Random-Forest-Based Analysis of URL Paths

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F17%3A00478626" target="_blank" >RIV/67985807:_____/17:00478626 - isvavai.cz</a>

Result on the web

<a href="http://ceur-ws.org/Vol-1885/129.pdf" target="_blank" >http://ceur-ws.org/Vol-1885/129.pdf</a>

DOI - Digital Object Identifier

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Result language

angličtina

Original language name

Random-Forest-Based Analysis of URL Paths

Original language description

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.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

<a href="/en/project/GA17-01251S" target="_blank" >GA17-01251S: Metalearning for Extraction of Rules with Numerical Consequents</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2017

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

Proceedings ITAT 2017: Information Technologies - Applications and Theory

ISBN

978-1974274741

ISSN

1613-0073

e-ISSN

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Number of pages

7

Pages from-to

129-135

Publisher name

Technical University & CreateSpace Independent Publishing Platform

Place of publication

Aachen & Charleston

Event location

Martinské hole

Event date

Sep 22, 2017

Type of event by nationality

EUR - Evropská akce

UT code for WoS article

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