Learning Optimized Patterns of Software Vulnerabilities with the Clock-Work Memory Mechanism

Authors : Canan BATUR ŞAHİN

Pages : 156-165

Doi:10.31590/ejosat.1159875

View : 10 | Download : 18

Publication Date : 2022-11-30

Article Type : Research Paper

Abstract :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 insert ignore into journalissuearticles values(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.
Keywords : Feature Selection, Nature inspired Algorithm, Optimization, Representation learning, Software Vulnerability