Evolving Decision Trees for the Categorization of Software
Current manual techniques of static reverse engineering are inefficient at providing semantic program understanding. We have developed an automated method to categorize applications in order to quickly determine pertinent characteristics. Prior work in this area has had some success, but a major strength of our approach is that it produces heuristics that can be reused for quick analysis of new data. Our method relies on a genetic programming algorithm to evolve decision trees which can be used to categorize software. The terminals, or leaf nodes, within the trees each contain values based on selected features from one of several attributes: system calls, byte n-grams, opcode n-grams, cyclomatic complexity, and bonding. The evolved decision trees are reusable and achieve average accuracies above 95% when categorizing programs based on compiler origin and versions. Developing new decision trees simply requires more labeled datasets and potentially different feature selection algorithms for other attributes, depending on the data being classified.
J. Hosic et al., "Evolving Decision Trees for the Categorization of Software," Proceedings of the IEEE 38th Annual International Computers, Software and Applications Conference Workshops, COMPSACW 2014, pp. 337, Institute of Electrical and Electronics Engineers (IEEE), Jan 2014.
The definitive version is available at http://dx.doi.org/10.1109/COMPSACW.2014.59
38th Annual IEEE Computer Software and Applications Conference Workshops, COMPSACW 2014 (2014: Jul. 27-29, Vasteras, Sweden)
Missouri University of Science and Technology. Natural Computation Laboratory
Keywords and Phrases
Genetic Programming; Program Understanding
International Standard Book Number (ISBN)
Article - Conference proceedings
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