Research on Deep Learning and Other Methods Based on Abnormal Traffic Detection in Complex Network Environment

2020 ◽  
Author(s):  
Guanglu Wei
Keyword(s):  
Deep Learning ◽  
Complex Network ◽  
Network Environment ◽  
Traffic Detection

Deep learning and complex network theory based analysis on socialized manufacturing resources utilisations and an application case study

2021 ◽  
pp. 1063293X2110031
Author(s):  
Maolin Yang ◽  
Auwal H Abubakar ◽  
Pingyu Jiang
Keyword(s):  
Deep Learning ◽  
Complex Network ◽  
Network Models ◽  
Neural Network Models ◽  
Complex Network Theory ◽  
New Type ◽  
Manufacturing Resource ◽  
Manufacturing Resources ◽  
Resource Characteristics

Social manufacturing is characterized by its capability of utilizing socialized manufacturing resources to achieve value adding. Recently, a new type of social manufacturing pattern emerges and shows potential for core factories to improve their limited manufacturing capabilities by utilizing the resources from outside socialized manufacturing resource communities. However, the core factories need to analyze the resource characteristics of the socialized resource communities before making operation plans, and this is challenging due to the unaffiliated and self-driven characteristics of the resource providers in socialized resource communities. In this paper, a deep learning and complex network based approach is established to address this challenge by using socialized designer community for demonstration. Firstly, convolutional neural network models are trained to identify the design resource characteristics of each socialized designer in designer community according to the interaction texts posted by the socialized designer on internet platforms. During the process, an iterative dataset labelling method is established to reduce the time cost for training set labelling. Secondly, complex networks are used to model the design resource characteristics of the community according to the resource characteristics of all the socialized designers in the community. Two real communities from RepRap 3D printer project are used as case study.

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.

HALNet: A Hybrid Deep Learning Model for Encrypted C&C Malware Traffic Detection

2021 ◽  
pp. 326-339
Author(s):  
Ruiyuan Li ◽  
Zehui Song ◽  
Wei Xie ◽  
Chengwei Zhang ◽  
Guohui Zhong ◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Model ◽  
Traffic Detection ◽  
Deep Learning Model
Sign in / Sign up

Export Citation Format

Share Document