Neural Network-Based Voting System with High Capacity and Low Computation for Intrusion Detection in SIEM/IDS Systems

Table 1

Summary of the literature survey.

Reference	Model	Dataset	Accuracy (%)
El Hajji et al. [9]	NN with best cost and training function	NSL-KDD	81.8
Javaid et al. [10]	Sparse autoencoder with SMR	NSL-KDD	78.06
Gurung et al. [11]	Sparse autoencoder with LR	NSL-KDD	87.2
Yin et al. [12]	RNN based IDS	NSL-KDD	81.29
Chouhan et al. [13]	CBR-CNN based IDS	NSL-KDD	89.41
Maddikunta et al. [14]	DNN with PCA-GWO	Kaggle dataset	99.9
Suarez-Tangil et al. [15]	NN and GP	–	–
Ussath et al. [16]	RNN and NN	—	89
Chiba et al. [17]	NN	KDD	99.62
Bhattacharya et al. [18]	PCA-firefly based XGBoost	Kaggle dataset	99.9
Gadekallu et al. [19]	Naive Bayes	Kaggle and CERT-In repositories	99.9