Automated Software Vulnerability Testing Using In-Depth Training Methods

Кuznetsov, Alexandr; Shapoval, Oleksiy; Chernov, Kyrylo; Yeromin, Yehor; Popova, M.V.; Syniavska, Olga

doi:10.32782/cmis/2353-18

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2019

2024

Publication Types

Select...

Article1

Other1

Relationship

Self Cite0

Independent2

Authors

Journals

Cited by 2 publications

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Artificial intelligence for system security assurance: A systematic literature review

Wen,

Shukla,

Katt

2024

Int. J. Inf. Secur.

View full text Add to dashboard Cite

System Security Assurance (SSA) has emerged as a critical methodology for organizations to verify the trustworthiness of their systems by evaluating security measures against industry standards, legal requirements, and best practices to identify any weakness and demonstrate compliance. In recent years, the role of Artificial Intelligence (AI) in enhancing cybersecurity has received increased attention, with an increasing number of literature reviews highlighting its diverse applications. However, there remains a significant gap in comprehensive reviews that specifically address the integration of AI within SSA frameworks. This systematic literature review seeks to fill this research gap by assessing the current state of AI in SSA, identifying key areas where AI contributes to improve SSA processes, highlighting the limitations of current methodologies, and providing the guidance for future advancements in the field of AI-driven SSA.

show abstract