Abstract
Function identification is a preliminary step in binary analysis for many extensive applications from malware detection, common vulnerability detection and binary instrumentation to name a few. In this paper, we propose the Code Pointer Network that leverages the underlying idea of a pointer network to efficiently and effectively tackle function scope identification - the hardest and most crucial task in function identification. We establish extensive experiments to compare our proposed method with the deep learning based baseline. Experimental results demonstrate that our proposed method significantly outperforms the state-of-the-art baseline in terms of both predictive performance and running time.
Original language | English |
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Title of host publication | 2020 International Joint Conference on Neural Networks (IJCNN), 2020 Conference Proceedings2020 International Joint Conference on Neural Networks, IJCNN 2020 - Proceedings |
Editors | Asim Roy |
Place of Publication | Piscataway NJ USA |
Publisher | IEEE, Institute of Electrical and Electronics Engineers |
Pages | 4280-4286 |
Number of pages | 7 |
ISBN (Electronic) | 9781728169262 |
ISBN (Print) | 9781728169279 |
DOIs | |
Publication status | Published - 2020 |
Event | IEEE International Joint Conference on Neural Networks 2020 - Virtual, Glasgow, United Kingdom Duration: 19 Jul 2020 → 24 Jul 2020 https://ieeexplore.ieee.org/xpl/conhome/9200848/proceeding (Proceedings) https://wcci2020.org/ijcnn-sessions/ (Website) |
Conference
Conference | IEEE International Joint Conference on Neural Networks 2020 |
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Abbreviated title | IJCNN 2020 |
Country/Territory | United Kingdom |
City | Virtual, Glasgow |
Period | 19/07/20 → 24/07/20 |
Internet address |
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Keywords
- Cyber Security
- Deep Learning
- Function Scope Identification