Cite this DOI

Pawan, D. V. R. N., Kumar, K. V., & Prasad, C. K. (2022). Relevance feature selection via analysis of the KDD '99 intrusion detection dataset. *Journal of Science and Technology*, *07*(09), 135–140. https://doi.org/10.46243/jst.2022.v7.i09.pp0135-140

@article{pawan2022relevance,
  author    = {Pawan, Dr.Y V R Naga and Kumar, K Vijay and Prasad, CH Krishna},
  title     = {{Relevance feature selection via analysis of the KDD '99 intrusion detection dataset}},
  journal   = {Journal of Science and Technology},
  year      = {2022},
  volume    = {07},
  number    = {09},
  pages     = {135--140},
  publisher = {Longman Publishers},
  issn      = {2456-5660},
  doi       = {10.46243/jst.2022.v7.i09.pp0135-140},
  url       = {https://doi.org/10.46243/jst.2022.v7.i09.pp0135-140},
  abstract  = {The rapid development of business and othertransaction systems over the Internet makes computer securitya critical issue. In recent times, data mining and machine learning have been subjected to extensive research in intrusion detection with emphasis on improving the accuracy of detection classifier. But selecting important features from input data lead to a simplification of the problem, faster and more accurate detection rates. In this paper, we presented therelevance of each feature in KDD ’99 intrusion detection dataset to the detection of each class. Rough set degree of dependency and dependency ratio of each class were employed to determine the most discriminating features for each class. Empirical results show that seven features were not relevant in the detection of any class.}
}

TY  - JOUR
TI  - Relevance feature selection via analysis of the KDD '99 intrusion detection dataset
AU  - Pawan, Dr.Y V R Naga
AU  - Kumar, K Vijay
AU  - Prasad, CH Krishna
JO  - Journal of Science and Technology
PY  - 2022
DA  - 2022///
VL  - 07
IS  - 09
SP  - 135
EP  - 140
PB  - Longman Publishers
SN  - 2456-5660
AB  - The rapid development of business and othertransaction systems over the Internet makes computer securitya critical issue. In recent times, data mining and machine learning have been subjected to extensive research in intrusion detection with emphasis on improving the accuracy of detection classifier. But selecting important features from input data lead to a simplification of the problem, faster and more accurate detection rates. In this paper, we presented therelevance of each feature in KDD ’99 intrusion detection dataset to the detection of each class. Rough set degree of dependency and dependency ratio of each class were employed to determine the most discriminating features for each class. Empirical results show that seven features were not relevant in the detection of any class.
DO  - 10.46243/jst.2022.v7.i09.pp0135-140
UR  - https://doi.org/10.46243/jst.2022.v7.i09.pp0135-140
ER  -

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    "DOI": "10.46243/jst.2022.v7.i09.pp0135-140",
    "URL": "https://doi.org/10.46243/jst.2022.v7.i09.pp0135-140",
    "title": "Relevance feature selection via analysis of the KDD '99 intrusion detection dataset",
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    "abstract": "The rapid development of business and othertransaction systems over the Internet makes computer securitya critical issue. In recent times, data mining and machine learning have been subjected to extensive research in intrusion detection with emphasis on improving the accuracy of detection classifier. But selecting important features from input data lead to a simplification of the problem, faster and more accurate detection rates. In this paper, we presented therelevance of each feature in KDD ’99 intrusion detection dataset to the detection of each class. Rough set degree of dependency and dependency ratio of each class were employed to determine the most discriminating features for each class. Empirical results show that seven features were not relevant in the detection of any class.",
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From the record as registered (version 1) — the record and its history. Programs: https://registry.smartscholars.in/api.php?action=cite&doi=10.46243%2Fjst.2022.v7.i09.pp0135-140 gives all four in one JSON answer.