Table 53 Quantitative analysis : proposed vs. baseline.

Model	Train %	Accuracy	Precision	Recall (Sensitivity)	F1-Score	FPR	FNR	ROC-AUC
LR	70%	0.9256	0.9191	0.936	0.9278	0.081	0.064	0.9375
Random Forest	70%	0.9578	0.9458	0.9706	0.958	0.0548	0.0294	0.9579
SVM (Linear)	70%	0.9557	0.9408	0.9734	0.9573	0.0589	0.0273	0.9572
CNN	70%	0.9482	0.9387	0.9603	0.9498	0.0625	0.0397	0.9556
BiLSTM	70%	0.9544	0.9398	0.9707	0.9543	0.0613	0.0294	0.956
VANET-DDoSNet + +	70%	0.9804	0.987	0.987	0.987	0.0143	0.014	0.9906
LR	80%	0.9378	0.9261	0.9412	0.9337	0.072	0.058	0.9416
Random Forest	80%	0.9603	0.9493	0.9733	0.9617	0.0516	0.0272	0.9624
SVM (Linear)	80%	0.9589	0.9443	0.9779	0.9573	0.0567	0.0241	0.9636
CNN	80%	0.9501	0.9404	0.9643	0.952	0.0609	0.0357	0.9517
BiLSTM	80%	0.9557	0.9411	0.9728	0.9574	0.0593	0.0272	0.9568
VANET-DDoSNet++	80%	0.9918	0.9915	0.9915	0.9915	.0077	0.0085	0.997

Quick links

Search