Table 4 The results of multi-classification using features selected by the MCP algorithm.

Method	NF-CSE-CIC-IDS2018-v2			NF-ToN-IoT-v2			NF-UNSW-NB15-v2			NF-BoT-IoT-v2			CIC-ToN-IoT
Method	num	F1	Acc	num	F1	Acc	num	F1	Acc	num	F1	Acc	num	F1	Acc
Fast-mRMR	33	0.968	0.972	35	0.943	0.935	36	0.983	0.984	23	0.925	0.917	43	0.829	0.861
CMA-ES	21	0.753	0.630	26	0.947	0.945	22	0.975	0.976	17	0.976	0.976	36	0.831	0.853
GA	23	0.918	0.922	26	0.945	0.944	25	0.971	0.971	22	0.978	0.978	41	0.823	0.856
PSO	20	0.811	0.851	25	0.946	0.947	19	0.971	0.973	24	0.974	0.974	39	0.818	0.850
Leevy et al.²⁴	9	0.960	0.975	9	0.620	0.705	9	0.960	0.972	9	0.830	0.837	9	0.820	0.870
MCP	9	0.975	0.977	7	0.949	0.949	9	0.978	0.977	9	0.967	0.968	13	0.830	0.849
MCP(FL)	9	0.984	0.984	7	0.956	0.956	9	0.992	0.992	9	0.988	0.988	13	0.825	0.873

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