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RETRACTED ARTICLE: A hybrid multi-layer intrusion detection system in cloud

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This article was retracted on 01 December 2022

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Abstract

Cloud computing being the representation of the technology makes use of the infrastructure for computing in an efficient manner. This type of a computing offers a large amount of potential in improving the productivity which reduces the costs and also ensures that this can handle the risks. The intrusion detection systems (IDS) are all widely used for malicious detection in the network of communication and also its host. The IDS system used currently has one set of rules with several patterns of attach which get stored inside the various databases and the whole traffic of network will be duly matched against this for the purpose of avoiding any other illegal or also unauthorized activities. Therefore, in this work, this structure has optimized multi-layer artificial neural network is based on the IDS in case of the cloud which has been presented. This hybrid glow swarm optimization (GSO)–tabu search (TS) is called the GSO–TS has been used for the optimization of the structure and also for the purpose of reduction of convergence time and for solving old problems, trapping of local optima and their premature convergence. The results have proved to have better performance.

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Authors and Affiliations

  1. GKM College of Engineering and Technology, Chennai, 600 063, Tamil Nadu, India

    M. Manickam

  2. Department of Computer Science and Engineering, GKM College of Engineering and Technology, Chennai, 600 063, Tamil Nadu, India

    S. P. Rajagopalan

Authors
  1. M. Manickam
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  2. S. P. Rajagopalan
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Correspondence to M. Manickam.

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This article has been retracted. Please see the retraction notice for more detail: https://doi.org/10.1007/s10586-022-03833-7

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Manickam, M., Rajagopalan, S.P. RETRACTED ARTICLE: A hybrid multi-layer intrusion detection system in cloud. Cluster Comput 22 (Suppl 2), 3961–3969 (2019). https://doi.org/10.1007/s10586-018-2557-5

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  • DOI: https://doi.org/10.1007/s10586-018-2557-5

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