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Detection and Prediction of Cyber Crimes in Financial Sector by Applying Data Mining Techniques


M. Ganesan and P. Mayilvahanan
Abstract

Cyber crimes are the felonies that are committed against individuals or groups of individuals with a criminal motivation to harm or create physical or mental harm, or loss, to the victim directly or indirectly, using modern telecommunication networks such as Internet and mobile devices to spread malware, illegal information, images or other materials. Some cybercrimes involving target computers to infect them with viruses, malwares, which are then spread to other machines and, entire networks. Data mining applications are utilized in many financial sectors for client segmentation, authentication, credit scores and authorization, predicting payment fault, advertising, detecting and predicting fake transactions, etc. The aim of cyber crime data mining is to recognize patterns in criminal manners in order to predict crime anticipate criminal activity and prevent it. Globally the internet is been accessed by numerous people within their restricted domains. When the client and server exchange information among each other, there is an activity that can be observed in log files. Log files give a entire description of the activities that occur in a network that shows the IP address, login and logout durations, the user behaviour etc. This involves several attacks occurring from the internet. Denial of service is a very dangerous attack that jeopardizes the IT resources of an organization or institution by overloading with imitation messages or multiple requests from unauthorized and unauthenticated users.

Volume 11 | 03-Special Issue

Pages: 1114-1118