WOA-XGboost classifier to detect XSS attacks

As the internet grows, online services increase year by year, so people also use web applications to make life easier. Almost all companies and organizations use the latest technology and web applications to provide services like online business, banking transactions, and social media. It makes our life advances to word technology, but it also has some backing, such as our stolen information. The cross-site attack is most frequent in web security flaws. It is also mentioned in OWSP as the top ten. This paper proposed WOA-XGboost, a web XSS attack detection framework. Whale Optimization Algorithm (WOA) is one of the nature-inspired optimization algorithms that repeat humpback whales’ community actions, pick the feature selection subset, and apply an XG boost classifier to detect the attack. We also cover the existing research gap with a high prediction rate. The proposed methods achieve recall,Accuracy, False Positive rate, False Positive rate, Precision, F-measure, Entropy as 98.2%, 99.8%,0.00039,096%,97.1%,0.0391 respectively.