IoT Security Detection Method Based on Multifeature and Multineural Network Fusion

Table 6

Evaluating indicator.

Eigenvalue	Evaluation indicators	RNN (recurrent neural network) (%)	LSTM (Long short-term memory) neural network (%)	GRU (gated neural network) (%)
Normal (normal)	TPR (true positive rate)	99.785652	99.70653	99.785632
	FPR (false positive rate)	0.02629	0.02623	0.02645
	Precision	99.924538	99.536	99.924538
Key (whether the key is compromised)	TPR (true positive rate)	55.706935	55.84048	55.825778
	FPR (false positive rate)	0.24754	0.0365363	0.365378
	Precision	97.470417	96.322675	96.321742
FR (spoofing the router to join the network)	TPR (true positive rate)	94.716305	95.237292	94.703649
	FPR (false positive rate)	0.2544174	0.2869203	2.533927
	Precision	77.965484	75.980211	78.030935
Authentication (presence or absence of authentication mechanisms)	TPR (true positive rate)	97.770954	97.751925	97.995848
	FPR (false positive rate)	0.2576831	0.2512793	2.889814
	Precision	89.793535	90.01485	88.742412
NE (network data encryption status)	TPR (true positive rate)	96.295142	96.866101	97.52915
	FPR (false positive rate)	0.2466249	0.2996873	3.521875
	Precision	84.666841	82.114505	79.797605
ACL (access control)	TPR (true positive rate)	80.545442	80.666809	82.123224
	FPR (false positive rate)	0.079273	0.010239	0.35294
	Precision	96.534611	95.576045	86.475718
NODE (identify foreign nodes)	TPR (true positive rate)	42.371681	8.106195	14.584071
	FPR (false positive rate)	1.402514	0.0259163	0.452776
	Precision	14.449541	14.764668	15.158205
NOR (detects nonrepudiation of network node communications)	TPR (true positive rate)	25.150763	31.997162	1.631784
	FPR (false positive rate)	0.795674	0.104477	0.054798
	Precision	14.926316	14.55543	14.110429
Aggregate	acc (accuracy)	90.137387	90.13333	90.1364139