|DARPA 98||Snort’s detection, 69% of total generated alerts are considered to be false alarms.||SIDS is applied without AIDS||Hu, et al. (2009)|
|ANN analysis system calls, 96% detection rate.||A classifier based on artificial neural network (ANN) has been executed for preparing and testing of framework.||McHugh (2000)|
|SVM on subset of DARPA 98, 99.6% detection rate.||SVM isolates information into various classes by a hyperplane or hyperplanes since it can deal with multidimensional information. SVM usually demonstrate good performance for a binary class problem.||Chen, et al. (2005)|
|KDDCUP 99||Multivariate statistical analysis of audit data, 90% detection rate||Multivariate is used to reduce false alarm rates.||Ye, et al. (2002), Hotta, et al. (2008)|
|The best results have been achieved by the C4.5 algorithm which attains the 95% true positive rate.||The decision trees created by C4.5 can be utilized for classification||Ferrari and Cribari-Neto (2004); Shafi and Abbass (2013); Laskov, et al. (2005)|
97% detection rate.
|This SVM based classifier with SMO implementation produces good detection accuracy. However, the accuracy reported is less than that in (Chen et al., 2005), because the KDDCUP 99 dataset is more complex and comprehensive than DARPA 98 dataset.||Shafi and Abbass (2013)|
|The best model is an HNB model, where 95% confidence level is used to compare the models.||Hidden Naïve Bayes (HNB) techniques could be applied to IDS area that suffer from dimensionality, highly associated attributes and high network speed. HNB technique is better than the one based on the traditional NB method in terms of detection accuracy for IDS.||Koc, et al. (2012)|
|NSL-KDD||K-Nearest Neighbour (k-NN) algorithm, the detection rate of 94%.||The k-NN algorithm uses all labelled training instances as a model of the target function. During the classification phase, k-NN uses a similarity-based search strategy to determine a locally optimal hypothesis function.||Adebowale, et al. (2013)|
|Naïve Bayes, the detection rate is 89%.||Bayesian classifiers provide moderate accuracy because the focus is on classifying the classes for the instances, not the exact probabilities.||Adebowale, et al. (2013)|
|C4.5 gave the best detection rate of 99%.||C4.5 selects the feature of the data that most efficiently divides its set of samples into subsets, contributing to improved accuracy||Thaseen and Kumar (2013)|
|SMO classifier, the detection rate is 97%.||The work also uses SVM based classifier and achieves detection rate similar to (Chen et al., 2005).||Adebowale, et al. (2013)|
|Expectation Maximization (EM) clustering, the accuracy is 78%||EM forms a “soft” task of each row to various clusters in percentage to the probability of each cluster. The accuracy in this method is low as EM does not give a parameter covariance matrix for standard errors||Ahmed, et al. (2016)|
Creech et al. have used Hidden Markov Model|
(HMM), Extreme Learning Machine (ELM) and SVM. They reported 74.3% accuracy for HMM, 98.57% accuracy for ELM and 99.64% accuracy for SVM.
The ADFA-WD is a much new data set and contains new attacks. This is why reported accuracy was not as good as for every machine learning technique when compared to the accuracy using legacy KDD98 data.|
SVM has been reported to produce the highest accuracy.
|Creech and Hu (2014b)|
|ADFA-LD||100% accuracy for using ELM using original semantic feature||New semantic features are applied. Therefore, ELM, are capable to use the new semantic feature easily and quickly by including amounts of semantic phrases.||Creech and Hu (2014b)|
|CICIDS2017||94.5% accuracy obtained by using MLP solely, by using MLP and Payload Classifier together 95.2% accuracy rate is detected.||Feature selection is done by using Fisher Score algorithm.||Usteba, et al. (2018)|
The highest accuracy from the SVM model.|
98% detection rate
|This SVM based method has produced good detection accuracy (Mitchell & Chen, 2015; Chen et al., 2005; Ferrari & Cribari-Neto, 2004)||Koroniotis, et al. (2018)|