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Creators/Authors contains: "Raicu, Daniela"

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  1. ; ; ; ; ; (, 15th International Conference on Machine Learning and Computing (ICMLC 2023))
    Detecting software vulnerabilities has been a challenge for decades. Many techniques have been developed to detect vulnerabilities by reporting whether a vulnerability exists in the code of software. But few of them have the capability to categorize the types of detected vulnerabilities, which is crucial for human developers or other tools to analyze and address vulnerabilities. In this paper, we present our work on identifying the types of vulnerabilities using deep learning. Our data consists of code slices parsed in a manner that captures the syntax and semantics of a vulnerability, sourced from prior work. We train deep neural networks on these features to perform multiclass classification of software vulnerabilities in the dataset. Our experiments show that our models can effectively identify the vulnerability classes of the vulnerable functions in our dataset. 
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    Accepted Manuscript Full Text Available
  2. ; ; ; (, 2020 35th International Conference on Image and Vision Computing New Zealand (IVCNZ))
    Full Text Available 
  3. ; ; ; (, 2020 3rd Artificial Intelligence and Cloud Computing Conference)
    Full Text Available 
  4. ; ; ; (, 2020 IEEE 20th International Conference on Bioinformatics and Bioengineering (BIBE))
    Full Text Available 
  5. ; ; ; ; ; (, SPIE Medical Imaging Symposium)
    Landman, Bennett A.; Išgum, Ivana (Ed.)
    Full Text Available 
  6. ; ; ; (, 2019 IEEE 19th International Conference on Bioinformatics and Bioengineering (BIBE))
    Full Text Available 
  7. ; ; ; ; (, International Conference on Computational Science and Computational Intelligence)
    null (Ed.)
    Full Text Available 
  8. ; ; ; ; ; ; ; (, SPIE Medical Imaging Symposium)
    Hahn, Horst K.; Mazurowski, Maciej A. (Ed.)
    Full Text Available