A Network Management System Based on DPI

Yang, Chu-Sing; Liao, Ming-Liang; Luo, Mon-Yen; Wang, Shin-Min; Yeh, Chih-Shen

doi:10.1109/nbis.2010.71

Cited by 14 publications

(7 citation statements)

References 1 publication

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“…Machine learning is an important tool for the study of network traffic identification. Yang et al described the current popular machine learning method . After comparing and evaluating the clustering algorithm, it was found that the feature selection algorithm was better for supervised machine learning .…”

Section: Introductionmentioning

confidence: 99%

Retracted: Traffic identification and traffic analysis based on support vector machine

Wang

Yang

et al. 2019

Concurrency and Computation

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Summary The number of applications based on the Internet is increasing, which results the traffic becoming more and more complex. Therefore, how to improve the service quality and security of the network is becoming more and more important. This paper studies the application of SVM in traffic identification to classify network traffic. Through data collection and feature generation methods and network traffic feature screening methods, SVM is used as a classifier by using the generalization capability of SVM, and the parameters and kernel functions of the SVM are adjusted and selected based on cross comparison ideas and methods. Using the cross‐validation method to make the most reasonable statistics for the classification and recognition accuracy of the adjusted support vector machine avoids the situation that the classification accuracy of the support vector machine is unstable or the statistics are inaccurate. Finally, a traffic classification and identification system based on SVM is realized. The final recognition rate of encrypted traffic is up to 99.31%, which overcomes the disadvantages of traditional traffic identification and achieves a fairly reliable accuracy.

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Section: Introductionmentioning

confidence: 99%

Retracted: Traffic identification and traffic analysis based on support vector machine

Wang

Yang

et al. 2019

Concurrency and Computation

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“…As described in Reference [5], Reference [6] has proposed a network management system with DPI server which supports the network management system to classify the network traffic. For example, if the DPI equipment finds that the user id of the VoIP session is in the black list, the DPI equipment informs the NMS about this SIP session.…”

Section: Related Workmentioning

confidence: 99%

DPI-Based Congestion Control Method for Servers and Network Lines

Kuribayashi¹

2018

IJCNC

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The use of Deep Packet Inspection (DPI) equipment in a network could simplify the conventional workload for system management and accelerate the control action. The authors proposed a congestion control method that uses DPI equipment installed in a network to estimate overload conditions of servers or network lines and, upon detecting an overload condition, resolves congestion by moving some virtual machines to other servers or rerouting some communication flows to other routes. However, since the previous paper was focused on confirming the effectiveness of using DPI technology, it assumed some restrictive control conditions. This paper proposes to enhance the existing DPI-based congestion control, in order to dynamically select an optimal solution for cases where there are multiple candidates available for: virtual machines to be moved, physical servers to which virtual machines are to be moved, communication flows to be diverted, and routes to which communication flows are to be diverted. This paper also considers server congestion for cases where computing power congestion and bandwidth congestion occur simultaneously in a server, and line congestion for cases where the maximum allowable network delay of each communication flow is taken into consideration. Finally, the feasibility of the proposed methods is demonstrated by an evaluation system with real DPI equipment.

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“…Reference [5] has proposed to apply DPI technology to the CDMA mobile network packet switch domain and constructed a DPI based network traffic monitoring, analysis and management system. References [7] and [8] has presented a technical survey for the implementation and evaluating of traffic classification modules under a common platform. Reference [9] has proposed a method that enables to find out a DPI engine deployment that satisfies the trade-off between the minimum number of engines and the minimum network load for a considered set of costs and operational constraints.…”

Section: Related Workmentioning

confidence: 99%

Server Congestion Control And Reduction of Server Power Consumption With DPI Technology

Kuribayashi¹

2015

IJCNC

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Deep packet inspection (DPI) technology has conventionally been introduced for traffic monitoring, bandwidth control, access control of specific types of traffic, etc. As a new potential application of DPI technology, this paper proposes three promising approaches of network and server control with DPI technology, and presents implementation examples of two approaches. These approaches could simplify dramatically the conventional work load for system management and reduction of p ower consumption by servers and accelerate the control action.

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A Network Management System Based on DPI

Cited by 14 publications

References 1 publication

Retracted: Traffic identification and traffic analysis based on support vector machine

Retracted: Traffic identification and traffic analysis based on support vector machine

DPI-Based Congestion Control Method for Servers and Network Lines

Server Congestion Control And Reduction of Server Power Consumption With DPI Technology

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