[Retracted] Software Defined Network Enabled Fog-to-Things Hybrid Deep Learning Driven Cyber Threat Detection System

Table 1

Comprehensive comparison of existing related work.

Ref	Year	Dataset	Algorithms	Findings
[7]	2018	ISCX, AWID	LSTM, LR	98.22% accuracy achieved in multiclass
[8]	2019	MAWI	RNN, CNN	98% accuracy achieved in multiclass
[9]	2020	NSL-KDD	ML and DL	99% accuracy achieved in multiclass
[10]	2020	NSL-KDD	Multilayered RNN	92.18% accuracy achieved in multiclass
[11]	2021	IoTID20	Exact greedy algorithm	84.4% accuracy achieved in multiclass
[12]	2018	5G data	CCA security model	Proposed model provides security using encryption method
[13]	2017	Coca-Cola dataset	AES algorithm	Data is secured through encryption
[14]	2016	CIDDS-01	NIDS	Data is protected through NIDS
[15]	2021	SDN port data	IoT-DDoS algorithm	DDoS SDN-enabled model successfully detects and prevents attacks
[16]	2021	Survey paper	IDS algorithms	Fog models detect attacks with low accuracy rate
[17]	2021	SOHO architecture data	DL algorithms	99.66% anomaly detection network accuracy rate in IEEE 802.11