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RETRACTED ARTICLE: Network traffic detection for peer-to-peer traffic matrices on bayesian network in WSN

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This article was retracted on 06 June 2022

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Abstract

With the wide application of wireless sensor networks, network security has been a terrible problem when it provides many more services and applications. Rapid usage of internet and connectivity demands a network anomaly system combating cynical network attacks. Meanwhile, it is a common approach for acquiring, which can be used by network operators to carry out network management and configuration. Moreover, a great number of evaluations have been proposed to simulate and analyse the Wireless Sensor Network traffic, it is still a remarkable challenge since, and network traffic characterization has been tremendously changed, in particular, for a sensor computing network. Bayesian Based Network Traffic Prediction (BNTP) is proposed to solve the deep learning of statistical features of network traffic flow so that all the packets were sent to the receiver properly without any traffic density. Bayesian network-based peer-to-peer network traffic design is proposed to determine the spatial structure of traffic flow. PVM fault localization feature is proposed to remove the accuracy measure issues and performance problems. The co-existence mechanism is used to minimize the inference and overlap problem in wireless network devices. This paper avoids the conflicts in traffic analysis and statistical features of the network. The performance of the network is increased to 80% when compared to the existing methods.

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Authors and Affiliations

  1. Computer Science and Engineering, PSN College of Engineering and Technology, Tirunelveli, India

    D. Geepthi & C. Christopher Columbus

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  1. D. Geepthi
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  2. C. Christopher Columbus
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Correspondence to D. Geepthi.

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This article has been retracted. Please see the retraction notice for more detail: https://doi.org/10.1007/s12652-022-04079-2

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Geepthi, D., Columbus, C.C. RETRACTED ARTICLE: Network traffic detection for peer-to-peer traffic matrices on bayesian network in WSN. J Ambient Intell Human Comput 12, 6975–6986 (2021). https://doi.org/10.1007/s12652-020-02355-7

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  • DOI: https://doi.org/10.1007/s12652-020-02355-7

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