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pro vyhledávání: '"Le, Triet H. M."'
Autor:
Le, Triet H. M., Babar, M. Ali
Background: Software Vulnerability (SV) prediction needs large-sized and high-quality data to perform well. Current SV datasets mostly require expensive labeling efforts by experts (human-labeled) and thus are limited in size. Meanwhile, there are gr
Externí odkaz:
http://arxiv.org/abs/2407.17803
Autor:
Le, Triet H. M., Babar, M. Ali
Background: Software Vulnerability (SV) assessment is increasingly adopted to address the ever-increasing volume and complexity of SVs. Data-driven approaches have been widely used to automate SV assessment tasks, particularly the prediction of the C
Externí odkaz:
http://arxiv.org/abs/2407.10722
Background: Software Vulnerability (SV) prediction in emerging languages is increasingly important to ensure software security in modern systems. However, these languages usually have limited SV data for developing high-performing prediction models.
Externí odkaz:
http://arxiv.org/abs/2404.17110
Collecting relevant and high-quality data is integral to the development of effective Software Vulnerability (SV) prediction models. Most of the current SV datasets rely on SV-fixing commits to extract vulnerable functions and lines. However, none of
Externí odkaz:
http://arxiv.org/abs/2401.11105
Autor:
Le, Triet H. M.
The thesis advances the field of software security by providing knowledge and automation support for software vulnerability assessment using data-driven approaches. Software vulnerability assessment provides important and multifaceted information to
Externí odkaz:
http://arxiv.org/abs/2207.11708
Autor:
Le, Triet H. M., Babar, M. Ali
Many studies have developed Machine Learning (ML) approaches to detect Software Vulnerabilities (SVs) in functions and fine-grained code statements that cause such SVs. However, there is little work on leveraging such detection outputs for data-drive
Externí odkaz:
http://arxiv.org/abs/2203.08417
Autor:
Duan, Xuanyu, Ge, Mengmeng, Le, Triet H. M., Ullah, Faheem, Gao, Shang, Lu, Xuequan, Babar, M. Ali
Internet of Things (IoT) based applications face an increasing number of potential security risks, which need to be systematically assessed and addressed. Expert-based manual assessment of IoT security is a predominant approach, which is usually inef
Externí odkaz:
http://arxiv.org/abs/2109.04029
It is increasingly suggested to identify Software Vulnerabilities (SVs) in code commits to give early warnings about potential security risks. However, there is a lack of effort to assess vulnerability-contributing commits right after they are detect
Externí odkaz:
http://arxiv.org/abs/2108.08041
Publikováno v:
ACM Comput. Surv., 55, 5 (2022), Article 100
Software Vulnerabilities (SVs) are increasing in complexity and scale, posing great security risks to many software systems. Given the limited resources in practice, SV assessment and prioritization help practitioners devise optimal SV mitigation pla
Externí odkaz:
http://arxiv.org/abs/2107.08364
Publikováno v:
Proceedings of the 16th International Conference on Mining Software Repositories, 2019, pp. 371-382
Software Engineering researchers are increasingly using Natural Language Processing (NLP) techniques to automate Software Vulnerabilities (SVs) assessment using the descriptions in public repositories. However, the existing NLP-based approaches suffe
Externí odkaz:
http://arxiv.org/abs/2103.11316