Shigeaki Harada

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We investigate how to detect network anomalies using flow statistics obtained through packet sampling. First, we show that network anomalies generating a huge number of small flows, such as network scans or SYN flooding, become difficult to detect when we execute packet sampling. This is because such flows are more unlikely to be sampled than normal flows.(More)
—As a promising solution to manage the huge work-load of large-scale VoD services, managed peer-assisted CDN systems, such as P4P [25] has attracted attention. Although the approach works well in theory or in a controlled environment, to our best knowledge, there have been no general studies that address how actual peers can be incentivized in the wild(More)
—We present a method of detecting network anomalies , such as DDoS (distributed denial of service) attacks and flash crowds, automatically in real time. We evaluated this method using measured traffic data and found that it successfully differentiated suspicious traffic. In this paper, we focus on cyclic traffic, which has a daily and/or weekly cycle, and(More)
We consider the online auction problem in which an auctioneer is selling an identical item each time when a new bidder arrives. It is known that results from online prediction can be applied and achieve a constant competitive ratio with respect to the best fixed price profit. These algorithms work on a predetermined set of price levels. We take into account(More)
Packet sampling has become a practical and indispensable means to measure flow statistics. Recent studies have demonstrated that analyzing traffic patterns is crucial in detecting network anomalies. We may not be able to infer the original traffic patterns correctly from the sampled flow statistics because sampling process wipes out a lot of information(More)