Internet Traffic Classification by Aggregating Correlated Naive Bayes Predictions

Zhang, Jun; Chen, Chao; Xiang, Yang; Zhou, Wanlei; Xiang, Yong

doi:10.1109/tifs.2012.2223675

Cited by 152 publications

(66 citation statements)

References 30 publications

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“…The effectiveness of this scheme is validated via simulation. Flow correlation information is utilized by Zhang et al [10][11][12] to further improve the classification accuracy considering only a small number of training instances based on K-Nearest-Neighbor and Naive Bayes classifiers that are used to detect anomalies in the network. Yan et al [13] propose a framework of security and trust for 5G based on the perspective that the next generation network functions will be highly virtualized and software defined networking is applied for traffic control.…”

Section: Anomaly Detection Techniquesmentioning

confidence: 99%

Botnet detection using graph-based feature clustering

et al. 2017

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Section: Anomaly Detection Techniquesmentioning

confidence: 99%

Botnet detection using graph-based feature clustering

et al. 2017

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“…In constrained based clustering and classification technique provide the different constraints function for the process of clustering. The constrained based clustering technique set the constrained function for different type of data for a separate constraints function [2]. The constrained based clustering technique is very complex process and defines separate constraints function for the processing of classification and cluster generation.…”

Section: Introductionmentioning

confidence: 99%

Untitled

2018

IJET

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“…Rapid developments in multimedia and broadband applications have made traffic classification a difficult subject, but over the years it has drawn significant importance [1]- [5] among researchers. Use of nonstandard ports, user privacy and huge traffic load on the network is creating major bottlenecks to some of the developed techniques.…”

Section: Introductionmentioning

confidence: 99%

Lightweight Internet Traffic Classification based on Packet Level Hidden Markov Models

Akhtar¹,

Kamran²

2017

ijacsa

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Abstract-During the last decade, Internet traffic classification finds its importance not only to safeguard the integrity and security of network resources, but also to ensure the quality of service for business critical applications by optimizing existing network resources. But optimization at first place requires correct identification of different traffic flows. In this paper, we have suggested a framework based on Hidden Markov Model, which will use Internet Packet intrinsic statistical characteristics for traffic classification. The packet inspection based on statistical analysis of its different characteristics has helped to reduce overall computational complexity. Generally, the major challenges associated with any internet traffic classifier are: 1) the limitation to accurately identify encrypted traffic when classification is performed using traditional port based techniques; 2) overall computational complexity, and 3 ) to achieve high accuracy in traffic identification. Our methodology takes advantage of internet packet statistical characteristics in terms of its size and their inter arrival time in order to model different traffic flows. For experimental results, the data set of mostly used internet applications was used. The proposed HMM models best fit the observed traffic with high accuracy. Achieved traffic identification accuracy was 91% for packet size classifier whereas it was 82% for inter packet time based classifier.

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Internet Traffic Classification by Aggregating Correlated Naive Bayes Predictions

Cited by 152 publications

References 30 publications

Botnet detection using graph-based feature clustering

Botnet detection using graph-based feature clustering

Untitled

Lightweight Internet Traffic Classification based on Packet Level Hidden Markov Models

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