Abstract: Mining terrorist data and analyzing user’s profile category can be a key for different types of applications. In specific, many applications under anti-terrorism, cyber security and web security need complete user behavior and their Wi-Fi access history to improve the security by detecting abnormal web behavior. In data mining there are numerous studies with the above intentions are made a successful outcome. Classifying users profile and finding their profile category is a major task. This can be done using several data mining algorithm because the access log size is huge in size and contains more auxiliary information. Every user’s access behavior differs from one location to another location. So finding the users based on the Wi-Fi access history is important. This survey brings the overall summary of data mining techniques and tools used to analyze and classify user profiles. This helps to know the user type like abnormal, normal, abnormal-terrorist and genuine user. And this survey gives cons of existing work and this may give a new idea to empower the user profile classification and analysis framework to detect terrorist or other kinds of users.

Keywords: Data Mining, Weblog Mining, Wi-Fi Log Mining, Normal Users (NU), Abnormal Genuine Users (AGU), Abnormal Terrorist User (ATU), Access Point (AP)