Article

Classification Technique

Results

Accuracy

TT (sec)

prediction

TP

TN

FP

FN

Error rate

Precision

Recall

DR

FAR

3: Detecting anomaly based network intrusion using feature extraction and classification techniques

Decision Tree

95.09%

1.032

0.003

4649

2702

279

100

4.9

94.34%

97.34%

.

,

MLP

92.46%

20.59

0.004

4729

2419

562

29

7.54

89.38

99.56%

KNN

92.78%

82.956

13.24

4726

2446

535

23

7.22

89.83%

99.52%

Linear SVM

92.59%

78.343

2.11

4723

2434

547

26

7.41

89.62%

99.45%

Passive aggressive

90.34

0.275

0.001

4701

2282

699

48

9.66

89.62%

99.45%

RBF SVM

91.67%

99.47

2.547

4726

2960

621

23

8.33

89.39%

99.52%

Random Forest

93.62%

1.189

0.027

4677

2560

621

23

6.38

91.74%

98.48%

AdaBoost

93.52%

29.556

0.225

4676

2553

428

73

6.48

91.61%

98.46%

Gausian NB

94.35%

244

0.006

4642

2651

330

107

5.65

93.36%

97.75%

-

-

MultionmINB

91.71%

0.429

0.001

4732

2357

624

17

8.29

88.35%

99.64%

-

-

Adratic Discriminat Ana

93.23%

1.305

0.0019

4677

2530

451

72

6.77

91.20%

84.87%

-

-