Table 1 the summarizes of reviewed detection methods,datasets used and accuracies
References | Datasets | Techniques | Accuracy % |
|---|---|---|---|
Kim et al. [4] | KDD-99,CICIDS2018 | CNN,RNN | 99 |
Sabeel et al. [7] | CICIDS2017,ANTS2019 | DNN,LSTM | 99.68 |
Lee et al. [8] | NSL-KDD | DNN,STL,RNN | 98.9 |
Wu et al. [9] | NSL-KDD,UNSW-NB15 | CNN+LSTM,LuNet,RNN | 99.36 |
Almomani et al. [11] | WSN-DS | NB,DT,RF,SVM,J48,ANN,KNN,BN | 99.7 |
Vinayakumar et al. [12] | KDD-99,NSL-KDD ,WSN-DS,CICIDS2017,WSN-DS,CICIDS2017,Kyoto | DNN | 99.2 |
Park et al. [13] | WSN-DS | RF | 97.8 |
Abdullah et al. [14] | WSN-DS | SVM,NB,DT,RF | 96.7 |
Premkumar and Sundararajan [15] | WSN CH | RBF | 99 |
Asad et al. [16] | CICIDS2017 | ANN | 98 |
Loukas et al. [17] | malware (Net) | LSTM,LMP | 86.9 |
Shaaban et al. [18] | simulated network traffic and NSL-KDD | CNN | 99 |
Salmi and Oughdir [20] | WSN-DS | CNN+LSTM | 97 |
Wazirali and Ahmad [19] | WSN dataset | KNN,LR,SVM,Gboost,DT,LSTM,MLP | 99.6 |
Deshpande et al. [21] | WSN-DS | ANN,SVM,RF,KNN,LR,NB | 99 |