Analysis of Malware Detection Using Naive Bayes Algorithm Comparing Support Vector Machine Algorithm.

Abstract

Aim:  To enhance the accuracy in Detection of Malware in Analysis of Novel Malware Detection Using  Naive Bayes Algorithm comparing Support Vector Machine Algorithm. Materials and Methods: This study contains two groups one is the Novel Naive Bayes Algorithm comparing Support Vector Machine Algorithm.  Each group consists of a sample size of 30. Their accuracies are compared with each other using different sample sizes also. Results: SPSS was used to calculate the sample size. The pre-test analysis was maintained at 80%. G-power is used to calculate sample size. The  Support Vector Machine Algorithm is 64.1% more accurate than the Naive Bayes Algorithm of 62.8% in classifying the malware Detection. There is a statistically insignificant distinction in accuracy for 2Algorithms is p>0.05 by performing independent samples t-tests which is 0.206. Conclusion: Through this, prediction is done for  The Naive Bayes Algorithm is significantly better than the Support Vector Machine (SVM) in identifying Malware detection. It can be also considered as a better option for the classification of malware detection.