A Novel Framework Design of Network Intrusion Detection Based on Machine Learning Techniques

Table 7

Evaluation results for various types of traffic under multiclass classification.

	Accuracy: 0.9990
	Precision	Recall	F1-score
Bengin	0.9996	0.9995	0.9995
Bot	0.9142	0.8492	0.8805
DDos	0.9999	0.9997	0.9998
Dos goldeneye	0.9984	0.9793	0.9887
Dos hulk	0.9963	0.9993	0.9978
Dos slow http test	0.9963	0.9891	0.9927
Dos slow loris	0.9960	0.9908	0.9934
FTP-Patator	1.0000	0.9983	0.9992
Heartbleed	1.0000	0.3333	0.5000
Infiltration	1.0000	0.2727	0.4286
PortScan	0.9997	0.9999	0.9998
SSH-patator	0.9943	0.9853	0.9898
Web attack-brute force	0.6808	0.8872	0.7704
Web attack-sql injection	1.0000	0.1667	0.2857
Web attack-XSS	0.4655	0.1378	0.2126
Weighted avg	0.9989	0.9990	0.9989