Data augmentation-based conditional Wasserstein generative adversarial network-gradient penalty for XSS attack detection system.

ABSTRACT: The rapid growth of the worldwide web and accompanied opportunities of web applications in various aspects of life have attracted the attention of organizations, governments, and individuals. Consequently, web applications have increasingly become the target of cyberattacks. Notably, cross-site scripting (XSS) attacks on web applications are increasing and have become the critical focus of information security experts' reports. Machine learning (ML) technique has significantly advanced and shown impressive results in the area of cybersecurity. However, XSS training datasets are often limited and significantly unbalanced, which does not meet well-developed ML algorithms' requirements and potentially limits the detection system efficiency. Furthermore, XSS attacks have multiple payload vecto

SUBMITTER: Mokbal FMM 

PROVIDER: S-EPMC7924509 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature