Optimization Enabled Deep Learning-Based DDoS Attack Detection in Cloud Computing

Table 3

Discussion with comparison of the proposed technique with existing techniques.

Classification types	Methods/metrics	TEHO-DBN	LUCID	Ensemble approach	DNN	SD-LVQ	FS-WOA	Proposed GHLBO-basedDSA
BOT-IoT with 90% learning data	Testing accuracy	0.824	0.846	0.873	0.899	0.902	0.905	0.917
	TPR	0.819	0.840	0.866	0.891	0.896	0.900	0.909
	TNR	0.831	0.842	0.860	0.891	0.903	0.905	0.909
NSL-KDD with 90% learning data	Testing accuracy	0.828	0.848	0.878	0.896	0.898	0.908	0.914
	TPR	0.828	0.848	0.871	0.891	0.894	0.899	0.909
	TNR	0.816	0.827	0.866	0.883	0.886	0.892	0.901

Bold values show higher performance compared to other methods.