Detecting Security-Relevant Methods using Multi-label Machine Learning
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arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Acceso en línea: | |
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| _version_ | 1866916156938387456 |
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| author | Johnson, Oshando Piskachev, Goran Krishnamurthy, Ranjith Bodden, Eric |
| author_facet | Johnson, Oshando Piskachev, Goran Krishnamurthy, Ranjith Bodden, Eric |
| contents | To detect security vulnerabilities, static analysis tools need to be configured with security-relevant methods. Current approaches can automatically identify such methods using binary relevance machine learning approaches. However, they ignore dependencies among security-relevant methods, over-generalize and perform poorly in practice. Additionally, users have to nevertheless manually configure static analysis tools using the detected methods. Based on feedback from users and our observations, the excessive manual steps can often be tedious, error-prone and counter-intuitive.
In this paper, we present Dev-Assist, an IntelliJ IDEA plugin that detects security-relevant methods using a multi-label machine learning approach that considers dependencies among labels. The plugin can automatically generate configurations for static analysis tools, run the static analysis, and show the results in IntelliJ IDEA. Our experiments reveal that Dev-Assist's machine learning approach has a higher F1-Measure than related approaches. Moreover, the plugin reduces and simplifies the manual effort required when configuring and using static analysis tools. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_07501 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Detecting Security-Relevant Methods using Multi-label Machine Learning Johnson, Oshando Piskachev, Goran Krishnamurthy, Ranjith Bodden, Eric Machine Learning To detect security vulnerabilities, static analysis tools need to be configured with security-relevant methods. Current approaches can automatically identify such methods using binary relevance machine learning approaches. However, they ignore dependencies among security-relevant methods, over-generalize and perform poorly in practice. Additionally, users have to nevertheless manually configure static analysis tools using the detected methods. Based on feedback from users and our observations, the excessive manual steps can often be tedious, error-prone and counter-intuitive. In this paper, we present Dev-Assist, an IntelliJ IDEA plugin that detects security-relevant methods using a multi-label machine learning approach that considers dependencies among labels. The plugin can automatically generate configurations for static analysis tools, run the static analysis, and show the results in IntelliJ IDEA. Our experiments reveal that Dev-Assist's machine learning approach has a higher F1-Measure than related approaches. Moreover, the plugin reduces and simplifies the manual effort required when configuring and using static analysis tools. |
| title | Detecting Security-Relevant Methods using Multi-label Machine Learning |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2403.07501 |