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Table 1 Related works on IDS using ML models

From: Performance analysis of machine learning models for intrusion detection system using Gini Impurity-based Weighted Random Forest (GIWRF) feature selection technique

Proposed by Used dataset Feature selection Algorithm Accuracy
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%