Empirical Evaluation of Noise Influence on Supervised Machine Learning Algorithms Using Intrusion Detection Datasets

Table 15

The results of running ML algorithms on a filtered and then injected intrusion dataset.

Noise (%)	ML algorithm	NSL-KDD			UNSW-NB15
		Accuracy	Precision	Recall	Accuracy	Precision	Recall
Baseline	DT (J48)	81.5339	0.858	0.815	100	1.000	1.000
	RF	80.4516	0.852	0.805	98.4903	0.985	0.985
	SVM	75.3948	0.802	0.754	100	1.000	1.000
	ANN	77.7147	0.817	0.777	100	1.000	1.000
	NB	76.1178	0.809	0.761	87.4435	0.884	0.874
Filtered then 5% noise injected	DT (J48)	80.6742	0.844	0.807	100	1.000	1.000
	RF	80.4869	0.746	0.805	96.3296	0.967	0.963
	SVM	80.4869	0.746	0.805	100	1.000	1.000
	ANN	80.5243	0.760	0.805	98.5001	0.985	0.985
	NB	80.4869	0.746	0.805	74.285	0.795	0.743
Filtered then 10% noise injected	DT (J48)	80.6742	0.844	0.807	100	1.000	1.000
	RF	80.2996	0.721	0.803	96.336	0.967	0.963
	SVM	80.4869	0.746	0.805	100	1.000	1.000
	ANN	80.6367	0.796	0.806	99.892	0.999	0.999
	NB	80.4869	0.746	0.805	74.2055	0.795	0.742
Filtered then 20% noise injected	DT (J48)	80.5618	0.843	0.806	71.4472	0.815	0.714
	RF	81.2734	0.777	0.813	92.656	0.938	0.927
	SVM	80.4869	0.746	0.805	99.9841	1.000	1.000
	ANN	80.6367	0.796	0.806	99.9682	1.000	1.000
	NB	80.4869	0.746	0.805	74.0784	0.794	0.741
Filtered then 30% noise injected	DT (J48)	78.5768	0.662	0.786	70.1824	0.797	0.702
	RF	80.9738	0.804	0.810	87.6001	0.897	0.876
	SVM	80.4869	0.746	0.805	99.9174	0.999	0.999
	ANN	80.7116	0.766	0.807	99.9078	0.999	0.999
	NB	80.4869	0.746	0.805	74.0721	0.794	0.741