Comparative Analysis of Selected Filtered Feature Rankers Evaluators for Cyber Attacks Detection

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Olasehinde Olayemi
Olasehinde Olayemi

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GJCST Volume 22 Issue E1

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Comparative Analysis of Selected Filtered Feature Rankers Evaluators for Cyber Attacks Detection Banner
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An increase in global connectivity and rapid expansion of computer usage and computer networks has made the security of the computer system an important issue; with the industries and cyber communities being faced with new kinds of attacks daily. The high complexity of cyberattacks poses a great challenge to the protection of cyberinfrastructures, Confidentiality, Integrity, and availability of sensitive information stored on it. Intrusion detection systems monitors’ network traffic for suspicious (Intrusive) activity and issues alert when such activity is detected. Building Intrusion detection system that is computationally efficient and effective requires the use of relevant features of the network traffics (packets) identified by feature selection algorithms. This paper implemented K-Nearest Neighbor and Naïve Bayes Intrusion detection models using relevant features of the UNSW-NB15 Intrusion detection dataset selected by Gain Ratio, Information Gain, Relief F and Correlation rankers feature selection techniques.

Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

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No ethics committee approval was required for this article type.

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Not applicable for this article.

Olasehinde Olayemi. 2026. \u201cComparative Analysis of Selected Filtered Feature Rankers Evaluators for Cyber Attacks Detection\u201d. Global Journal of Computer Science and Technology - E: Network, Web & Security GJCST-E Volume 22 (GJCST Volume 22 Issue E1): .

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Enhanced cybersecurity evaluation using feature ranking techniques.
Issue Cover
GJCST Volume 22 Issue E1
Pg. 37- 44
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Crossref Journal DOI 10.17406/gjcst

Print ISSN 0975-4350

e-ISSN 0975-4172

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March 12, 2022

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English

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An increase in global connectivity and rapid expansion of computer usage and computer networks has made the security of the computer system an important issue; with the industries and cyber communities being faced with new kinds of attacks daily. The high complexity of cyberattacks poses a great challenge to the protection of cyberinfrastructures, Confidentiality, Integrity, and availability of sensitive information stored on it. Intrusion detection systems monitors’ network traffic for suspicious (Intrusive) activity and issues alert when such activity is detected. Building Intrusion detection system that is computationally efficient and effective requires the use of relevant features of the network traffics (packets) identified by feature selection algorithms. This paper implemented K-Nearest Neighbor and Naïve Bayes Intrusion detection models using relevant features of the UNSW-NB15 Intrusion detection dataset selected by Gain Ratio, Information Gain, Relief F and Correlation rankers feature selection techniques.

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Comparative Analysis of Selected Filtered Feature Rankers Evaluators for Cyber Attacks Detection

Olasehinde Olayemi
Olasehinde Olayemi

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