This paper presents a detection algorithm for anomaly network traffic, which is based on spectral kurtosis analysis. Firstly, we turn network traffic into time-frequency signals at different scales. These time-frequency signals hold the more detailed nature corresponding to different scales. Secondly, the time-frequency signals at different scales are transformed into a series of new time signals by time-frequency analysis theory. These new time signals hold obvious narrowband nature and embody the local properties of network traffic. Thirdly, we calculate the spectral kurtosis values of the new time signals and then perform the feature extractions. As a result, the abnormal network traffic can be correctly identified. Simulation results show that our algorithm is feasible and promising.

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In this paper, an energy-efficient multicast routing algorithm in multi-hop wireless networks is proposed aiming at new generation wireless communications. Different from the previous methods, this paper targets maximizing the energy efficiency of networks. In order to get the optimal energy efficiency to build the network multicast route, our proposed method tries to maximize the network throughput and minimize the network energy consumption by exploiting network coding and sleeping scheme. Simulation results show that the proposed algorithm has better energy efficiency and performance improvements comparing with the existing methods.

Anomalous traffic often has a significant impact on network activities and lead to the severe damage to our networks because they usually are involved with network faults and network attacks. How to detect effectively network traffic anomalies is a challenge for network operators and researchers. This paper proposes a novel method for detecting traffic anomalies in a network, based on continuous wavelet transform. Firstly, continuous wavelet transforms are performed for network traffic in several scales. We then use multi-scale analysis theory to extract traffic characteristics. And these characteristics in different scales are further analyzed and an appropriate detection threshold can be obtained. Consequently, we can make the exact anomaly detection. Simulation results show that our approach is effective and feasible.

This paper proposes an energy-efficient model to overcome the energy-efficient problem in large-scale IP networks, based on QoS constraints. To characterize network energy consumption, we present a link energy consumption model based on the sleep and speed scaling mechanisms. If there is no traffic on a link, let it sleep, or activate it and divide its energy consumption into base energy consumption and traffic energy consumption. And then according to the link energy consumption model, we can build our energy-efficient model to improve the network energy efficiency. Finally, simulation results show that our model can significantly improve the network energy efficiency.