A Data-Mining Based DoS Detection Technique
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Denial of Service(DoS) is a type of frequent network attack which can severely impact the availability of networks and services.DoS usually utilizes packetattribute spoof techniques to confuse present IDSs such as snort.Typically,the spoof techniques minimize effective and automatic DoS attacks detection.A novel technique based on data mining to detect DoS attacks in real-time called DMDoSD is presented.First,the Apriori association algorithm extracts traffic patterns from empirical network data and subsequently the K-means cluster algorithm adaptively generates a detection model.By combining these two algorithms,DoS attacks can be detected swiftly,automatically and effectively as they arise.In addition to the alerts typically sent out by IDSs,DMDoSD also determines signatures of malicious packets automatically to help to react to DoS attacks.