempotents. They enable us to construct functions which achieve the best possible tradeoffs between the cryptographic fundamental properties: balancedness, correlation-immunity, a high degree and a high nonlinearity (that is a high distance from the affine functions). They all represent extremely secure cryptographic primitives to be implemented in stream ciphers.

As a general rule, copycats produce most of malware variants from an original malware strain. For this purpose, they widely perform black-box analyses of commercial scanners aiming at extracting malware detection patterns. In this paper, we first study the malware detection pattern extraction problem from a complexity point of view and provide the results of a wide-scale study of commercial scanners' black-box analysis. These results clearly show that most of the tested commercial products fail to thwart black-box analysis. Such weaknesses therefore urge copycats to produce even more malware variants. Then, we present a new model of malware detection pattern based on Boolean functions and identify some properties that a reliable detection pattern should have. Lastly, we describe a combinatorial, probabilistic malware pattern scanning scheme that, on the one hand, highly limits black-box analysis and on the other hand can only be bypassed in the case where there is collusion between a number of copycats. This scheme can incidentally provide some useful technical information to malware crime investigators, thus allowing a faster identification of copycats.

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