COMPARATIVE ANALYSIS OF FIREWALL RULE SET USING CLASSIFICATION ALGORITHMS
Mohd Fazzly Rassis bin Md Kasim, Mohamad Fadli Zolkipli · 2019
This study focuses on comparative analysis of firewall rule set using classification algorithms basedon the fundamental concept of data mining to evaluate the accuracy and performance of severalclassification algorithms. Rule sets grow to large numbers written by different network administrators.This condition will cause increase the rule set policy and complexity poses problem among otherinconsistencies in the firewall configuration. This led to firewall poses overload and used highprocess performance. The Knowledge Discovery in Database (KDD) is adopted as researchmethodology to illustrate how this study was conducted. In this study, classification algorithmsnamely JRIP, J48, Naive Bayes, Random tree and Random forest were used for the classification ofdataset. Waikato Environment for Analysis Knowledge (WEKA) was used in comparing thesealgorithms. Two firewall dataset were used, KUIPSAS 1098 dataset and PSDC 1024 dataset astraining and testing data on different classification algorithms. The experiment used dataset that havebeen formatted into ARFF 10 folds cross validation and the results were compared for accuracy.Based on the comparative analysis, it can be concluded that using two different datasets fromdifferent sources indicated that the Random Tree algorithm shows the best performance in terms ofaccuracy which are 99.70% for PSDC and 99.80% for KUIPSAS.