A Residual Learning-Based Network Intrusion Detection System

Table 5

Comparison of results of recall with other classifiers.

Model	Testing metric R
	Analysis	Backdoors	DoS	Exploits	Fuzzers	Generic	Normal	Recon	Shellcode	Worms
RF	0	0	0.012	0.839	0.741	0.630	0.999	0.754	0.111	0
SVM	0.062	0.033	0.183	0.669	0.730	0.763	0.997	0.645	0.206	0.068
MLP	0.007	0.039	0.076	0.825	0.679	0.891	0.999	0.661	0.508	0.227
LSTM	0	0	0.008	0.853	0.793	0.747	0.998	0.683	0	0.045
LeNet-5	0	0	0	0.852	0.689	0.901	0.996	0.787	0.553	0
AlexNet	0	0.002	0.057	0.833	0.725	0.915	0.999	0.711	0.352	0.114
RLC-CNN	0	0.026	0.077	0.875	0.711	0.926	0.998	0.805	0.370	0
RLF-CNN	0.435	0.482	0.311	0.860	0.732	0.918	0.993	0.797	0.712	0.818