Table 9 Comparison of existing studies (utilizing ToN-IoT dataset) based on accuracy.
Year (Reference) | FS Algorithm | Classification technique | Selected Features | Accuracy |
|---|---|---|---|---|
202154 | Tabu Search | Random Forest | 16 | 83.12% |
202169 | Trustworthy Privacy-Preserving Secured Framework | XG-Boost 19 | 19 | 98.84% |
202170 | Chi2-SMOTE | XG-Boost 20 | 20 | 99.10% |
202271 | ReliefF | Neural Network (NN) | 20 | 98.39% |
202372 | Regularization Technique | Convolutional NN (CNN) | 8 | 97.94% |
202373 | Firefly Algorithm | CNN | 20 | 96.65% |
202374 | Non-dominated Sorting Genetic Algorithm (NSGA)-II based FS Scheme | KNN | 25 | 95% |
202375 | NSGA-II based FS Scheme | SVM | 18 | 98.86% |
202375 | (Hybrid) Filter + NSGA-II | SVM | 13 | 99.48% |
202451 | Pearson correlation coefficient | CNN | 85 | 98.75% |
Proposed Method | Enhanced Grey Wolf Optimization | RF | 23 | 99.93% |
25 (with standard CEC2014 benchmark) | 98.95% |
