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manually. To deal with unlabeled packets, first, the detection system would learns general rules of DDoS backscatter using information from 80/TCP and 53/UDP. After the learning process, the generalized detection system is used to detect the DDoS backscatter from unlabeled packets. This detection system consists of two main modules which are pre-processing and classifier. In the pre-processing module, the incoming packets are transformed into feature vectors. As for the classifier module, since it is important to detect DDoS backscatter from all protocols as early as possible, we use Resource Allocating Network (RAN) with data selection. Using this classifier, the learning time is shortened because the classifier only learns essential data. Here, essential data means the data located in “well learned” regions, in which the classifier gives trustable predictions. To quickly search for the regions closest to given data, the well-known Locality Sensitive Hashing (LSH) method is used. The performance of the proposed detection system is evaluated using 9,968 training data from labeled packets and 5,933 test data from unlabeled packets. They are collected from January 1st, 2013 until January 20th, 2014 at National Institute of Information and Communications Technology (NICT), Japan. The results indicate that the detection system can detects the DDoS backscatter with high detection rate within a short time.

Distributed Denial of Service (DDoS) Backscatter Detection System Using Resource Allocating Network with Data Selection