Random-Forest-Based Analysis of URL Paths

0478626 - ÚI 2018 RIV DE eng C - Konferenční příspěvek (zahraniční konf.)
Puchýř, J. - Holeňa, Martin
Random-Forest-Based Analysis of URL Paths.
Proceedings ITAT 2017: Information Technologies - Applications and Theory. Aachen & Charleston: Technical University & CreateSpace Independent Publishing Platform, 2017 - (Hlaváčová, J.), s. 129-135. CEUR Workshop Proceedings, V-1885. ISBN 978-1974274741. ISSN 1613-0073.
[ITAT 2017. Conference on Theory and Practice of Information Technologies - Applications and Theory /17./. Martinské hole (SK), 22.09.2017-26.09.2017]
Grant CEP: GA ČR GA17-01251S
Institucionální podpora: RVO:67985807
Klíčová slova: malicious URLs detection * classification * random forest
Obor OECD: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
http://ceur-ws.org/Vol-1885/129.pdf

One of the key sources of spreading malware are malicious web sites - either tricking user to install malware imitating legitimate software or, in the case of various exploit kits, initiating malware installation even without any user action. The most common technique against such web sites is blacklisting. However, it provides little to no information about new sites never seen before. Therefore, there has been important research into predicting malicious web sites based on their features. This work-in-progress paper presents a light-weight prediction method using solely lexical features of the site URL and classification by random forests. To this end, three possibilities of feature extraction have been elaborated and investigated on real-world data sets with respect to precision and recall. The obtained results indicate that there is nearly never a significant difference betweeen the considered methods, and that in spite of the limitation to the lexical features of the site URL, they have an impressive performance in terms of area under the precision-recall curve for the path parts of URLs.
Trvalý link: http://hdl.handle.net/11104/0274765