Detecting Botnet Using Data Mining

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Executive Summary

As with the increase in the number of spywares, malwares over Internet and Distributed Networks, Botnets are emerging as the most serious threat against Cyber security in one way or another as they act as basis for various Illegal activities like Denial of Service Attacks (DoS Attacks), Malwares, Phishing and Online Fraud. Detecting Botnet is divided into four classes: Signature-Based, Anomaly-Based, DNS-Based & Mining Based. The main aim of this paper is to study Minnesota Intrusion Detection System, which uses combination of various Data Mining Algorithms to defend the Network and acts as Guard for various attacks from outside world.

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