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network traffic Based on GARCH-M Model and Extreme Value Theory

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Transactions of Tianjin University 2005年第5期11卷 386-390页

作者：沈菲王洪礼史道济李栋 School of Sciences Tianjin University School of Mechanical Engineering Tianjin University School of Management Tianjin University

GARCH-M ( generalized autoregressive conditional heteroskedasticity in the mean) model is used to analyse the volatility clustering phenomenon in mobile communication network traffic. Normal distribution, t distribution and generalized Pareto distribution assumptions are adopted re- spectively to simulate the random component in the model. The demonstration of the quantile of network traffic series indicates that common GARCH-M model can partially deal with the "fat tail" problem. However, the "fat tail" characteristic of the random component directly affects the accura- cy of the calculation. Even t distribution is based on the assumption for all the data. On the other hand, extreme value theory, which only concentrates on the tail distribution, can provide more ac- curate result for high quantiles. The best result is obtained based on the generalized Pareto distribu- tion assumption for the random component in the GARCH-M model.

关键词： network traffic GARCH-M extreme value theory generalized Pareto distribution

Small-time scale network traffic prediction based on a local support vector machine regression model

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Chinese Physics B 2009年第6期18卷 2194-2199页

作者：孟庆芳陈月辉彭玉华 School of Information Science and Engineering University of Jinan School of Information Science and Engineering Shandong University

In this paper we apply the nonlinear time series analysis method to small-time scale traffic measurement data. The prediction-based method is used to determine the embedding dimension of the traffic data. Based on the reconstructed phase space, the local support vector machine prediction method is used to predict the traffic measurement data, and the BIC-based neighbouring point selection method is used to choose the number of the nearest neighbouring points for the local support vector machine regression model. The experimental results show that the local support vector machine prediction method whose neighbouring points are optimized can effectively predict the small-time scale traffic measurement data and can reproduce the statistical features of real traffic measurements.

关键词： network traffic small-time scale nonlinear time series analysis support vector machine regression model

Chaotic Control of network traffic

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China Communications 2010年第5期7卷 112-119页

作者： Yang Tan Guo Shize Jin Yuehui Cheng Shiduan School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu 611731 P. R. China State Key Laboratory of networking and Switching Beijing University of Posts and Telecommunications Beijing 100876 P. R. China Information Security Center Beijing University of Posts and Telecommunications Beijing 100876 P. R. China

This paper presents a chaotic control method on network traffic. By this method, the chaotic network traffic can be controlled to pre-assigned equifibrium point according to chaotic prediction and the Largest Lyapunov Exponent （LLE） of the traffic on congested link is reduced, thereby the probability of traffic burst and network congestion can be reduced. Numerical examples show that this method is effective.

关键词： network traffic chaotic control Lyapunov exponent chaotic prediction

RRCNN: Request Response-Based Convolutional Neural network for ICS network traffic Anomaly Detection

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Computers, Materials & Continua 2023年第6期75卷 5743-5759页

作者： Yan Du Shibin Zhang Guogen Wan Daohua Zhou Jiazhong Lu Yuanyuan Huang Xiaoman Cheng Yi Zhang Peilin He Country School of Cybersecurity Chengdu University of Information TechnologyChengdu610225China Advanced Cryptography and System Security Key Laboratory of Sichuan Province Chengdu610225China DAQSOFT CO. LTDChengdu610213China Communication and Information Technology Center Petro China Southwest Oil and Gas CompanyChengdu610057China School of Computing and Information University of PittsburghPittsburgh152607PAUSA

Nowadays,industrial control system(ICS)has begun to integrate with the *** the Internet has brought convenience to ICS,it has also brought severe security *** ICS network traffic anomaly detection methods rely on statistical features manually extracted using the experience of network security *** are not aimed at the original network data,nor can they capture the potential characteristics of network ***,the following improvements were made in this study:(1)A dataset that can be used to evaluate anomaly detection algorithms is produced,which provides raw network data.(2)A request response-based convolutional neural network named RRCNN is proposed,which can be used for anomaly detection of ICS network *** of using statistical features manually extracted by security experts,this method uses the byte sequences of the original network packets directly,which can extract potential features of the network packets in greater *** regards the request packet and response packet in a session as a Request-Response Pair(RRP).The feature of RRP is extracted using a one-dimensional convolutional neural network,and then the RRP is judged to be normal or abnormal based on the extracted *** results demonstrate that this model is better than several other machine learning and neural network models,with F1,accuracy,precision,and recall above 99%.

关键词： Industrial control system(ICS) dataset network traffic anomaly detection

WPANFIS:combine fuzzy neural network with multiresolution for network traffic prediction

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The Journal of China Universities of Posts and Telecommunications 2010年第4期17卷 88-93页

作者： LI Rui CHEN Jian-ya LIU Yun-jie WANG Zhen-kai School of Information and Telecommunication Engineering Beijing University of Posts and Telecommunications Beijing 100876 China China Unicorn Beijing 100140 China

A novel methodology for prediction of network traffic, WPANFIS, which relies on wavelet packet transform （WPT） for multi-resolution analysis and adaptive neuro-fuzzy inference system （ANFIS） is proposed in this article. The widespread existence of self-similarity in network traffic has been demonstrated in earlier studies, which exhibits both long range dependence （LRD） and short range dependence （SRD）. Also, it has been shown that wavelet decomposition is an effective tool for LRD decorrelation. The new method uses WPT as extension of wavelet transform which can decoorrelate LRD and make more precisely partition in the high-frequency section of the original traffic. Then ANFIS which can extract useful information from the original traffic is implemented in this study for better prediction performance of each decomposed non-stationary wavelet coefficients. Simulation results show that the proposed WPANFIS can achieve high prediction accuracy in real network traffic environment.

