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Computational Intelligence in Pattern Recognition pp 279–294Cite as

Statistical Analysis of the UNSW-NB15 Dataset for Intrusion Detection

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 999))

Abstract

Intrusion Detection System (IDS) has been developed to protect the resources in the network from different types of threats. Existing IDS methods can be classified as either anomaly based or misuse (signature) based or sometimes combination of both. This paper proposes a novel misuse-based intrusion detection system to defend our network from five categories such as Exploit, DOS, Probe, Generic, and Normal. Most of the related works on IDS are based on KDD99 or NSL-KDD 99 dataset. These datasets are considered obsolete to detect recent types of attacks and have no significance. In this paper, UNSW-NB15 (Moustafa and Slay, Military Communications and Information Systems Conference (2015) [1]) dataset is considered as the offline dataset to design intrusion detection model for detecting malicious activities in the network. The performance evaluation of proposed work with the UNSW-NB15 (benchmark dataset) shows higher accuracy and IDR compared to other existing approaches. Performance analysis proves that clustering technique is really useful in order to analyze similarity in behavior of different categories and hence helpful to improve the performance of IDS.

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Acknowledgements

This research was supported by Information Security Education and Awareness (ISEA) Project II funded by Ministry of Electronics and Information Technology (MeitY), Govt. of India.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, National Institute of Technology, Patna, 800005, India

    Vikash Kumar & Ditipriya Sinha

  2. Birla Institute of Technolgy, Patna, 800014, India

    Ayan Kumar Das

Authors
  1. Vikash Kumar
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  2. Ayan Kumar Das
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  3. Ditipriya Sinha
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Corresponding author

Correspondence to Vikash Kumar .

Editor information

Editors and Affiliations

  1. Department of Computer Science and Technology, Indian Institute of Engineering Science and Technology, Howrah, West Bengal, India

    Asit Kumar Das

  2. Department of Computer Science and Engineering, Sri Sivani College of Engineering, Srikakulam, Andhra Pradesh, India

    Janmenjoy Nayak

  3. Department of Computer Application, Veer Surendra Sai University of Technology, Burla, Sambalpur, Odisha, India

    Bighnaraj Naik

  4. Department of Bioinformatics, Maulana Abul Kalam Azad University of Technology, Kolkata, West Bengal, India

    Soumen Kumar Pati

  5. Faculty of Communication Sciences, University of Teramo, Teramo, Italy

    Danilo Pelusi

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Kumar, V., Das, A.K., Sinha, D. (2020). Statistical Analysis of the UNSW-NB15 Dataset for Intrusion Detection. In: Das, A., Nayak, J., Naik, B., Pati, S., Pelusi, D. (eds) Computational Intelligence in Pattern Recognition. Advances in Intelligent Systems and Computing, vol 999. Springer, Singapore. https://doi.org/10.1007/978-981-13-9042-5_24

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