Table 3 Comparison analysis of CREA-HDLMOA model with existing approaches on CIC-IDS2017 dataset.

From: Advancements in cyberthreat intelligence through resource exhaustion attack detection using hybrid deep learning with heuristic search algorithms

CIC-IDS2017 dataset

Approach

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

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

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

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

J48 consolidated

91.94

93.39

92.02

93.59

LIBSVM

98.44

93.34

90.26

92.68

MLP Method

93.70

93.29

90.52

93.28

Naïve Bayes

91.20

90.89

91.57

90.83

CNN-LSTM

92.38

89.40

90.95

91.74

5-layer AE

98.97

90.09

92.32

93.18

XGBoost-SVM

97.51

91.21

90.49

91.91

CREA-HDLMOA

99.31

95.41

93.67

94.38

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