Code Pointer Network for binary function scope identification

Van Nguyen, Tue Le, Khanh Nguyen, Olivier de Vel, Paul Montague, DInh Phung

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review


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 languageEnglish
Title of host publication2020 International Joint Conference on Neural Networks (IJCNN), 2020 Conference Proceedings2020 International Joint Conference on Neural Networks, IJCNN 2020 - Proceedings
EditorsAsim Roy
Place of PublicationPiscataway NJ USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages7
ISBN (Electronic)9781728169262
ISBN (Print)9781728169279
Publication statusPublished - 2020
EventIEEE International Joint Conference on Neural Networks 2020 - Virtual, Glasgow, United Kingdom
Duration: 19 Jul 202024 Jul 2020 (Proceedings) (Website)


ConferenceIEEE International Joint Conference on Neural Networks 2020
Abbreviated titleIJCNN 2020
CountryUnited Kingdom
CityVirtual, Glasgow
Internet address


  • Cyber Security
  • Deep Learning
  • Function Scope Identification

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