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　vectors　that　execute　in　different　ways,　resulting　in　many　real　threats　passing　through　the　detection　system　undetected.　In　this　study,　we　propose　a　conditional　Wasserstein　generative　adversarial　network　with　a　gradient　penalty　to　enhance　the　XSS　detection　system　in　a　low-resource　data　environment.　The　proposed　method　integrates　a　conditional　generative　adversarial　network　and　Wasserstein　generative　adversarial　network　with　a　gradient　penalty　to　obtain　necessary　data　from　directivity,　which　improves　the　strength　of　the　security　system　over　unbalance　data.　The　proposed　method　generates　synthetic　samples　of　minority　class　that　have　identical　distribution　as　real　XSS　attack　scenarios.　The　augmented　data　were　used　to　train　a　new　boosting　model　and　subsequently　evaluated　the　model　using　a　real　test　dataset.　Experiments　on　two　unbalanced　XSS　attack　datasets　demonstrate　that　the　proposed　model　generates　valid　and　reliable　samples.　Furthermore,　the　samples　were　indistinguishable　from　real　XSS　data　and　significantly　enhanced　the　detection　of　XSS　attacks　compared　with　state-of-the-art　methods.