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Deep neural network empowered bi-directional cross GAN in context of classifying DDoS over flash crowd event on web server

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Abstract

DDoS (distributed denial of service) attack is a constant and serious danger to the Internet. New application-layer-based DDoS attacks that use valid requests to overload target resources are more undetected than low-layer DDoS attacks. When flash crowd assaults imitate or occur during a flash crowd event on webservers, the situation may become more dangerous. Flash Crowd Assaults (FCAs) are DDoS attacks that flood victim services, such as Web servers, with well-formed requests created by a large number of bots. Because both valid and attack requests appear identical, it’s difficult to identify and filter such attacks.Hence, differentiating DDoS and FC is a critical task in protecting web servers against attacks, which can be fatal to cyber-systems.In this article, a novel Deep Neural Network empowered Bi-Directional Cross Generative Adversarial Network (GAN) is introduced for recognizing and separating distributed denial-of-service attacks from flash crowd attacks over web applications. The suggested research uses an ensemble feature selection approach to construct real samples of attack data in order to accomplish the goal. In the meanwhile, the generator network uses a bidirectionally cross GAN to generate bogus attack data by observing the random noise vector as input. The discriminator receives both manual and model input and using DNN to distinguish between DDoS assaults and flash crowd attacks. The proposed model is implemented in the working platform of python and is tested using the evaluation metrics like accuracy, precision, recall, and f1-measure.From the implementation result, is evident that the proposed model achieves an effective performance in whichthe obtained accuracy of the proposed approach is 96.58% which is comparatively higher than the existing techniques.

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Data availability

The open-source World Cup 1998 dataset and the CAIDA DDoS 2007 dataset were used to collect data in order to distinguish between Flash crowd attacks and DDoS attacks.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, JNTU Kakinada, Kakinada, Andhra Pradesh, India

    Sekhar C. H.

  2. Department of Computer Science and Engineering, Guru Nanak Institutions Technical Campus, Manchal, Ibrahimpatnam, Telangana, India

    K. Venkata Rao

  3. Department of Computer Science and Engineering, University College of Engineering Kakinada (Autonomous), JNTUK, Kakinada, Andhra Pradesh, India

    M. H. M. Krishna Prasad

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  1. Sekhar C. H.
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H., S.C., Rao, K.V. & Prasad, M.H.M.K. Deep neural network empowered bi-directional cross GAN in context of classifying DDoS over flash crowd event on web server. Multimed Tools Appl 82, 37303–37326 (2023). https://doi.org/10.1007/s11042-023-15030-8

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  • DOI: https://doi.org/10.1007/s11042-023-15030-8

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