Low complexity, high performance neuro-fuzzy system for Internet traffic flows early classification

Rizzi, Antonello; Colabrese, Silvia; Baiocchi, Andrea

doi:10.1109/iwcmc.2013.6583538

Cited by 15 publications

(4 citation statements)

References 23 publications

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“…Hullar et al present an automatic machine learning (ML) method for P2P traffic classification at early stage, which consumes limited computational and memory resources for early stage traffic identification of P2P traffic. Rizzi et al in 2013 [13] present a very effective neuro fuzzy system to identify early stage traffic. Nguyen et al [14] further extend the early stage to "timely" for VoIP traffic classification.…”

Section: Related Workmentioning

confidence: 99%

Effective Packet Number for 5G IM WeChat Application at Early Stage Traffic Classification

Shafiq

2017

Mobile Information Systems

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Accurate network traffic classification at early stage is very important for 5G network applications. During the last few years, researchers endeavored hard to propose effective machine learning model for classification of Internet traffic applications at early stage with few packets. Nevertheless, this essential problem still needs to be studied profoundly to find out effective packet number as well as effective machine learning (ML) model. In this paper, we tried to solve the above-mentioned problem. For this purpose, five Internet traffic datasets are utilized. Initially, we extract packet size of 20 packets and then mutual information analysis is carried out to find out the mutual information of each packet onnflow type. Thereafter, we execute 10 well-known machine learning algorithms using crossover classification method. Two statistical analysis tests, Friedman and Wilcoxon pairwise tests, are applied for the experimental results. Moreover, we also apply the statistical tests for classifiers to find out effective ML classifier. Our experimental results show that 13–19 packets are the effective packet numbers for 5G IM WeChat application at early stage network traffic classification. We also find out effective ML classifier, where Random Forest ML classifier is effective classifier at early stage Internet traffic classification.

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Section: Related Workmentioning

confidence: 99%

Effective Packet Number for 5G IM WeChat Application at Early Stage Traffic Classification

Shafiq

2017

Mobile Information Systems

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“…7, where we report the CPU time for the test set evaluation only. This fact might assume more importance in particular applications, especially in those where the synthesis of the classifier can be effectively performed only once in off-line mode and the classification model is employed to process high-rate data streams in real-time [49].…”

Section: Acronymmentioning

confidence: 99%

Designing Labeled Graph Classifiers by Exploiting the Rényi Entropy of the Dissimilarity Representation

Livi

2017

Entropy

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Representing patterns as labeled graphs is becoming increasingly common in the broad field of computational intelligence. Accordingly, a wide repertoire of pattern recognition tools, such as classifiers and knowledge discovery procedures, are nowadays available and tested for various datasets of labeled graphs. However, the design of effective learning procedures operating in the space of labeled graphs is still a challenging problem, especially from the computational complexity viewpoint. In this paper, we present a major improvement of a general-purpose classifier for graphs, which is conceived on an interplay between dissimilarity representation, clustering, information-theoretic techniques, and evolutionary optimization algorithms. The improvement focuses on a specific key subroutine devised to compress the input data. We prove different theorems which are fundamental to the setting of the parameters controlling such a compression operation. We demonstrate the effectiveness of the resulting classifier by benchmarking the developed variants on well-known datasets of labeled graphs, considering as distinct performance indicators the classification accuracy, computing time, and parsimony in terms of structural complexity of the synthesized classification models. The results show state-of-the-art standards in terms of test set accuracy and a considerable speed-up for what concerns the computing time.

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“…T. T. T. Nguyen et al use statistical features derived from sub-flows for timely identification of VoIP traffics [13], they extend the concept of early stage to "timely", since a sub-flows refers to a small number of most recent packets taken at any point in a flow's lifetime. A. Rizzi et al proposed a highly efficient neuro-fuzzy system for early stage traffic identification [14].…”

Section: Introductionmentioning

confidence: 99%

Early Stage Internet Traffic Identification Using Probabilistic Neural Networks

Peng¹

2015

IJCCE

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Identifying network traffics at their early stages accurately is very important for network management and security. Recent years, more and more studies have devoted to find effective machine learning models to identify traffics with the few packets at the early stage. In this paper, we try to build an effective early stage traffic identification model by applying probabilistic neural networks. Three network traffic data sets including two open data sets are used for the study. Packet sizes and three statistics are applied as features. Six classical classifiers are employed as the comparing methods in the identification experiments. PNN outperforms the other methods for most cases in the identification experiments, and it behaves very well for both of accuracy and AUC. Thus, PNN is effective for early stage traffic identification.

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Low complexity, high performance neuro-fuzzy system for Internet traffic flows early classification

Cited by 15 publications

References 23 publications

Effective Packet Number for 5G IM WeChat Application at Early Stage Traffic Classification

Effective Packet Number for 5G IM WeChat Application at Early Stage Traffic Classification

Designing Labeled Graph Classifiers by Exploiting the Rényi Entropy of the Dissimilarity Representation

Early Stage Internet Traffic Identification Using Probabilistic Neural Networks

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