Towards self adaptive network traffic classification

Tongaonkar, Alok; Torres, Ruben; Iliofotou, Marios; Keralapura, Ram; Nucci, Antonio

doi:10.1016/j.comcom.2014.03.026

Cited by 69 publications

(43 citation statements)

References 19 publications

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“…The second type of profiling is called modelbased profiling [18][19][20][21][22][23], which provides the pre-defined instructions/functions tables associated with hardware energy consumptions. It can be further classified into high-level model [20,21] and low-level model [18,19,[23][24][25]. High-level model is employed to analyze the energy consumption of system-level or function-level on the target systems.…”

Section: Previous Workmentioning

confidence: 99%

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A Machine Learning Method for Power Prediction on the Mobile Devices

Chen

Chen²,

Chen³

et al. 2015

J Med Syst

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Energy profiling and estimation have been popular areas of research in multicore mobile architectures. While short sequences of system calls have been recognized by machine learning as pattern descriptions for anomalous detection, power consumption of running processes with respect to system-call patterns are not well studied. In this paper, we propose a fuzzy neural network (FNN) for training and analyzing process execution behaviour with respect to series of system calls, parameters and their power consumptions. On the basis of the patterns of a series of system calls, we develop a power estimation daemon (PED) to analyze and predict the energy consumption of the running process. In the initial stage, PED categorizes sequences of system calls as functional groups and predicts their energy consumptions by FNN. In the operational stage, PED is applied to identify the predefined sequences of system calls invoked by running processes and estimates their energy consumption.

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Section: Previous Workmentioning

confidence: 99%

“…Linux [5,26,27,25] is an open-sourced OS developed by community and is suitable for client-server infrastructures. To the best of our knowledge, this is the first time that power consumption can be predicted by means of recognizing patterns from a series of system calls using machine-learning methods [11][12][13].…”

Section: Previous Workmentioning

confidence: 99%

A Machine Learning Method for Power Prediction on the Mobile Devices

Chen

Chen²,

Chen³

et al. 2015

J Med Syst

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“…Deep packet inspection methods, usually the most accurate, are based on inspection of the packets' payload. They rely on a database of previously known signatures that are associated to application protocols and search each packet for strings that match any of the signatures . Searching feature string by DPI is generally in the application layer, which is the load of TCP or UDP.…”

Section: Related Workmentioning

confidence: 99%

“…They rely on a database of previously known signatures that are associated to application protocols and search each packet for strings that match any of the signatures. 1,5,6 Searching feature string by DPI is generally in the application layer, which is the load of TCP or UDP. Nevertheless, the main drawbacks of DPI techniques are the following: (1) There are more and more nonstandard applications and private protocols without the open and available protocol specification.…”

Section: Based On Deep Packet Inspectionmentioning

confidence: 99%

“…It is the foundation of high‐performance network protocol design and network operation management. Four traditional methods, which are port numbers, deep packet inspection (DPI), protocol analysis, and machine learning techniques, based on current traffic classification research suffer from different practical issues. The port numbers–based method runs faster but is sensitive to ports confusion.…”

Section: Introductionmentioning

confidence: 99%

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An accurate traffic classification model based on support vector machines

Cao

Fang

et al. 2017

Int. J. Network Mgmt

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SummaryNetwork traffic classification is a fundamental research topic on high-performance network protocol design and network operation management. Compared with other state-of-the-art studies done on the network traffic classification, machine learning (ML) methods are more flexible and intelligent, which can automatically search for and describe useful structural patterns in a supplied traffic dataset. As a typical ML method, support vector machines (SVMs) based on statistical theory has high classification accuracy and stability. However, the performance of SVM classifier can be severely affected by the data scale, feature dimension, and parameters of the classifier. In this paper, a real-time accurate SVM training model named SPP-SVM is proposed. An SPP-SVM is deducted from the scaling dataset and employs principal component analysis (PCA) to extract data features and verify its relevant traffic features obtained from PCA. By employing PCA algorithm to do the dimension extraction, SPP-SVM confirms the critical component features, reduces the redundancy among them, and lowers the original feature dimension so as to reduce the over fitting and increase its generalization effectively. The optimal working parameters of kernel function used in SPP-SVM are derived automatically from improved particle swarm optimization algorithm, which will optimize the global solution and make its inertia weight coefficient adaptive without searching for the parameters in a wide range, traversing all the parameter points in the grid and adjusting steps gradually. The performance of its two-and multi-class classifiers is proved over 2 sets of traffic traces, coming from different topological points on the Internet. Experiments show that the SPP-SVM's two-and multi-class classifiers are superior to the typical supervised ML algorithms and performs significantly better than traditional SVM in classification accuracy, dimension, and elapsed time.

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