Annals of Applied Statistics

Stochastic identification of malware with dynamic traces

Curtis Storlie, Blake Anderson, Scott Vander Wiel, Daniel Quist, Curtis Hash, and Nathan Brown

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Abstract

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 nonmalware programs, and the results are compared to other leading detection schemes (both signature and classification based). This article also has supplementary materials available online.

Article information

Source
Ann. Appl. Stat., Volume 8, Number 1 (2014), 1-18.

Dates
First available in Project Euclid: 8 April 2014

Permanent link to this document
https://projecteuclid.org/euclid.aoas/1396966276

Digital Object Identifier
doi:10.1214/13-AOAS703

Mathematical Reviews number (MathSciNet)
MR3191980

Zentralblatt MATH identifier
06302225

Keywords
Malware detection classification elastic net Relaxed Lasso Adaptive Lasso logistic regression splines empirical Bayes

Citation

Storlie, Curtis; Anderson, Blake; Vander Wiel, Scott; Quist, Daniel; Hash, Curtis; Brown, Nathan. Stochastic identification of malware with dynamic traces. Ann. Appl. Stat. 8 (2014), no. 1, 1--18. doi:10.1214/13-AOAS703. https://projecteuclid.org/euclid.aoas/1396966276


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Supplemental materials

  • Supplementary material: Supplement to “Stochastic identification and clustering of malware with dynamic traces”. This article also has a supplemental document Storlie et al. (2014) available online which presents preliminary work on the clustering of malware, to aid in reverse engineering. Some computational complexity considerations for the proposed method are also discussed.
    Digital Object Identifier: doi:10.1214/13-AOAS703SUPP
    Supplemental PDF (1031 KB)

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