An Innovative Method in Improving the accuracy in Intrusion detection by comparing Random Forest over Support Vector Machine

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Abstract

Improving the accuracy of intruders in innovative Intrusion detection by comparing Machine Learning classifiers such as Random Forest (RF) with Support Vector Machine (SVM). Two groups of supervised Machine Learning algorithms acquire perfection by looking at the Random Forest calculation (N=20) with the Support Vector Machine calculation (N=20)G power value is 0.8. Random Forest (99.3198%) has the highest accuracy than the SVM (9S.56l5%) and the independent T-test was carried out (=0.507) and shows that it is statistically insignificant (p >0.05) with a confidence value of 95% by comparing RF and SVM. Conclusion: The comparative examination displays that the Random Forest is more productive than the Support Vector Machine for identifying the intruders are significantly tested.

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