A Critical Review of Artificial Intelligence Based Approaches in Intrusion Detection: A Comprehensive Analysis

Table 2

Critical review of deep learning based approaches in ID.

Ref	Authors	Year	Cited by	DL approach	Accuracy (%)
[48]	Yin et al.	2017	1323	RNN-IDS	97.09
[49]	Vani	2017	19	LSTM based ensemble method	92.3
[50]	Wang et al.	2017	428	Hierarchical spatial-temporal features-based IDS (HAST-IDS) with CNN	99.89
[51]	Loukas et al.	2017	211	RNN	86.9
[52]	Shone et al.	2018	1046	NDAE	97.85
[53]	Lee et al.	2018	51	Autoencoder	98.9
[54]	Al-Qatf et al.	2018	334	Self-taught learning (STL)-IDS	99.41
[55]	Ding and Zhai	2018	91	CNN	80.13
[56]	Parampottupadam and Moldovann	2018	27	Deep learning H2O (binomial and multinomial models)	99.98
[57]	Xin et al.	2018	756	CNN	99.41
[58]	Faker and Dogdu	2019	140	DNN	99.16
[59]	Laqtib et al.	2019	15	CNN	77
[60]	Ge et al.	2019	121	FFNN	82
[61]	Khan et al.	2019	237	Two-stage deep learning (TSDL) model	99.31
[62]	Gurung et al.	2019	80	Auto-encoders	87.2
[63]	Su et al.	2020	153	BAT model	84.25
[64]	Gamage and Samarabandu	2020	156	ANN	98.25
[65]	Boukhalfa et al.	2020	30	LSTM	99.93
[66]	Shende and Thorat	2020	8	LSTM	96.92
[67]	Kocher and Kumar	2021	28	ANN	99.4
[68]	Mighan and Kahani	2021	65	ANN	98.51
[69]	Ashiku and Dagli	2021	33	DNN	95.6
[70]	Salih et al.	2021	20	Bayesian CNN	99.3271
[71]	Imrana et al.	2021	68	Bidirectional (BiDLSTM)	94.26
[72]	Otoum et al.	2022	115	DL-IDS	99
[73]	Nasir et al.	2022	13	DF-IDS	99.9
[74]	Jasim	2022	23	Deep belief networks (DBNs)	99
[75]	Akshay Kumaar et al.	2022	5	DL-based hybrid framework “ImmuneNet”	99.2
[76]	Houda et al.	2022	13	Explainable artificial intelligence (XAI) based DL framework	99
[77]	Chaganti et al.	2023	0	LSTM	97.1
[78]	Figueiredo et al.	2023	0	LSTM	99
[79]	Rizvi et al.	2023	2	1D-dilated causal neural network (1D-DCNN)	99.98