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master's thesis
Klasifikacija zloćudnih programa primjenom dubokog učenja
Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing, 2019. urn:nbn:hr:168:332368
Brajdić, Ivona
University of Zagreb
Faculty of Electrical Engineering and Computing

Institutional repository: FER Repository

Cite this document

Brajdić, I. (2019). Klasifikacija zloćudnih programa primjenom dubokog učenja (Master's thesis). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:332368

Brajdić, Ivona. "Klasifikacija zloćudnih programa primjenom dubokog učenja." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2019. https://urn.nsk.hr/urn:nbn:hr:168:332368

Brajdić, Ivona. "Klasifikacija zloćudnih programa primjenom dubokog učenja." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2019. https://urn.nsk.hr/urn:nbn:hr:168:332368

Brajdić, I. (2019). 'Klasifikacija zloćudnih programa primjenom dubokog učenja', Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 25 August 2024, https://urn.nsk.hr/urn:nbn:hr:168:332368

Brajdić I. Klasifikacija zloćudnih programa primjenom dubokog učenja [Master's thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2019 [cited 2024 August 25] Available at: https://urn.nsk.hr/urn:nbn:hr:168:332368

I. Brajdić, "Klasifikacija zloćudnih programa primjenom dubokog učenja", Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2019. Available at: https://urn.nsk.hr/urn:nbn:hr:168:332368

Metadata
TitleKlasifikacija zloćudnih programa primjenom dubokog učenja
Title (english)Malware Classification Based on Deep Learning
AuthorIvona Brajdić
MentorZoran Kalafatić (mentor)
Committee memberZoran Kalafatić (predsjednik povjerenstva)
Committee memberTomislav Hrkać (član povjerenstva)
Committee memberStjepan Groš (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Electrical Engineering and Computing
Zagreb
Defense date and country2019-07-11, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Abstract
Zloćudni programi problem su svakog korisnika Interneta, a sve su brojniji i mogu izazvati veliku štetu. Godišnje se otkriju stotine milijuna novih varijanti zloćudnih programa, a praćenje istih postaje sve kompliciranije. Međutim, nove varijante zloćudnih programa najvećim dijelom nastaju kao manje modifikacije već poznatih programa. Napadači tako pokušavaju izbjeći njihovu detekciju uz jednaku funkcionalnost. Zbog toga se pojavila potreba svrstavanja zloćudnih programa u obitelji, odnosno grupe programa čija je funkcionalnost međusobno jednaka, no datoteke su naizgled različite. Ovaj rad bavi se problematikom svrstavanja zloćudnih programa u obitelji, primjenom vizualizacije njihovog binarnog koda te klasifikacijom dobivenih slika konvolucijskom neuronskom mrežom. Istražuju se prednosti i nedostaci ovakvog pristupa kroz implementaciju vizualizacije i klasifikacije te evaluaciju klasifikatora.
Abstract (english)
Nearly every Internet user has come across a malicious program at least once. Malware can cause financial damage and invade people's privacy. Research has shown that hundreds of millions of new malware variants are discovered each year and detecting them is becoming more complicated. However, most of these new variants are just modifications of existing malicious programs. Attackers modify malware in order to avoid detection, which results in many seemingly different files that have the same behaviour. In order to be effective in analyzing and classifying these files, we should be able to group them and identify their respective families. This thesis discusses the method of malware binary visualization following with a convolutional neural network that predicts the family to which the malware belongs.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:168:332368
Study programmeTitle: Computing
Study programme type: university
Study level: graduate
Academic / professional title: magistar/magistra inženjer/inženjerka računarstva (magistar/magistra inženjer/inženjerka računarstva)
Type of resourceText
File originBorn digital
Access conditionsClosed access
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Created on2020-01-07 16:58:47