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DDOS attack prevention and validation with metric based ensemble approach

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Abstract

DDOS attack is malicious attack that causes disturbance in service corresponding to target server. Malicious user in this case try to flood the server with large volume of packets. Resources thus will not be available to consumers and network is said to be jammed. The research towards distributed denial of service is carried out. Research indicates that network layer is most prone to this type of attack. The proposed work prevents the DDOS attack by reducing the surface area of the attack. This is accomplished by reducing the exposure of resources to ports and applications. The entire work of prevention is categorized into pre-processing that is accomplished through normalization. This operation is used to bring the features from the dataset into specific range. Feature extraction and selection is with optimal procedure of KMLP. K value indicates the exposure value that will act as threshold for exposing server to different ports and applications. Multi-layer perceptron will be used to select optimal features from extracted features. Classification is the last phase that is accomplished with voting classifier including adaptive boost, random forest and KNN. The result of the classification indicates reduced packet drop and increased lifetime of the network. Packet drop ratio is improved by 10% and lifetime by 7%.

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All the data is collected from the simulation reports of the software and tools used by the authors. Authors are working on implementing the same using real world data with appropriate permissions.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, SoET, BGSB University, Rajouri, India

    Amit Dogra

  2. Department of Computer Science and Technology at Guru Nanak Dev University, Regional Campus, Gurdaspur, India

    Taqdir

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  1. Amit Dogra
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  2. Taqdir
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Correspondence to Amit Dogra.

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Dogra, A., Taqdir DDOS attack prevention and validation with metric based ensemble approach. Multimed Tools Appl 82, 44147–44154 (2023). https://doi.org/10.1007/s11042-023-15523-6

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  • DOI: https://doi.org/10.1007/s11042-023-15523-6

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