Vulnerability Detection in Assembly Code Using Deep Learning

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dc.contributor.author Thangavelu, Karthiga
dc.date.accessioned 2023-03-02T00:49:06Z
dc.date.available 2023-03-02T00:49:06Z
dc.date.copyright 2022 en_US
dc.date.issued 2023-03-01
dc.identifier.uri http://hdl.handle.net/1828/14805
dc.description.abstract Language modelling for source code is a state-of-the-art method which is developing significantly in recent years. Its applications are found in code completion, translating programming languages from one to another, translating text documents to code, finding vulnerabilities in source code, etc. Unlike other source code modelling such as C, C++ or Python, modelling assembly language is a tedious process. Most of the approaches involved in feature engineering are manual in assembly code. In this project, the pattern of assembly code is recognized, and malicious code is classified from non-malicious code. The strings of jumps are introduced into the assembly code to make it non-malicious. The pattern recognition and classification process consist of 3 main tasks. Firstly, the strings of jumps are introduced to the assembly code and tokenize the assembly code. Secondly, converting instructions to vectors using assembly language model for instruction embedding based on BERT language transformer, which minimizes the manual process of dataset pre-processing. The final task is a downstream task where the instruction embeddings are fed into the LSTM network for classifying malicious code from non-malicious code using an assembly code dataset. The performance of the model is evaluated using various evaluation metrics such as accuracy, confusion matrix, recall, precision, and F1 score. en_US
dc.language.iso en en_US
dc.rights Available to the World Wide Web en_US
dc.subject Vulnerability detection en_US
dc.subject Assembly code en_US
dc.subject Transformer based-model en_US
dc.subject Instruction embedding en_US
dc.title Vulnerability Detection in Assembly Code Using Deep Learning en_US
dc.type project en_US
dc.contributor.supervisor Sima, Mihai
dc.degree.department Department of Anthropology en_US
dc.degree.level Master of Engineering M.Eng. en_US
dc.description.scholarlevel Graduate en_US

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