Developer’s Roadmap to Design Software Vulnerability Detection Model Using Different AI Approaches
Automatic software vulnerability detection has caught the eyes of researchers as because software vulnerabilities are exploited vehemently causing major cyber-attacks. Thus, designing a vulnerability detector is an inevitable approach to eliminate vulnerabilities. With the advances of Natural langua...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2022-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9830729/ |
| _version_ | 1811342048288047104 |
|---|---|
| author | Pooja S Chandrakala C. B. Laiju K. Raju |
| author_facet | Pooja S Chandrakala C. B. Laiju K. Raju |
| author_sort | Pooja S |
| collection | DOAJ |
| description | Automatic software vulnerability detection has caught the eyes of researchers as because software vulnerabilities are exploited vehemently causing major cyber-attacks. Thus, designing a vulnerability detector is an inevitable approach to eliminate vulnerabilities. With the advances of Natural language processing in the application of interpreting source code as text, AI approaches based on Machine Learning, Deep Learning and Graph Neural Network have impactful research works. The key requirement for developing an AI based vulnerability detector model from a developer perspective is to identify which AI model to adopt, availability of labelled dataset, how to represent essential feature and tokenizing the extracted feature vectors, specification of vulnerability coverage with detection granularity. Most of the literature review work explores AI approaches based on either Machine Learning or Deep Learning model. The existing literature work either highlight only feature representation technique or identifying granularity level and dataset. A qualitative comparative analysis on ML, DL, GNN based model is presented in this work to get a complete picture on VDM thus addressing the challenges of a researcher to choose suitable architecture, feature representation and processing required for designing a VDM. This work focuses on putting together all the essential bits required for designing an automated software vulnerability detection model using any various AI approaches. |
| first_indexed | 2024-04-13T19:04:38Z |
| format | Article |
| id | doaj.art-40f99243f85f46e48d9ce2adf2999329 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-13T19:04:38Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-40f99243f85f46e48d9ce2adf29993292022-12-22T02:34:01ZengIEEEIEEE Access2169-35362022-01-0110756377565610.1109/ACCESS.2022.31911159830729Developer’s Roadmap to Design Software Vulnerability Detection Model Using Different AI ApproachesPooja S0https://orcid.org/0000-0002-2635-7695Chandrakala C. B.1https://orcid.org/0000-0003-3818-0679Laiju K. Raju2https://orcid.org/0000-0002-7688-8320Department of Information & Communication Technology, Manipal Academy of Higher Education, Manipal Institute of Technology, Manipal, Karnataka, IndiaDepartment of Information & Communication Technology, Manipal Academy of Higher Education, Manipal Institute of Technology, Manipal, Karnataka, Indiadltledgers India Private Limited, Thiruvananthapuram, Kerala, IndiaAutomatic software vulnerability detection has caught the eyes of researchers as because software vulnerabilities are exploited vehemently causing major cyber-attacks. Thus, designing a vulnerability detector is an inevitable approach to eliminate vulnerabilities. With the advances of Natural language processing in the application of interpreting source code as text, AI approaches based on Machine Learning, Deep Learning and Graph Neural Network have impactful research works. The key requirement for developing an AI based vulnerability detector model from a developer perspective is to identify which AI model to adopt, availability of labelled dataset, how to represent essential feature and tokenizing the extracted feature vectors, specification of vulnerability coverage with detection granularity. Most of the literature review work explores AI approaches based on either Machine Learning or Deep Learning model. The existing literature work either highlight only feature representation technique or identifying granularity level and dataset. A qualitative comparative analysis on ML, DL, GNN based model is presented in this work to get a complete picture on VDM thus addressing the challenges of a researcher to choose suitable architecture, feature representation and processing required for designing a VDM. This work focuses on putting together all the essential bits required for designing an automated software vulnerability detection model using any various AI approaches.https://ieeexplore.ieee.org/document/9830729/Machine learningdeep learninggraph neural networkfeature representationtokenizationgranularity |
| spellingShingle | Pooja S Chandrakala C. B. Laiju K. Raju Developer’s Roadmap to Design Software Vulnerability Detection Model Using Different AI Approaches IEEE Access Machine learning deep learning graph neural network feature representation tokenization granularity |
| title | Developer’s Roadmap to Design Software Vulnerability Detection Model Using Different AI Approaches |
| title_full | Developer’s Roadmap to Design Software Vulnerability Detection Model Using Different AI Approaches |
| title_fullStr | Developer’s Roadmap to Design Software Vulnerability Detection Model Using Different AI Approaches |
| title_full_unstemmed | Developer’s Roadmap to Design Software Vulnerability Detection Model Using Different AI Approaches |
| title_short | Developer’s Roadmap to Design Software Vulnerability Detection Model Using Different AI Approaches |
| title_sort | developer x2019 s roadmap to design software vulnerability detection model using different ai approaches |
| topic | Machine learning deep learning graph neural network feature representation tokenization granularity |
| url | https://ieeexplore.ieee.org/document/9830729/ |
| work_keys_str_mv | AT poojas developerx2019sroadmaptodesignsoftwarevulnerabilitydetectionmodelusingdifferentaiapproaches AT chandrakalacb developerx2019sroadmaptodesignsoftwarevulnerabilitydetectionmodelusingdifferentaiapproaches AT laijukraju developerx2019sroadmaptodesignsoftwarevulnerabilitydetectionmodelusingdifferentaiapproaches |