We propose an anomaly detection method for finding patterns in network traffic that do not conform to legitimate (i.e., normal) behavior. The proposed method trains a baseline model describing the normal behavior of network traffic without using manually labeled traffic data. The trained baseline model is used as the basis for comparison with the audit network traffic. This anomaly detection works in an unsupervised manner through the use of time-periodic packet sampling, which is used in a manner that differs from its intended purpose-the lossy nature of packet sampling is used to extract normal packets from the unlabeled original traffic data. Evaluation using actual traffic traces showed that the proposed method has false positive and false negative rates in the detection of anomalies regarding TCP SYN packets comparable to those of a conventional method that uses manually labeled traffic data to train the baseline model. Performance variation due to the probabilistic nature of sampled traffic data is mitigated by using ensemble anomaly detection that collectively exploits multiple baseline models in parallel. Alarm sensitivity is adjusted for the intended use by using maximum-and minimum-based anomaly detection that effectively take advantage of the performance variations among the multiple baseline models. Testing using actual traffic traces showed that the proposed anomaly detection method performs as well as one using manually labeled traffic data and better than one using randomly sampled (unlabeled) traffic data.

We propose an anomaly detection method that trains a baseline model describing the normal behavior of network traffic without using manually labeled traffic data. The trained baseline model is used as the basis for comparison with the audit network traffic. The proposed method can be carried out in an unsupervised manner through the use of time-periodical packet sampling for a different purpose from which it was intended. That is, we take advantage of the lossy nature of packet sampling for the purpose of extracting normal packets from the unlabeled original traffic data. By using real traffic traces, we show that the proposed method is comparable in terms of false positive and false negative rates on detecting anomalies regarding TCP SYN packets to the conventional method that requires manually labeled traffic data to train the baseline model. In addition, in order to mitigate the possible performance variation due to probabilistic nature of sampled traffic data, we devise an ensemble anomaly detection method that exploits multiple baseline models in parallel. Experimental results show that the proposed ensemble anomaly detection performs well and is not affected by the variability of time-periodical packet sampling.

It is important for network operators to know how users use the network to plan and design network facilities. In this paper, we propose flow classification method using binned time-series data in consideration of practicality. In this paper, we evaluate the proposed method by using mobile data traffic and show the method can maintain classification accuracy as compared with method using the whole flow information. We also confirm applicability to classification of encrypted flow.