Neural Networks and Rules-based Systems used to Find Rational and Scientific Correlations between being Here and Now with Afterlife Conditions
Neural Networks and Rules-based Systems used to Find Rational and
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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.
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): .
Crossref Journal DOI 10.17406/gjcst
Print ISSN 0975-4350
e-ISSN 0975-4172
The methods for personal identification and authentication are no exception.
Total Score: 101
Country: Nigeria
Subject: Global Journal of Computer Science and Technology - E: Network, Web & Security
Authors: Olasehinde Olayemi (PhD/Dr. count: 0)
View Count (all-time): 248
Total Views (Real + Logic): 3230
Total Downloads (simulated): 25
Publish Date: 2026 01, Fri
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Neural Networks and Rules-based Systems used to Find Rational and
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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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