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undergraduate thesis
Detekcija zlonamjernih URL-ova
2022. urn:nbn:hr:168:168354
Petrušić, Nina
University of Zagreb
Faculty of Electrical Engineering and Computing

Cite this document

Petrušić, N. (2022). Detekcija zlonamjernih URL-ova (Undergraduate thesis). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:168354

Petrušić, Nina. "Detekcija zlonamjernih URL-ova." Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2022. https://urn.nsk.hr/urn:nbn:hr:168:168354

Petrušić, Nina. "Detekcija zlonamjernih URL-ova." Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2022. https://urn.nsk.hr/urn:nbn:hr:168:168354

Petrušić, N. (2022). 'Detekcija zlonamjernih URL-ova', Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 26 December 2024, https://urn.nsk.hr/urn:nbn:hr:168:168354

Petrušić N. Detekcija zlonamjernih URL-ova [Undergraduate thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2022 [cited 2024 December 26] Available at: https://urn.nsk.hr/urn:nbn:hr:168:168354

N. Petrušić, "Detekcija zlonamjernih URL-ova", Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2022. Available at: https://urn.nsk.hr/urn:nbn:hr:168:168354

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Metadata
TitleDetekcija zlonamjernih URL-ova
Title (english)Malicious URL Detection
AuthorNina Petrušić
MentorMarin Vuković (mentor)
Committee memberMarin Vuković (predsjednik povjerenstva)
Committee memberRenato Šoić (član povjerenstva)
Committee memberKarlo Slovenec (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Electrical Engineering and Computing
Zagreb
Defense date and country2022-09-01, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Abstract
U radu "Detekcija zlonamjernih URL-ova" navode se problemi tradicionalnih me- toda detekcije zlonamjernih URL-ova te se bavi rješavanjem problema korištenjem me- toda strojnog učenja. Opisane su metode: stablo odlučivanja i slučajna šuma. Opisuje se korišteni skup podataka te način analize URL-a. Navedene metode implementirane su u programskom jeziku Python te su uspoređeni rezultati dobiveni na istom skupu podataka.
Abstract (english)
The paper "Malicious URL detection" lists problems with traditional methods for detecting malicious URLs and explains usage of machine learning methods for mali- cious URL detection. Methods explained and used in this paper are: decision tree and random forest. The paper describes used dataset and URL analysis. Used methods are implemented in programming language Python. Results from both methods using the described dataset are then compared.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:168:168354
Study programmeTitle: Computing
Study programme type: university
Study level: undergraduate
Academic / professional title: sveučilišni/a prvostupnik/ prvostupnica (baccalaureus/baccalaurea) inženjer/inženjerka računarstva (sveučilišni/a prvostupnik/ prvostupnica (baccalaureus/baccalaurea) inženjer/inženjerka računarstva)
Type of resourceText
File originBorn digital
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Created on2023-01-15 11:03:50