Table 2.

The present state of the art in the field of intrusion detection research.

Key Method	References	Form of Benchmark Data	The Categories/Type of Intrusion in the IDS Dataset	Performance Measuring Criteria
Single technique-based recurrent neural network	[16]	Standard IDS data set	Probe, DoS, U2R and R2L	Precision, positive detection rate, false-positive rate.
	[17]	Standard IDS data set	SMTP, HTTP web, IAMP, TCP, ICMP, secure web, misapplication, IRC, Flow-Gen, ICMP, and DNS	Recall, F1-score, precision, AUC error rate, accuracy.
Machine learning methods	[18]	Standard IDS data set	Probe, DoS, U2R and R2L	True positive rate, accuracy, F1-score, precision, and recall.
	[19]	A real-time IDS data set	Probe, DoS, U2R and R2L	Accuracy, TPR, FP, TN, precision, TNR, recall.
	[20]	Standard IDS data set	Threats, malware, cyber threats	Accuracy and precision.
Evolutionary Machine Learning methods	[21]	Standard IDS data set	DoS, R2L, U2R and probe	Precision, recall true positive: false negative, false positive, true negative.
	[22]	Standard IDS data set	DoS, R2L, U2R and probe, SYN, threats, and DDoS	Precision, TPR, F1-score, accuracy.