It is possible to better provide the security of the codebase and keep testing efforts at a minimum level by detecting vulnerable codes early in the course of software development. We assume that nature-inspired metaheuristic optimization algorithms can obtain “optimized patterns” from vulnerabilities created in an artificial manner. This study aims to use nature-inspired optimization algorithms combining heterogeneous data sources with the objective of learning optimized representations of vulnerable source codes. The chosen vulnerability-relevant data sources are cross-domain, involving historical vulnerability data from variable software projects and data from the Software Assurance Reference Database (SARD) comprising vulnerability examples. The main purpose of this paper is to outline the state-of-the-art and to analyze and discuss open challenges with regard to the most relevant areas in the field of bio-inspired optimization based on the representation of software vulnerability. Empirical research has demonstrated that the optimized representations produced by the suggested nature-inspired optimization algorithms are feasible and efficient and can be transferred for real-world vulnerability detection.
Feature Selection Nature-inspired Algorithm Optimization Representation learning Software Vulnerability.
It is possible to better provide the security of the codebase and keep testing efforts at a minimum level by detecting vulnerable codes early in the course of software development. We assume that nature-inspired metaheuristic optimization algorithms can obtain “optimized patterns” from vulnerabilities created in an artificial manner. This study aims to use nature-inspired optimization algorithms combining heterogeneous data sources with the objective of learning optimized representations of vulnerable source codes. The chosen vulnerability-relevant data sources are cross-domain, involving historical vulnerability data from variable software projects and data from the Software Assurance Reference Database (SARD) comprising vulnerability examples. The main purpose of this paper is to outline the state-of-the-art and to analyze and discuss open challenges with regard to the most relevant areas in the field of bio-inspired optimization based on the representation of software vulnerability. Empirical research has demonstrated that the optimized representations produced by the suggested nature-inspired optimization algorithms are feasible and efficient and can be transferred for real-world vulnerability detection.
Feature Selection Nature-inspired Algorithm Optimization Representation learning Software Vulnerability. Feature Selection, Nature-inspired Algorithm, Optimization, Representation learning, Software Vulnerability.
Birincil Dil | İngilizce |
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Konular | Mühendislik |
Bölüm | Makaleler |
Yazarlar | |
Erken Görünüm Tarihi | 2 Ekim 2022 |
Yayımlanma Tarihi | 30 Kasım 2022 |
Yayımlandığı Sayı | Yıl 2022 Sayı: 41 |