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Effective Pattern Identification Model for DDoS Attack Detection


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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Distinguished Architects  Randy Arthur (Americas) serves as product owner for CSC’s IaaS offerings and as a lead solutions architect for complex integration projects involving cloud computing technologies. During his 16-year career with CSC, Randy  has worked successfully in various roles including midrange service delivery, pre-sales solution development and product management. He was the first CTO of CSC’s Cloud  technology “incubator.” Bio on | LinkedIn| Twitter
Graham Chastney(UKI&N) is a global domain architect experienced in workplace technologies,  solution strategy and solution governance. He is a global collaborator who is relied upon to provide thought leadership to solution teams and to build and development teams. Graham is the founder  and lead author of the Technology Perspectives blog, which he regards as part of a broader ambition to …