A cloud platform for flow-based analysis of large-scale network traffic

2013 ◽  
Author(s):  
Wenzhao Liao ◽  
Zhiren Fu
Keyword(s):  
Network Traffic ◽  
Large Scale ◽  
Cloud Platform ◽  
Large Scale Network ◽  
Scale Network

Research of the Method for Traffic Generating Based on Genetic Algorithm

2014 ◽  
Vol 701-702 ◽  
pp. 3-7
Author(s):  
Liu Bo
Keyword(s):  
Genetic Algorithm ◽  
Network Traffic ◽  
Large Scale ◽  
Network Reliability ◽  
Actual Usage ◽  
Large Scale Network ◽  
Scale Network ◽  
Self Similar ◽  
Optimal Values

It has great impact on result of the network test or simulation if the test simulated traffic is corresponding to real situation. The network traffic is the superposition of different traffic streams in the actual usage of the network. But because of the complexity and time-consumption to generate different traffic streams, it is difficult to generate the network traffic in the simulation for the large scale network. This paper proposes a kind of method for traffic generating based on genetic algorithm .According to building the self-similar traffic model ,the optimal values of the model’s parameters has been obtained. A case study shows the effectiveness of the method for the network reliability.

scholarly journals A Large-Scale Network Data Analysis via Sparse and Low Rank Reconstruction

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Liang Fu Lu ◽  
Zheng-Hai Huang ◽  
Mohammed A. Ambusaidi ◽  
Kui-Xiang Gou
Keyword(s):  
Network Traffic ◽  
Large Scale ◽  
Low Rank ◽  
Prototype System ◽  
Traffic Data ◽  
Proximal Gradient Method ◽  
Large Scale Network ◽  
Network Operation ◽  
Efficient Data ◽  
Scale Network

With the rapid growth of data communications in size and complexity, the threat of malicious activities and computer crimes has increased accordingly as well. Thus, investigating efficient data processing techniques for network operation and management over large-scale network traffic is highly required. Some mathematical approaches on flow-level traffic data have been proposed due to the importance of analyzing the structure and situation of the network. Different from the state-of-the-art studies, we first propose a new decomposition model based on accelerated proximal gradient method for packet-level traffic data. In addition, we present the iterative scheme of the algorithm for network anomaly detection problem, which is termed as NAD-APG. Based on the approach, we carry out the intrusion detection for packet-level network traffic data no matter whether it is polluted by noise or not. Finally, we design a prototype system for network anomalies detection such as Probe and R2L attacks. The experiments have shown that our approach is effective in revealing the patterns of network traffic data and detecting attacks from large-scale network traffic. Moreover, the experiments have demonstrated the robustness of the algorithm as well even when the network traffic is polluted by the large volume anomalies and noise.

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