Comparative Study of Different Models before Feature Selection and AFTER Feature Selection for Intrusion Detection

Pant, Janmejay; Pant, Bhaskar; Juyal, Amit

doi:10.5120/17251-7591

Cited by 3 publications

(3 citation statements)

References 18 publications

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“…Our experiment is based on NSL-KDD data set of intrusion detection [8]. NSL-KDD data set is used to perform the experiments through the WEKA.…”

Section: Experimental Work and Resultsmentioning

confidence: 99%

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Comparative Analysis of Naïve Bayes and J48 Algorithms on Intrusion Detection System (IDS)

Kotlia¹,

Pant²

2019

IJCA

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Intrusion detection system (IDS) is the science of detecting malicious activity across the computer network, as it is expanding, there is a challenge to compete with the malicious users or intruders who can easily break into the system .This paper put a light on performance analysis by J48 and Naïve Bayes algorithm to detect the error in intrusion detection system (IDS). Where Naïve bayes algorithm is based on probability and j48 algorithm is based on decision tree. The paper set out to make the comparative study of algorithms Naïve Bayes and J48 in the IDS data set to maximize the True Positive Rate and minimize the False Positive Rate using the WEKA Tool. The paper is showing the experimental result about classification of accuracy, sensitivity and specificity on data set of IDS and also shows that J48 algorithm is much better than that of Naïve Bayes algorithm in terms of precision and accuracy.

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“…Our experiment is based on NSL-KDD data set of intrusion detection [8]. NSL-KDD data set is used to perform the experiments through the WEKA.…”

Section: Experimental Work and Resultsmentioning

confidence: 99%

“…NSL-KDD data set is used to perform the experiments through the WEKA. It consists of a good and reasonable proportion of various types of records [8].…”

Section: Experimental Work and Resultsmentioning

confidence: 99%

Comparative Analysis of Naïve Bayes and J48 Algorithms on Intrusion Detection System (IDS)

Kotlia¹,

Pant²

2019

IJCA

Self Cite

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“…The advantage of this classification method is that it can be used for predictor variables on a categorical or continuous scale and it is also capable of handling very large data [2], hence in practice this method is so popular". "In the real world, the main problem that poses a challenge in classification methods is class imbalance, which has attracted the attention of academicians and researchers in recent years" [3]. "Class imbalance occurs if there is an unequal number between classes contained in a data set (unbalanced data distribution)" [4].…”

Section: Introductionmentioning

confidence: 99%

Bayesian Additive Regression Trees for Classification of Unbalanced Class of Credit Collectability Data

Naufal,

Efendi,

Sumarminingsih

2023

AJPAS

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Aims: This study aims at determining the classification results using the Bayesian Additive Regression Trees (BART) method on bank credit collectability data, where there is a class imbalance in the data. Study Design: Quantitative Design. Place and Duration of Study: The used data are secondary data in the form of bank debtor’s credit collectability data with nine predictor variables and one response variable in the form of credit collectability. They are collected from Banks in East Java, Indonesia, from the date of 01 May 1986 to 31 May 2018. Methodology: The Bayesian approach is one of the estimation methods in statistics that is currently being popularly used, this is because the rapid development of technology makes computational challenges no longer a problem. The Bayesian estimation continues to develop and can be used in various statistical methods, for instance both for regression and classification. The Classification and Regression Trees (CART) method is one of the most popular classification methods used. Debtors, in a bank, who have delinquent credit have a small proportion compared to debtors who have current credit. Standard classifier methods such as CART are not suitable for handling this case, as CART is sensitive to classes that have a high degree. Hence, additional methods such as ensemble BART (Bayesian Additive Regression Trees), are needed in order to increase the accuracy of classification in cases of class imbalance. Results: The results of the cross-validation on the BART show a high consistency of classification accuracy, 83.49%. This indicates that the BART method can work consistently even though there is a class imbalance. The results of this study indicate that the classification accuracy of the training data is 84.53%, while the accuracy in the testing data is 85.48%. These results also show that the BART method has ability to overcome overfitting in the classification method, where overfitting often occurs in most of the classification methods that have very good classification abilities. Conclusion: The testing data show that the accuracy is relatively similar to the one of the training data, this indicates that the BART method has been able to capture patterns in the data.

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Analysis of agricultural crop yield prediction using statistical techniques of machine learning

Pant

Singh

et al. 2021

Materials Today: Proceedings

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Comparative Study of Different Models before Feature Selection and AFTER Feature Selection for Intrusion Detection

Cited by 3 publications

References 18 publications

Comparative Analysis of Naïve Bayes and J48 Algorithms on Intrusion Detection System (IDS)

Comparative Analysis of Naïve Bayes and J48 Algorithms on Intrusion Detection System (IDS)

Bayesian Additive Regression Trees for Classification of Unbalanced Class of Credit Collectability Data

Analysis of agricultural crop yield prediction using statistical techniques of machine learning

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