Optimal Ensemble Learning Based on Distinctive Feature Selection by Univariate ANOVA-F Statistics for IDS

• Autorzy: Shakeela Shaikh , Sai Shankar N. , Mohan Reddy P. , Kavya Tulasi T. , Mahesh Koneru M.

Identyfikatory

DOI

10.24425/ijet.2021.135975

• Języki publikacji: EN

Abstrakty

EN

Cyber-attacks are increasing day by day. The generation of data by the population of the world is immensely escalated. The advancements in technology, are intern leading to more chances of vulnerabilities to individual’s personal data. Across the world it became a very big challenge to bring down the threats to data security. These threats are not only targeting the user data and also destroying the whole network infrastructure in the local or global level, the attacks could be hardware or software. Central objective of this paper is to design an intrusion detection system using ensemble learning specifically Decision Trees with distinctive feature selection univariate ANOVA-F test. Decision Trees has been the most popular among ensemble learning methods and it also outperforms among the other classification algorithm in various aspects. With the essence of different feature selection techniques, the performance found to be increased more, and the detection outcome will be less prone to false classification. Analysis of Variance (ANOVA) with F-statistics computations could be a reasonable criterion to choose distinctives features in the given network traffic data. The mentioned technique is applied and tested on NSL KDD network dataset. Various performance measures like accuracy, precision, F-score and Cross Validation curve have drawn to justify the ability of the method.

Słowa kluczowe

EN

ANOVA-F test; cross validation; decision tree; features; NSL-KDD; data set

• Wydawca: Polish Academy of Sciences, Committee of Electronics and Telecommunication
• Czasopismo: International Journal of Electronics and Telecommunications
• Rocznik: 2021
• Tom: Vol. 67, No. 2
• Strony: 267--275
• Opis fizyczny: Bibliogr. 16 poz., rys., schem., tab., wykr.

Twórcy

autor

Shakeela Shaikh

• ECM, Koneru Lakshmaiah Education Foundation, Vaddeswaram, AP, India

autor

Sai Shankar N.

• ECM, Koneru Lakshmaiah Education Foundation, Vaddeswaram, AP, India

autor

Mohan Reddy P.

• ECM, Koneru Lakshmaiah Education Foundation, Vaddeswaram, AP, India

autor

Kavya Tulasi T.

• ECM, Koneru Lakshmaiah Education Foundation, Vaddeswaram, AP, India

autor

Mahesh Koneru M.

• ECM, Koneru Lakshmaiah Education Foundation, Vaddeswaram, AP, India

Bibliografia

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• [13] Campbell and Zachary, “Differentially Private ANOVA Testing,” 2018 1st International Conference on Data Intelligence and Security (ICDIS) (2018): 281-285.
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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).