| Title | An Innovative Method in Classifying and predicting the accuracy of intrusion detection on cybercrime by comparing Decision Tree with Support Vector Machine |
| Publication Type | Conference Paper |
| Year of Publication | 2022 |
| Authors | Kumar, Marri Ranjith, K.Malathi, Prof. |
| Conference Name | 2022 International Conference on Business Analytics for Technology and Security (ICBATS) |
| Date Published | feb |
| Keywords | Accuracy, Classification algorithms, composability, deep learning., Innovative Decision Tree, intrusion, Intrusion detection, machine learning, machine learning algorithms, Metrics, network intrusion, Prediction algorithms, Predictive models, pubcrawl, resilience, Resiliency, supply vector machines, support vector machine, Support vector machines |
| Abstract | Classifying and predicting the accuracy of intrusion detection on cybercrime by comparing machine learning methods such as Innovative Decision Tree (DT) with Support Vector Machine (SVM). By comparing the Decision Tree (N=20) and the Support Vector Machine algorithm (N=20) two classes of machine learning classifiers were used to determine the accuracy. The decision Tree (99.19%) has the highest accuracy than the SVM (98.5615%) and the independent T-test was carried out (=.507) and shows that it is statistically insignificant (p\textgreater0.05) with a confidence value of 95%. by comparing Innovative Decision Tree and Support Vector Machine. The Decision Tree is more productive than the Support Vector Machine for recognizing intruders with substantially checked, according to the significant analysis. |
| DOI | 10.1109/ICBATS54253.2022.9759021 |
| Citation Key | kumar_innovative_2022-1 |
An Innovative Method in Classifying and predicting the accuracy of intrusion detection on cybercrime by comparing Decision Tree with Support Vector Machine