Table 1 Related works on IDS using ML models

Khan et al. (2018)

UNSW-NB 15 dataset

Feature importance (RF)

XGBoost,

RF,

Bagging,

KNN,

DT

71.43%,

74.87%,

74.64%,

74.22%,

71.10%

Tama and Rhee (2019)

NSL KDD,

UNSW-NB 15 and GPRS

Complete feature

GBM

91.31% (UNSW-NB),

91.82% (KDDTest +),

86.51% (KDDTest-21)

Jing and Chen (2019)

UNSW-NB 15

All features

SVM

85.99% (binary classification),

75.77% (multiclass classification)

Kasongo and Sun (2020)

UNSW-NB 15

XGBoost algorithm

SVM,

Logistic Regression,

KNN,

DT,

ANN

60.89,

77.64,

84.46,

90.85,

84.39

Ingre and Yadav (2015)

NSL-KDD

filter-based

DT

83.66% (multiclass classification),

90.30% (binary classification)

Osanaiye et al. (2016)

NSL-KDD

Chi-Square, information gain, gain ratio, and relieff algorithm

DT

99.67%

Alazzam et al. (2020)

UNSW-NB 15,

NSL-KDD, KDDCup99

Sigmoid PIO

Cosine PIO

DT

DT

91.7% (UNSW-NB 15);

88.3% (NSL-KDD);

96% (KDDCup99);

91.3% (UNSW-NB)

86.9% (NSL-KDD)

94.7% (KDDCup99)

Aboueata et al. (2019)

UNSW-NB 15

Univariate and principal component analysis (PCA)

ANN, SVM

91%, 92%

Meftah et al. (2019)

UNSW-NB 15

Random forest

GBM,

LR,

SVM

61.83%,

77.21%,

82.11%

Injadat et al. (2020)

CICIDS 2017,

UNSW-NB 15

Correlation based (CBFS) and information gain (IGBFS)

KNN

RF

99% for both datasets

Belgrana et al. (2021)

NSL-KDD

Condensed nearest neighbors (CNN)

Radial basis function (RBF), CNN

94.28%,

95.54%

Lee et al. (2020)

CICIDS2017 dataset

Autoencoder

Deep sparse autoencoder random forest (DSAE-RF)

99.83%

Gu and Lu (2021)

UNSW-NB 15, CICIDS2017, NSL-KDD, and Kyoto 2006 + 

Feature transformation with Naïve Bayes

SVM

98.92% (CICIDS2017),

99.35% (NSL-KDD),

93.75% (UNSW NB15),

98.58% (Kyoto 2006 +)

Moustafa (2021)

Network TON_IoT

Wrapper feature selection technique-based RF

GBM

RF

DNN

93.83%,

99.98%,

99.92%