Table 2 Malware detection of MDMIoV-DLXAI model under different epochs.

From: Two stage malware detection model in internet of vehicles (IoV) using deep learning-based explainable artificial intelligence with optimization algorithms

Class labels

\(\:Acc{u}_{y}\)

\(\:Pre{c}_{n}\)

\(\:Rec{a}_{l}\)

\(\:{F1}_{score}\)

\(\:AU{C}_{score}\)

Epoch − 500

Benign

96.15

98.93

96.15

97.52

97.56

Malware

98.96

96.26

98.96

97.59

97.56

Average

97.56

97.60

97.56

97.56

97.56

Epoch − 1000

Benign

97.22

97.99

97.22

97.60

97.61

Malware

98.00

97.25

98.00

97.62

97.61

Average

97.61

97.62

97.61

97.61

97.61

Epoch − 1500

Benign

96.51

98.95

96.51

97.72

97.75

Malware

98.98

96.60

98.98

97.77

97.75

Average

97.75

97.78

97.75

97.75

97.75

Epoch − 2000

Benign

96.46

99.23

96.46

97.83

97.86

Malware

99.26

96.56

99.26

97.89

97.86

Average

97.86

97.90

97.86

97.86

97.86

Epoch − 2500

Benign

97.42

98.08

97.42

97.75

97.76

Malware

98.10

97.44

98.10

97.77

97.76

Average

97.76

97.76

97.76

97.76

97.76

Epoch − 3000

Benign

96.82

99.02

96.82

97.90

97.93

Malware

99.04

96.89

99.04

97.95

97.93

Average

97.93

97.95

97.93

97.93

97.93