关键词： network traffic prediction WPT ANFIS

Reducing network traffic of Token Protocol Using Sharing Relation Cache

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Tsinghua Science and Technology 2007年第6期12卷 691-699页

作者：王海霞汪东升李鹏王惊雷李崇民 Research Institute of Information Technology National Laboratory for Information Science and Technology Tsinghua University

Token protocol provides a new coherence framework for shared-memory multiprocessor systems. It avoids indirections of directory protocols for common cache-to-cache transfer misses, and achieves higher interconnect bandwidth and lower interconnect latency compared with snooping protocols. However, the broadcasting increases network traffic, limiting the scalability of token protocol. This paper describes an efficient technique to reduce the token protocol network traffic, called sharing relation cache. This cache provides destination set information for cache-to-cache miss requests by caching directory information for recent shared data. This paper introduces how to implement the technique in a token protocol. Simulations using SPLASH-2 benchmarks show that in a 16-core chip multiprocessor system, the cache reduced the network traffic by 15% on average.

关键词： token protocol sharing relation cache network traffic

Study of network traffic Analysis Model Based on Time Granularity

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微计算机信息 2005年第8期 7-9页

作者： Tan,Xiaoling Xu,Yong Mei,Chenggang Liu,Lan (Dept. of Electronic Engineering , Chongqing Three Gorges Univ. , Chongqing 404000 , China) (Guangdong Key Laboratory of Computer network, South China Univ. of Tech., Guangzhou 510640,China) (dept. of the engineering of electronic information, Guangdong Polytechnic Normal Univ.,Guangzhou 510655 , China )

An analytic research on establishing different traffic models on the traffic nature of different time granularity can provide necessary academic foundation for network design and simulation as well as ensuring the quality of service and network management. This paper aims to make simulant predication by means of corresponding math tools on the modeling of real traffic of the different time granularity. The experimental results indicate that the predicated traffic is close to the real traffic distribution.

关键词： network traffic Time granularity traffic model

An Effective network traffic Data Control Using Improved Apriori Rule Mining

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Circuits and Systems 2016年第10期7卷 3162-3173页

作者： Subbiyan Prakash Murugasamy Vijayakumar Department of Information Technology Sri Shakthi Institute of Engineering and Technology Coimbatore India Department of Computer Science and Engineering Nandha College of Technology Erode India

The increasing usage of internet requires a significant system for effective communication. To pro- vide an effective communication for the internet users, based on nature of their queries, shortest routing path is usually preferred for data forwarding. But when more number of data chooses the same path, in that case, bottleneck occurs in the traffic this leads to data loss or provides irrelevant data to the users. In this paper, a Rule Based System using Improved Apriori (RBS-IA) rule mining framework is proposed for effective monitoring of traffic occurrence over the network and control the network traffic. RBS-IA framework integrates both the traffic control and decision making system to enhance the usage of internet trendier. At first, the network traffic data are ana- lyzed and the incoming and outgoing data information is processed using apriori rule mining algorithm. After generating the set of rules, the network traffic condition is analyzed. Based on the traffic conditions, the decision rule framework is introduced which derives and assigns the set of suitable rules to the appropriate states of the network. The decision rule framework improves the effectiveness of network traffic control by updating the traffic condition states for identifying the relevant route path for packet data transmission. Experimental evaluation is conducted by extrac- ting the Dodgers loop sensor data set from UCI repository to detect the effectiveness of theproposed Rule Based System using Improved Apriori (RBS-IA) rule mining framework. Performance evaluation shows that the proposed RBS-IA rule mining framework provides significant improvement in managing the network traffic control scheme. RBS-IA rule mining framework is evaluated over the factors such as accuracy of the decision being obtained, interestingness measure and execution time.

关键词： network traffic Internet traffic Condition Rule Mining Decision Rule Framework Interestingness traffic Data Web Log

ON THE TESTING FOR ALPHA-STABLE DISTRIBUTIONS OF ETHERNET network traffic

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Journal of Electronics(China) 2003年第4期20卷 309-312页

作者： Ge Xiaohu Zhu Guanxi Zhu Yaoting (Electron. & Info. Eng. Dept., Huazhong Univ. of Sci. & Tech., Wuhan 430074) Electron. & Info. Eng. Dept. Huazhong Univ. of Sci. & Tech. Wuhan

The modeling of network traffic is important for the design and application of networks, but little is known as to the characteristics of distribution of packets in network traffic. In this letter the distribution of ... 详细信息

关键词： Alpha-stable distributions Self-similar Maximum likelihood method network traffic