VulCatch: Enhancing Binary Vulnerability Detection through CodeT5 Decompilation and KAN Advanced Feature Extraction
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866911987402801152 |
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| author | Chukkol, Abdulrahman Hamman Adama Luo, Senlin Sharif, Kashif Haruna, Yunusa Abdullahi, Muhammad Muhammad |
| author_facet | Chukkol, Abdulrahman Hamman Adama Luo, Senlin Sharif, Kashif Haruna, Yunusa Abdullahi, Muhammad Muhammad |
| contents | Binary program vulnerability detection is critical for software security, yet existing deep learning approaches often rely on source code analysis, limiting their ability to detect unknown vulnerabilities. To address this, we propose VulCatch, a binary-level vulnerability detection framework. VulCatch introduces a Synergy Decompilation Module (SDM) and Kolmogorov-Arnold Networks (KAN) to transform raw binary code into pseudocode using CodeT5, preserving high-level semantics for deep analysis with tools like Ghidra and IDA. KAN further enhances feature transformation, enabling the detection of complex vulnerabilities. VulCatch employs word2vec, Inception Blocks, BiLSTM Attention, and Residual connections to achieve high detection accuracy (98.88%) and precision (97.92%), while minimizing false positives (1.56%) and false negatives (2.71%) across seven CVE datasets. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_07181 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | VulCatch: Enhancing Binary Vulnerability Detection through CodeT5 Decompilation and KAN Advanced Feature Extraction Chukkol, Abdulrahman Hamman Adama Luo, Senlin Sharif, Kashif Haruna, Yunusa Abdullahi, Muhammad Muhammad Cryptography and Security Artificial Intelligence Machine Learning Software Engineering Binary program vulnerability detection is critical for software security, yet existing deep learning approaches often rely on source code analysis, limiting their ability to detect unknown vulnerabilities. To address this, we propose VulCatch, a binary-level vulnerability detection framework. VulCatch introduces a Synergy Decompilation Module (SDM) and Kolmogorov-Arnold Networks (KAN) to transform raw binary code into pseudocode using CodeT5, preserving high-level semantics for deep analysis with tools like Ghidra and IDA. KAN further enhances feature transformation, enabling the detection of complex vulnerabilities. VulCatch employs word2vec, Inception Blocks, BiLSTM Attention, and Residual connections to achieve high detection accuracy (98.88%) and precision (97.92%), while minimizing false positives (1.56%) and false negatives (2.71%) across seven CVE datasets. |
| title | VulCatch: Enhancing Binary Vulnerability Detection through CodeT5 Decompilation and KAN Advanced Feature Extraction |
| topic | Cryptography and Security Artificial Intelligence Machine Learning Software Engineering |
| url | https://arxiv.org/abs/2408.07181 |