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master's thesis
Dekompajliranje Java aplikacija potpomognuto metodama strojnog učenja
Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing, 2018. urn:nbn:hr:168:633254
Kucijan, Mihovil
University of Zagreb
Faculty of Electrical Engineering and Computing

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Kucijan, M. (2018). Dekompajliranje Java aplikacija potpomognuto metodama strojnog učenja (Master's thesis). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:633254

Kucijan, Mihovil. "Dekompajliranje Java aplikacija potpomognuto metodama strojnog učenja." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2018. https://urn.nsk.hr/urn:nbn:hr:168:633254

Kucijan, Mihovil. "Dekompajliranje Java aplikacija potpomognuto metodama strojnog učenja." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2018. https://urn.nsk.hr/urn:nbn:hr:168:633254

Kucijan, M. (2018). 'Dekompajliranje Java aplikacija potpomognuto metodama strojnog učenja', Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 24 August 2024, https://urn.nsk.hr/urn:nbn:hr:168:633254

Kucijan M. Dekompajliranje Java aplikacija potpomognuto metodama strojnog učenja [Master's thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2018 [cited 2024 August 24] Available at: https://urn.nsk.hr/urn:nbn:hr:168:633254

M. Kucijan, "Dekompajliranje Java aplikacija potpomognuto metodama strojnog učenja", Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2018. Available at: https://urn.nsk.hr/urn:nbn:hr:168:633254

Metadata
TitleDekompajliranje Java aplikacija potpomognuto metodama strojnog učenja
Title (english)Decompilation of Java Applications Supported by Machine Learning Algorithms
AuthorMihovil Kucijan
MentorStjepan Groš (mentor)
Committee memberStjepan Groš (predsjednik povjerenstva)
Committee memberAnte Đerek (član povjerenstva)
Committee memberDejan Škvorc (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Electrical Engineering and Computing
Zagreb
Defense date and country2018-07-19, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Abstract
Razvojem strojnog učenja i povećanjem proceduralne moći računala, otvorio se novi način za pristupanju problemima. Metode strojnog učenja kao neuronske mreže omogućavaju iskorištavanje velike količine podataka kako bi se u sisteme ugradilo znanje o obrascima i pravilima bez direktnog programiranja u svrhu rješavanje problema. U ovom radu sagledat će se moguća primjena umjetnih neuronskih mreža za probleme dekompajliranje Java bajtkoda. Prezentirana su dva modela koji nastoje otkriti strukturu apstraktnih izraza izvornog jezika iz niza instrukcija koje su zapisane u izvršnom formatu. Prvi model koristeći povratne neuronske mreže označava instrukcije koje su generirana iz izraza uvjeta i petlja u izvornom kodu. Drugi model koristi povratne mreže kako bi iz niza instrukcija generirao oznake početaka i kraja izraza izvornog jezika. Modeli postižu minimalnu funkcionalnost prepoznavajući najjasnije obrasce izraza. Kod dužih i kompliciranijih nizova otkrivaju tek dio strukture te nisu u mogućnosti naći jasne granice izraza.
Abstract (english)
By development of machine learning techniques and increasing computer process abilities, new approach to solving problems has been presented. Machine learning methods like neural networks allow for analysis of large amounts of data to include knowledge of patterns and rules without usage of direct programming for solving problems. This paper will consider application of artificial neural networks for the problems of decompiling Java bytecode. Two models have been introduced to find the structure of abstract expressions of the original language from a series of instructions from a compiled code. The first model uses recurrent neural network to classify the instructions that were generated from conditional and loop expressions in the source code. The other model uses a recurrent neural networks to generate start and end labels of source expressions. Models achieve minimal functionality by recognizing the clearest patterns of expressions. On longer and more complicated array of instructions, model reveal only a part of the structure and is not able to find clear boundaries of expressions.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:168:633254
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 on2019-03-13 22:03:19