Abstract: Clickjacking attack is an emerging threat on the web. Although, tools are available for identifying clickjack attacks on web pages, complete information on attacks are not available and more over the procedure adopted by the tool to identify and handle attacks is not made visible for users. Hence, arises the need for developing an application which can aid users in collecting dataset, allow users to modify/streng then, the procedures for identifying and preventing clickjacking attack. In clickjacking attack the attacker presents a sensitive user interface to the user by making the user interface transparent and thus, the user is tricked to perform an action which is out of context. So, to mitigate the clickjacking attack a schema is introduced that consist of two phases: signature detection and in context defence. Signature detection approach determines whether the attack is detected by comparing them against a database of signatures or their pattern from the known malicious sites. By using a scoring mechanism signature detection is done. This method uses static features to identify potential malicious pages. Scoring algorithm works based on the concept of standard score which measure how standard deviation of the observed attribute is away from the mean. Using each instance, two types of scores are calculated, Foreign content score and the script content score and based on the score the web pages are classified as malicious or not. A threshold value is chosen and the group score greater than the threshold value is considered as a malicious page. In context checks whether an attack has occurred by comparing the referred bitmap and the screenshot of the current browser. Thus, a hybrid schema is introduced to mitigate the clickjacking attack. The focus of the researcher is to create an attack dataset that could be used to train the system to prevent the clickjacking attack.