Table 6 Result analysis of the ablation study of the IHDLM-CADEFST methodology.

From: An intelligent deep representation learning with enhanced feature selection approach for cyberattack detection in internet of things enabled cloud environment

Dataset

Approach

\(\:{A}{c}{c}{{u}}_{{y}}\)

\(\:{P}\varvec{r}{e}{{c}}_{{n}}\)

\(\:{R}{e}{c}{{a}}_{{l}}\)

\(\:{{F}1}_{{S}{c}{o}{r}{e}}\)

ToN-IoT dataset

RFE-IG

97.53

89.17

85.72

86.25

RMSProp

98.06

89.72

86.35

87.03

CNN-LSTM

98.67

90.23

87.02

87.77

IHDLM-CADEFST

99.45

91.01

87.61

88.51

Edge-IIoT dataset

RFE-IG

97.42

88.89

85.90

86.51

RMSProp

98.07

89.68

86.55

87.11

CNN-LSTM

98.65

90.42

87.11

87.78

IHDLM-CADEFST

99.45

91.01

87.61

88.51

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