DETEKCIJA MALICIOZNIH URL-OVA KORIŠTENJEM METODA MAŠINSKOG UČENJA. (Bosnian)
In: InfoM, 2020-03-01, Heft 71, S. 28-37
Online
academicJournal
Zugriff:
In this paper, we describe methods for detecting malicious URLs using machine learning algorithms with a purpose of discovering rules in data which could not be detected by traditional blacklist approach. The particular challenge was to analyze data sets and choose the most appropriate features. In order to show that efficiently predicting malicious URLs can be done using just URL, without page content, we focus on lexical and host-based features. Further, we implement automated services for gathering and generating all proposed feature values. We explore six binary classifiers using two data sets. The experimental results show that the combination of the proposed URL features and classifiers in this paper can achieve accuracy 96-99%. We also discuss issues and indicate some important open problems for further research. [ABSTRACT FROM AUTHOR]
U ovom radu analizirane su metode detekcije malicioznih URL-ova koristeći algoritme mašinskog učenja sa ciljem otkrivanja pravilnosti u podacima koje nisu mogle biti detektovane tradicionalnim blacklist pristupima. Posebna pažnja posvećena je određivanju skupa atributa koji će se koristiti i implementaciji prikupljanja vrijednosti odabranih atributa. Kao praktični dio implementirano je obučavanje šest predloženih klasifikacionih algoritama na dva skupa podataka. Urađena je evaluacija dobijenih modela kojom se pokazuje da odabrani klasifikatori, sa predloženim skupom atributa, daju tačnost od 96-99%, dakle sa velikom vjerovatnoćom uspijevaju da tačno detektuju maliciozne URL-ove. Takođe, urađena je analiza greške radi boljeg razumijevanja problema i dati su pravci mogućeg daljeg unapređenja. [ABSTRACT FROM AUTHOR]
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Titel: |
DETEKCIJA MALICIOZNIH URL-OVA KORIŠTENJEM METODA MAŠINSKOG UČENJA. (Bosnian)
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Autor/in / Beteiligte Person: | Jokić, Jelena ; Đurić, Zoran |
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Zeitschrift: | InfoM, 2020-03-01, Heft 71, S. 28-37 |
Veröffentlichung: | 2020 |
Medientyp: | academicJournal |
ISSN: | 1451-4397 (print) |
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