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This paper aims at evaluating the performance of artificial neural network with the help of standard datasets like KDD and UNSW-NB 15 respectively in intrusion detection system. The standard datasets of Knowledge Discovery in Databases (KDD’99) and University of New South Wales Network Based 2015 (UNSW-NB 15) have been used. KDD’99 is the popular benchmark for intrusion detection. UNSW-NB 15 was prepared using IXIA PerfectStorm tool of the Cyber Range Lab of the Australian Centre for Cyber Security (ACCS) to generate real-time day to day activities and simulated attacks in accordance to the current network environments. As these standard datasets have several types of attacks, the emphasis has been given to categorize the performance evaluation based on the types of attacks.

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A Comparative Study of Standard Datasets in Intrusion Detection System Using Various Artificial Neural Networks Techniques

  • 会议论文

  • 发表日期：

    2026年

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  • Inadyuti Dutt,
  • Samarjeet Borah,
  • Indra Kanta Maitra

摘要

This paper aims at evaluating the performance of artificial neural network with the help of standard datasets like KDD and UNSW-NB 15 respectively in intrusion detection system. The standard datasets of Knowledge Discovery in Databases (KDD’99) and University of New South Wales Network Based 2015 (UNSW-NB 15) have been used. KDD’99 is the popular benchmark for intrusion detection. UNSW-NB 15 was prepared using IXIA PerfectStorm tool of the Cyber Range Lab of the Australian Centre for Cyber Security (ACCS) to generate real-time day to day activities and simulated attacks in accordance to the current network environments. As these standard datasets have several types of attacks, the emphasis has been given to categorize the performance evaluation based on the types of attacks.