DeepVulSeeker: A novel vulnerability identification framework via code graph structure and pre-training mechanism
نویسندگان
چکیده
Software vulnerabilities can pose severe harms to a computing system. They lead system crash, privacy leakage, or even physical damage. Correctly identifying among enormous software codes in timely manner is so far the essential prerequisite patch them. Unfortantely, current vulnerability identification methods, either classic ones deep-learning-based ones, have several critical drawbacks, making them unable meet present-day demands put forward by industry. To overcome this paper, we propose DeepVulSeeker, novel fully automated framework, which leverages both code graph structures and semantic features with help of recently advanced Graph Representation Self-Attention pre-training mechanisms. Our experiments show that DeepVulSeeker not only reaches an accuracy as high 0.99 on traditional CWE datasets, but also outperforms all other exisiting methods two highly-complicated datasets. We testified based three case studies, found able understand implications vulnerbilities. implemented open-sourced it for future follow-up research.
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ژورنال
عنوان ژورنال: Future Generation Computer Systems
سال: 2023
ISSN: ['0167-739X', '1872-7115']
DOI: https://doi.org/10.1016/j.future.2023.05.016