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Stochastic identification of malware with dynamic traces. (English) Zbl 1429.62713
Summary: A novel approach to malware classification is introduced based on analysis of instruction traces that are collected dynamically from the program in question. The method has been implemented online in a sandbox environment (i.e., a security mechanism for separating running programs) at Los Alamos National Laboratory, and is intended for eventual host-based use, provided the issue of sampling the instructions executed by a given process without disruption to the user can be satisfactorily addressed. The procedure represents an instruction trace with a Markov chain structure in which the transition matrix, $$\mathbf{P}$$, has rows modeled as Dirichlet vectors. The malware class (malicious or benign) is modeled using a flexible spline logistic regression model with variable selection on the elements of $$\mathbf{P}$$, which are observed with error. The utility of the method is illustrated on a sample of traces from malware and non-malware programs, and the results are compared to other leading detection schemes (both signature and classification based).

##### MSC:
 62P30 Applications of statistics in engineering and industry; control charts 62J12 Generalized linear models (logistic models)
reglogit
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##### References:
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