Evolving Decision Trees for the Categorization of Software

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{Hosic:2014:COMPSACW,

• author = "Jasenko Hosic and Daniel R. Tauritz and Samuel A. Mulder",
• title = "Evolving Decision Trees for the Categorization of Software",
• booktitle = "Proceedings of the 38th IEEE Annual Computers, Software and Applications Conference Workshops (COMPSACW '14)",
• year = "2014",
• pages = "337--342",
• address = "Vasteras",
• month = "21-25 " # jul,
• publisher = "IEEE",
• keywords = "genetic algorithms, genetic programming, program understanding, SBSE, software categorisation, decision trees",
• DOI = "doi:10.1109/COMPSACW.2014.59",
• size = "6 pages",
• abstract = "Current manual techniques of static reverse engineering are inefficient at providing semantic program understanding. We have developed an automated method to categorise 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 categorise 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 95percent when categorising programs based on compiler origin and versions. Developing new decision trees simply requires more labelled datasets and potentially different feature selection algorithms for other attributes, depending on the data being classified.",
• notes = "Dept. of Comput. Sci., Missouri Univ. of Sci. & Technol., Rolla, MO, USA Also known as \cite{6903152}",

}

Genetic Programming entries for Jasenko Hosic Daniel R Tauritz Samuel A Mulder

Citations