Identifying XSS Attack using Combined Machine Learning Classifier

In recent years the number of cyber-attacks is being increased rapidly. There are many unique cyber-attacks emerging day by day that require more attention. One such attack is cross-site scripting (XSS). This attack can be caused by inserting the malicious code on a web page to interact with the attacker’s web server. The attacker can get much useful information about the victim and it is necessary to identify and prevent this type of attack. There are many methods available to detect XSS attacks like using n-grams, machine learning methods, intrusion detection systems (IDS), etc. In this paper, we used Support vector machine (SVM), K-nearest neighbour (KNN), discriminant classifier and proposed a combined classifier that best identifies the attack.