Table 4 Comparative outcome of XAICR-HDLOA approach under the Edge-IIoT dataset with existing models.

From: Explainable artificial intelligence-based cyber resilience in internet of things networks using hybrid deep learning with improved chimp optimization algorithm

Edge-IIoT dataset

Technique

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

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

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

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

RF

92.91

88.55

83.77

84.12

K-NN Algorithm

95.14

85.16

84.47

85.49

CNN Classifier

96.84

86.74

87.19

83.64

XGBoost

97.09

83.80

85.96

85.29

FFNN Method

93.60

87.14

89.04

84.57

MLP Model

94.73

87.67

84.42

85.39

SVM Method

92.31

83.37

89.86

83.01

XAICR-HDLOA

98.41

90.42

90.01

90.19

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