Martin, A;
Menéndez, HD;
Camacho, D;
(2016)
Genetic boosting classification for malware detection.
In:
Proceedings of the Congress on Evolutionary Computation (CEC) 2016.
(pp. pp. 1030-1037).
IEEE: Danvers (MA), USA.
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Abstract
In the last few years virus writers have made use of new obfuscation techniques with the aim of hindering malware in order to difficult their detection by Anti-Virus engines. Strategies to reverse this trend involve executing potentially malicious programs and monitor the actions they perform in runtime, what is known as dynamic analysis. In this paper we present a method able to reach a high accuracy rate without using this kind of analysis. Instead we use a static analysis approach, which discards those samples that cannot be classified with enough certainty and need, certainly, a dynamic analysis. The K-means clustering algorithm has been used to group samples into regions according to their features. Then a boosting process, guided by a genetic algorithm, is executed in each region that are evaluated using a test dataset discarding those regions which do not reach a minimum accuracy threshold.
| Type: | Proceedings paper |
|---|---|
| Title: | Genetic boosting classification for malware detection |
| Event: | Congress on Evolutionary Computation (CEC) 2016 |
| Location: | Vancouver (BC), Canada |
| Dates: | 24th-29th July 2016 |
| ISBN-13: | 978-1-5090-0623-6 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/CEC.2016.7743902 |
| Publisher version: | http://doi.org/10.1109/CEC.2016.7743902 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Malware, Boosting, Clustering algorithms, Genetic algorithms, Genetics, Training, Performance analysis |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10060102 |
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