Intelligent IP traffic matrix estimation by neural network and genetic algorithm

Omidvar, Abbas Erfanian; Shahhoseini, Hadi Shahriar

doi:10.1109/wisp.2011.6051689

Cited by 20 publications

(13 citation statements)

References 16 publications

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“…P r detecting anomalies when there are not (for j th flow) (11) It is interesting to note that, as it was expected, by increasing π A and P AS (or equivalently in the presence of more-frequent short-bursty anomalies) the detection performance is degraded. Among these, the probability of detection is decreased more noticeably, that is, it is more difficult to detect an anomaly with the rapid transitions between two silent and active states.…”

Section: B Mdfe Applications (2): Network Anomaly Detection For Cybementioning

confidence: 73%

“…(11). These two metrics are evaluated through a Monte-Carlo simulation and their averages are shown in Table III and Table IV.…”

Section: B Mdfe Applications (2): Network Anomaly Detection For Cybementioning

confidence: 99%

“…Accordingly, UDLI problems are naturally ill-posed as there are not sufficient observations to uniquely and accurately estimate the solution [5]. Therefore, side information from different perspectives and multiple sources must be incorporated into the problem formulation and/or underline intelligent computation engine to improve the estimation precision, and cope with the complexity of inference process [1] [5] [6] [7] [8] [9] [10] [11] [12] [13].…”

Section: Introductionmentioning

confidence: 99%

“…Existing studies generally attempt to infer TMs in a centralized manner where all measurements are collected and processed at a central node by applying domain-specific inference techniques and/or machine learning/intelligence algorithms [1] [7] [8] [9] [10] [11] [12] [13] [16]. However, the high computational complexity of these centralized inference techniques/algorithms hinders their deployment in large-scale and dynamic production networks where network inference processes/algorithms must be performed at much faster time scales for practical deployment [17].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Parallelizing Under-Determined Inverse Problems for Network Applications

Malboubi

Garrison

Chuah

et al. 2017

IEEE Trans. Emerg. Top. Comput. Intell.

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Abstract-In this paper, we introduce a new technique for partitioning a large-scale under-determined linear inverse problem into multiple smaller sub-problems that can be efficiently solved independently, and in parallel. When it is impossible or inefficient to solve a large-scale under-determined linear inverse problem, this technique can be used to significantly speed up the computation process without compromising the accuracy of the solution. We present numerical results that show the effectiveness of this approach when applied to network inference problems including traffic matrix estimation and network anomaly detection, both are important for managing large, complex networks and cyber-security. Our proposed framework is applicable to other emerging applications in computational intelligence that can be formulated as UDLI problems.

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Section: B Mdfe Applications (2): Network Anomaly Detection For Cybementioning

confidence: 73%

“…(11). These two metrics are evaluated through a Monte-Carlo simulation and their averages are shown in Table III and Table IV.…”

Section: B Mdfe Applications (2): Network Anomaly Detection For Cybementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Parallelizing Under-Determined Inverse Problems for Network Applications

Malboubi

Garrison

Chuah

et al. 2017

IEEE Trans. Emerg. Top. Comput. Intell.

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show abstract

“…A. Omidvar et al . proposed an approach, which combines artificial neural network and evolutionary algorithms called ARXGEN.…”

Section: Introductionmentioning

confidence: 99%

A reconstructing approach to end‐to‐end network traffic based on multifractal wavelet model

Nie

Jiang

Guo

et al. 2013

Int J Communication

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SUMMARYThis paper studies the reconstructing method of end-to-end network traffic. Due to the development of current communication networks, our networks become more complex and heterogeneous. Meanwhile, because of time-varying nature and spatio-temporal correlations of the end-to-end network traffic, to obtain it accurately is a great challenge. We propose to exploit discrete wavelet transforms and multifractal analysis to reconstruct the end-to-end network traffic from time-frequency domain. First, its time-frequency properties can be characterized in detail by discrete wavelet transforms. And then, we combine discrete wavelet transforms and multifractal analysis to reconstruct end-to-end network traffic from link loads. Furthermore, our method needs to measure end-to-end network traffic to build the statistical model named multifractal wavelet model. Finally, simulation results from the real backbone networks suggest that our method can reconstruct the end-to-end network traffic more accurately than previous methods.

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A novel hybrid prediction algorithm to network traffic

Jiang

2015

Ann. Telecommun.

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Network traffic describes the characteristics and users' behaviors of communication networks. It is a crucial input parameter of network management and network traffic engineering. This paper proposes a new prediction algorithm to network traffic in the large-scale communication network. First, we use signal analysis theory to transform network traffic from time domain to timefrequency domain. In the time-frequency domain, the network traffic signal is decomposed into the low-frequency and high-frequency components. Second, the gray model is exploited to model the low-frequency component of network traffic. The white Gaussian noise model is utilized to describe its high-frequency component. This is reasonable because the low-frequency and highfrequency components, respectively, represent the trend and fluctuation properties of network traffic, while the gray model and white Gaussian noise model can well capture the characteristics. Third, the prediction models of low-frequency and high-frequency components are built. The hybrid prediction algorithm is proposed to overcome the problem of network traffic prediction in the communication network. Finally, network traffic data from the real network is used to validate our approach. Simulation results indicate that our algorithm holds much lower prediction error than previous methods.

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Intelligent IP traffic matrix estimation by neural network and genetic algorithm

Cited by 20 publications

References 16 publications

Parallelizing Under-Determined Inverse Problems for Network Applications

Parallelizing Under-Determined Inverse Problems for Network Applications

A reconstructing approach to end‐to‐end network traffic based on multifractal wavelet model

A novel hybrid prediction algorithm to network traffic

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