In this paper, we propose a traffic feature-based botnet detection scheme emphasizing the importance of long patterns. Since the communication sequences of bots are not easily changed, the long communication patterns of botnets are useful for detection. The proposed scheme emphasizes the long pattern's importance by improving the feature extraction algorithms and giving weights to the long patterns with large occurrences. By the computer simulation with real dataset, we show the effectiveness of our scheme.
A shared concentration output queueing (SCOQ) switch with middle buffers at the input of a batcher-banyan network is proposed in order to decrease the packet loss probability due to overflow. The proposed model can avoid packet loss due to blocking in the batcher-banyan network and decrease packet loss probability due to overflow. It uses a middle buffer at the input of the batcher-banyan network in the switching module. The proposed switch consists of N/2 output ports of each switching module. The switching module consists of a filter, a batcher-banyan network, middle buffers and output buffers. Switching operation is evaluated with respect to packet loss probability and mean waiting time by numerical calculation and computer simulation. We show that our proposed switch is able to decrease packet loss probability under uniform traffic conditions.
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