Content Recognition of Network Traffic Using Wavelet Transform and CNN

2019 ◽  
pp. 224-238
Author(s):  
Yu Liang ◽  
Yi Xie ◽  
Xingrui Fei ◽  
Xincheng Tan ◽  
Haishou Ma
Keyword(s):  
Wavelet Transform ◽  
Network Traffic ◽  
Content Recognition

Network traffic forecasting combination model based on wavelet transform and chaos algorithm

2014 ◽  
Vol 12 (03) ◽  
pp. 1450029 ◽  
Author(s):  
Xiao Chao Dang ◽  
Zhan Jun Hao ◽  
Yan Li ◽  
Zhen Yu Lu ◽  
Qi Gao
Keyword(s):  
Neural Network ◽  
Wavelet Transform ◽  
Network Traffic ◽  
Local Optimum ◽  
Elman Neural Network ◽  
Bp Network ◽  
Combination Model ◽  
Traffic Forecasting ◽  
Gradient Descent Algorithm ◽  
Chaos Algorithm

Based on wavelet transform and chaos algorithm, this paper presents a Network Traffic Forecasting Combination Model. The model introduces chaos algorithm for training the BP network and optimizing weights so as to avoid gradient descent algorithm that slowly converges and likely obtains local optimum results. Before forecasting, we first perform wavelet decomposition on the pretreated flow. Then, we utilize the FARIMA model and the improved Elman neural network model to forecast according to approximate components and detailed components, respectively. At last, we use the combination model for the network traffic forecasting. Simulation results confirmed the improved accuracy of the model, and comparing to traditional FARIMA model and wavelet neural network (WNN) model, the model can reduce the deviation.

An Continuous Wavelet Transform-Based Detection Approach to Traffic Anomalies

2011 ◽  
Vol 130-134 ◽  
pp. 2098-2102
Author(s):  
Ding De Jiang ◽  
Cheng Yao ◽  
Zheng Zheng Xu ◽  
Peng Zhang ◽  
Zhen Yuan ◽  
...  
Keyword(s):  
Wavelet Transform ◽  
Continuous Wavelet Transform ◽  
Network Traffic ◽  
Wavelet Transforms ◽  
Detection Threshold ◽  
Continuous Wavelet ◽  
Continuous Wavelet Transforms ◽  
Traffic Characteristics ◽  
Analysis Theory ◽  
Detection Approach

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.

Robust estimation of the self-similarity parameter in network traffic using wavelet transform

2007 ◽  
Vol 87 (9) ◽  
pp. 2111-2124 ◽  
Author(s):  
Haipeng Shen ◽  
Zhengyuan Zhu ◽  
Thomas C.M. Lee
Keyword(s):  
Wavelet Transform ◽  
Robust Estimation ◽  
Network Traffic ◽  
The Self ◽  
Self Similarity ◽  
Similarity Parameter

scholarly journals Detection of DOS-attacks in network traffic by the method of Wavelet-transformation

2020 ◽  
Author(s):  
V. I. Dubrovin ◽  
◽  
B. V. Petryk ◽  
G. V. Nelasa ◽  
◽  
...  
Keyword(s):  
Data Mining ◽  
Wavelet Transform ◽  
Data Analysis ◽  
Data Streams ◽  
Network Traffic ◽  
Wavelet Transformation ◽  
Traffic Data ◽  
Dos Attacks ◽  
Transform Method ◽  
Numerous Data

Network traffic data analysis is very important for detecting DOS attacks and malicious anomalies. Many data mining techniques have been found to manage data and use it for security purposes. Fast and accurate search for content-based queries is critical to making such numerous data streams useful. This paper proposes an analysis of the deauthentication attack and the localization of the anomaly data by the wavelet transform method.

Sign in / Sign up

Export Citation Format

Share Document