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Effective Pattern Identification Model for DDoS Attack Detection

Abstract:

Distributed Denial of Service (DDoS) attacks are one of the major challenges to Internet community. Attackers send legitimate packets with often changing information from various compromised systems at random and at a very high frequency, rendering the target non-responsive for normal traffic. DDoS attacks are difficult to detect with traditional detection methods and standard Intrusion Detection Systems (IDS). Standard IDS tries to analyze the network traffic or system logs trying to identify emerging patterns on the network traffic. But due to randomness of the package origins it is difficult segregate true, false positive and normal traffic. This paper proposes a model based on Artificial Neural Networks to identify anomalies and detect DDoS patterns. In the proposed system sets of known characteristic features, which can separate attacks from normal traffic, are fed to the system to train the Artificial Neural Networks (ANN). This self learn system improves with each new attack as the false positives decrease and detection accuracy improves.

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