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master's thesis
Detekcija zloćudnog koda iz tokova podataka
Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing, 2020. urn:nbn:hr:168:719990
Mesarić, Marko
University of Zagreb
Faculty of Electrical Engineering and Computing

Institutional repository: FER Repository

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Mesarić, M. (2020). Detekcija zloćudnog koda iz tokova podataka (Master's thesis). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:719990

Mesarić, Marko. "Detekcija zloćudnog koda iz tokova podataka." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2020. https://urn.nsk.hr/urn:nbn:hr:168:719990

Mesarić, Marko. "Detekcija zloćudnog koda iz tokova podataka." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2020. https://urn.nsk.hr/urn:nbn:hr:168:719990

Mesarić, M. (2020). 'Detekcija zloćudnog koda iz tokova podataka', Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 25 June 2024, https://urn.nsk.hr/urn:nbn:hr:168:719990

Mesarić M. Detekcija zloćudnog koda iz tokova podataka [Master's thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2020 [cited 2024 June 25] Available at: https://urn.nsk.hr/urn:nbn:hr:168:719990

M. Mesarić, "Detekcija zloćudnog koda iz tokova podataka", Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2020. Available at: https://urn.nsk.hr/urn:nbn:hr:168:719990

Metadata
TitleDetekcija zloćudnog koda iz tokova podataka
Title (english)Malicious code detection from data streams
AuthorMarko Mesarić
MentorMarin Vuković (mentor)
Committee memberMarin Vuković (predsjednik povjerenstva)
Committee memberMiljenko Mikuc (član povjerenstva)
Committee memberDragan Jevtić (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Electrical Engineering and Computing
Zagreb
Defense date and country2020-07-08, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Abstract
U ovom radu analiziran je pojam zloćudnog programa i prikazana je podjela zloćudnog programa u karakteristične razrede. Analizirane su metode detekcije zloćudnog koda iz tokova podataka s naglaskom na metode koje detektiraju zloćudan kod iz podatkovnog dijela paketa mrežnog prometa. Dan je uvod u konvolucijske neuronske mreže. Prezentiran je ostvaren sustav za detekciju zloćudnog koda iz podatkovnog dijela TCP paketa temeljen na dubokom učenju, odnosno konvolucijskim neuronskim mrežama. Opisan je proces prikupljanja i pripreme skupa podataka korišten za učenje i validaciju ostvarenog modela. Prikazani su ostvareni rezultati i predložene su metode za buduća poboljšanja.
Abstract (english)
In this paper, the concept of malware is analyzed and the division of malware into characteristic classes is presented. Methods of detecting malicious code from data streams are analyzed, with emphasis on methods that detect malicious code from the network traffic payloads. An introduction to convolutional neural networks is presented. An implemented system for detecting malicious code from the TCP packet payloads based on deep learning, that is, convolutional neural networks, is presented. The process of data collection and preparation used for learning and validation of the realized model is described. The achieved results are presented and methods for future improvements are proposed.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:168:719990
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
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Created on2023-01-13 10:25:20