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Recommendations for DDOS Attack-Based Intrusion Detection System Through Data Analysis

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Proceedings of Second Doctoral Symposium on Computational Intelligence

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1374))

Abstract

As internet usage is increasing as the days are passing on, it is essential to secure the network from intruders. So, it indicates the necessity of the construction of an intrusion detection system. But one must know on which basis the intrusion detection system (IDS) needs to be built. This thought dragged us to generate a new idea of forming the recommendations which can act as a basis for the development of IDS. In this paper, we have provided the recommendation by analyzing the standard datasets, KDD, and NSL-KDD. For the analysis purpose, MS-Excel was utilized.

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Author information

Authors and Affiliations

  1. School of Computer Science and Engineering, Lovely Professional University, Punjab, India

    Sagar Pande & Aditya Kamparia

  2. Department of Computer Science Engineering, Maharaja Agrasen Institute of Technology, New Delhi, India

    Deepak Gupta

Authors
  1. Sagar Pande
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  2. Aditya Kamparia
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  3. Deepak Gupta
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Editor information

Editors and Affiliations

  1. Department of Computer Science Engineering, Maharaja Agrasen Institute of Technology, Rohini, Delhi, India

    Deepak Gupta

  2. Maharaja Agrasen Institute of Technology, Rohini, Delhi, India

    Ashish Khanna

  3. Institute of Engineering and Technology, Lucknow, Uttar Pradesh, India

    Vineet Kansal

  4. University of Calabria, Rende, Cosenza, Italy

    Giancarlo Fortino

  5. Department of Information Technology, Cairo University, Giza, Egypt

    Aboul Ella Hassanien

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Pande, S., Kamparia, A., Gupta, D. (2022). Recommendations for DDOS Attack-Based Intrusion Detection System Through Data Analysis. In: Gupta, D., Khanna, A., Kansal, V., Fortino, G., Hassanien, A.E. (eds) Proceedings of Second Doctoral Symposium on Computational Intelligence . Advances in Intelligent Systems and Computing, vol 1374. Springer, Singapore. https://doi.org/10.1007/978-981-16-3346-1_73

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