| Title | An Innovative Method in Improving the accuracy in Intrusion detection by comparing Random Forest over Support Vector Machine |
| Publication Type | Conference Paper |
| Year of Publication | 2022 |
| Authors | Kumar, Marri Ranjith, Malathi, K. |
| Conference Name | 2022 International Conference on Business Analytics for Technology and Security (ICBATS) |
| Date Published | feb |
| Keywords | Accuracy, Analytical models, attacks, composability, Forestry, Innovative Support Vector Machine, Intrusion detection, machine learning algorithms, Metrics, Performance, pubcrawl, Radio frequency, Random Forest, recognition, resilience, Resiliency, security, supervised machine learning, supply vector machines, Support vector machines |
| 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 \textgreater0.05) 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. |
| DOI | 10.1109/ICBATS54253.2022.9759062 |
| Citation Key | kumar_innovative_2022 |
An Innovative Method in Improving the accuracy in Intrusion detection by comparing Random Forest over Support Vector Machine