EURASIP Journal on Wireless Communications and Networking

Table 8 Experimental analysis results under different network traffic types

From: Fusion of transformer and ML-CNN-BiLSTM for network intrusion detection

Traffic name	AETMLCBAE	Q-learning	TCNN	MCRNN
Normal	0.935	0.889	0.901	0.911
Fuzzers	0.936	0.916	0.911	0.915
Worms	0.902	0.875	0.887	0.898
Shellcode	0.911	0.902	0.909	0.911
Generic	0.919	0.895	0.915	0.914
Reconnaissance	0.924	0.911	0.908	0.907
Backdoor	0.916	0.891	0.895	0.913
Exploits	0.921	0.889	0.886	0.912
DoS	0.935	0.892	0.899	0.916
Analysis	0.911	0.894	0.914	0.910

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