Analiza i detekcija zlonamjernog Javascript koda ; Analysis and detection of malicious Javascript code
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| Title: | Analiza i detekcija zlonamjernog Javascript koda ; Analysis and detection of malicious Javascript code |
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| Authors: | Pušelj, Dora |
| Contributors: | Vuković, Marin |
| Publisher Information: | Sveučilište u Zagrebu. Fakultet elektrotehnike i računarstva. University of Zagreb. Faculty of Electrical Engineering and Computing. |
| Publication Year: | 2021 |
| Collection: | Croatian Digital Theses Repository (National and University Library in Zagreb) |
| Subject Terms: | JavaScript, zlonamjerni JavaScript, strojno učenje, AST stablo, malicious JavaScript, machine learning, AST tree, TEHNIČKE ZNANOSTI. Računarstvo, TECHNICAL SCIENCES. Computing |
| Description: | Kako je Internet postao važan dio naše svakodnevice, tako su i razni napadi putem njega postali sve ćešći. Jedan od izvora takvih napada je upravo JavaScript kod. U ovome radu pokušava se detektirati takav zlonamjerni JavaScript kod tehnikama strojnog učenja. Iz JavaScript koda izgradi se apstraktno sintaksno stablo iz kojeg se pomoću DFS algoritma dobiju sekvence sintaktičkih jedinica. Zatim se koristi Word2Vec model kako bi se dobila numerička reprezentacija tih sintaktičkih jedinica. Učeni su modeli unaprijedna slojevita neuronska mreža, LSTM te BiLSTM te je pokazano kako BiLSTM daje najbolje rezultate. ; As the Internet has become an important part of our everyday lives, the attacks by its means became more pervasive too. One of the sources of such attacks is JavaScript. In this thesis, a model for detecting malicious JavaScript is proposed. An AST tree is built from JavaScript source code and converted to a sequence of syntactic units by a depth-first search algorithm. Moreover, a Word2Vec model is used to obtain a numeric representation of the syntactic units. Three models are trained: feed-forward neural network, LSTM, and BiLSTM. The BiLSTM model turned out to give the best results. |
| Document Type: | master thesis |
| File Description: | application/pdf |
| Language: | Croatian |
| Relation: | https://zir.nsk.hr/islandora/object/fer:8557; https://urn.nsk.hr/urn:nbn:hr:168:452696; https://repozitorij.unizg.hr/islandora/object/fer:8557; https://repozitorij.unizg.hr/islandora/object/fer:8557/datastream/PDF |
| Availability: | https://zir.nsk.hr/islandora/object/fer:8557 https://urn.nsk.hr/urn:nbn:hr:168:452696 https://repozitorij.unizg.hr/islandora/object/fer:8557 https://repozitorij.unizg.hr/islandora/object/fer:8557/datastream/PDF |
| Rights: | http://rightsstatements.org/vocab/InC/1.0/ ; info:eu-repo/semantics/closedAccess |
| Accession Number: | edsbas.9FC4C14C |
| Database: | BASE |
Nájsť tento článok vo Web of Science | Abstract: | Kako je Internet postao važan dio naše svakodnevice, tako su i razni napadi putem njega postali sve ćešći. Jedan od izvora takvih napada je upravo JavaScript kod. U ovome radu pokušava se detektirati takav zlonamjerni JavaScript kod tehnikama strojnog učenja. Iz JavaScript koda izgradi se apstraktno sintaksno stablo iz kojeg se pomoću DFS algoritma dobiju sekvence sintaktičkih jedinica. Zatim se koristi Word2Vec model kako bi se dobila numerička reprezentacija tih sintaktičkih jedinica. Učeni su modeli unaprijedna slojevita neuronska mreža, LSTM te BiLSTM te je pokazano kako BiLSTM daje najbolje rezultate. ; As the Internet has become an important part of our everyday lives, the attacks by its means became more pervasive too. One of the sources of such attacks is JavaScript. In this thesis, a model for detecting malicious JavaScript is proposed. An AST tree is built from JavaScript source code and converted to a sequence of syntactic units by a depth-first search algorithm. Moreover, a Word2Vec model is used to obtain a numeric representation of the syntactic units. Three models are trained: feed-forward neural network, LSTM, and BiLSTM. The BiLSTM model turned out to give the best results. |
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