Neural Networks and Rules-based Systems used to Find Rational and Scientific Correlations between being Here and Now with Afterlife Conditions
February 17, 2026
Neural Networks and Rules-based Systems used to Find Rational and
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The problems with the current researches on intrusion detection using data mining approach are that they try to minimize the error rate (make the classification decision to minimize the probability of error) by totally ignoring the cost that could be incurred. However, for many problem domains, the requirement is not merely to predict the most probable class label, since different types of errors carry different costs. Instances of such problems include authentication, where the cost of allowing unauthorized access can be much greater than that of wrongly denying access to authorized individuals, and intrusion detection, where raising false alarms has a substantially lower cost than allowing an undetected intrusion. In such cases, it is preferable to make the classification decision that has minimum cost, rather than that with the lowest error rate.For this reason, we examine how cost-sensitive machine learning methods can be used in Intrusion Detection systems. The performance of the approach is evaluated under different experimental conditions and different models in comparison with the KDD Cup 99 winner resultsin terms of average misclassification cost, as well as detection accuracy and false positive ratesthough the winner used original KDD dataset whereas for this research NSL-KDD dataset which is new version of the original KDD cup data and it is better than the original dataset in that it has no redundant data is used.
Adamu Teshome. 2014. \u201cA Cost Sensitive Machine Learning Approach for Intrusion Detection\u201d. Global Journal of Computer Science and Technology - C: Software & Data Engineering GJCST-C Volume 14 (GJCST Volume 14 Issue C6): .
Crossref Journal DOI 10.17406/gjcst
Print ISSN 0975-4350
e-ISSN 0975-4172
The methods for personal identification and authentication are no exception.
The methods for personal identification and authentication are no exception.
Neural Networks and Rules-based Systems used to Find Rational and
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The problems with the current researches on intrusion detection using data mining approach are that they try to minimize the error rate (make the classification decision to minimize the probability of error) by totally ignoring the cost that could be incurred. However, for many problem domains, the requirement is not merely to predict the most probable class label, since different types of errors carry different costs. Instances of such problems include authentication, where the cost of allowing unauthorized access can be much greater than that of wrongly denying access to authorized individuals, and intrusion detection, where raising false alarms has a substantially lower cost than allowing an undetected intrusion. In such cases, it is preferable to make the classification decision that has minimum cost, rather than that with the lowest error rate.For this reason, we examine how cost-sensitive machine learning methods can be used in Intrusion Detection systems. The performance of the approach is evaluated under different experimental conditions and different models in comparison with the KDD Cup 99 winner resultsin terms of average misclassification cost, as well as detection accuracy and false positive ratesthough the winner used original KDD dataset whereas for this research NSL-KDD dataset which is new version of the original KDD cup data and it is better than the original dataset in that it has no redundant data is used.
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