Industrial IoT Fog Node Adaptation in Complex Network Environment

2019 ◽  
Author(s):  
Shuaitian Wang ◽  
Yuxi Liu ◽  
Yanling Zhang ◽  
Linghe Kong ◽  
Guihai Chen
Keyword(s):  
Complex Network ◽  
Network Environment ◽  
Industrial Iot

The Study and Design of a UDP Flow Measurement System

2013 ◽  
Vol 278-280 ◽  
pp. 946-949
Author(s):  
Hai Feng Guo
Keyword(s):  
Complex Network ◽  
Measurement System ◽  
Flow Measurement ◽  
Data Flow ◽  
Comparison Function ◽  
Data Sets ◽  
Network Environment ◽  
Network Bandwidth ◽  
Packet Capture ◽  
The One

Proposed a way to UPD flow and UPD system ideology. The system is considered the one-way characteristics of UDP flow in the backbone of the network, used the WinPcap packet capture technology. The system including network packet captures module, packet replay module, packets spell flow module, UDP analysis module, while using the map template classes in stl, improved the performance of UDP packets through a comparison function with efficient custom;Contrast to the data characteristics under the complex network environment, the system adopts the step-by-step small tools design way to facilitate the system to expand new analysis function. Through the three sets of data : a backbone data sets and two DARPA1999 data sets, it can be seen that the overall development of UDP data flow is expanding the network bandwidth , and small UDP flows is more.The quicker network bandwidth development, the shorter the UDP flows average time.

The Research and Simulation on Optimization Method for Diverse Network Intrusions

2014 ◽  
Vol 556-562 ◽  
pp. 5740-5743
Author(s):  
Feng Kang ◽  
Xiao Ping Zeng ◽  
Xiao Hui Jiang
Keyword(s):  
Complex Network ◽  
Error Estimation ◽  
Detection Method ◽  
Optimization Method ◽  
Experimental Results ◽  
Classification Methods ◽  
Network Environment ◽  
Estimation Model ◽  
Network Intrusions

In current complex network environment, various kinds of attacks are mixed together forming the attack group. The diversity of the attack group leads to diverse attack signatures, which cannot be constrained by uniform conditions. Based on mixed attack signatures estimation model, the paper proposes a detection method for mixed and diverse attack groups. The paper classifies the different attacks in the mixed and diverse attack groups by use of attack constraint classification methods, builds invasion recognition particle tree, and detects mixed diverse attack groups in complex network environment according to error estimation. Experimental results show that the algorithm can effectively improve the accuracy of detection in complex network environment. atures;

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