Analyzing Online Un-authorize Activity (Cyber Crime) Using Data Mining

Analyzing Online Un-authorize Activity (Cyber Crime) Using Data Mining
Author(s):
Suraj Bharat Kokne, Ashwin Gautam Sonone, Ankush Ramrao Ade, Bhagyashri Pradeep Kedari, Prof. A. A. Bamanikar
Keywords:
Denial of Service, Log File, Cyber Crimes, Data mining, outliers, Association rules
Abstract
It is very important to maintain security of data and protect it from unauthorized or illegal access. Many intruders try to attack the systems by using different types of cyber attacks like Denial of Service (DoS), Brute force attack, Man-In-the-Middle attack etc. Our focus of research in this paper is Denial of Service (DoS) attacks with the help of pattern recognition techniques in data mining. Through which the Denial of Service attack is identified. When the client and server exchange messages among each other, there is an activity that can be observed in log files. Log files give a detailed description of the activities that occur in a network that shows the IP address, login and logout durations, the user’s behavior etc. In denial of service attack, IT resources of any organization are overloaded with imitation messages or multiple requests from unauthorized users. This system will try to apply pattern recognition technique on the data of user’s or organization’s log file.
Article Details
Unique Paper ID: 146352 Publication Volume & Issue: Volume 4, Issue 12 Page(s): 328 - 331
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