Mal-Netminer: Malware Classification Approach Based on Social Network Analysis of System Call Graph

Table 7

Comparison of classification accuracies and AUC for 3,615 types of malware and 153 benign samples.

Classifier	Accuracy			AUC
	Dataset_1	Dataset_2	Dataset_3	Dataset_1	Dataset_2	Dataset_3
Naïve Bayes	99.96	90.19	95.16	1.00	0.95	0.98
Boosted NB	99.92	92.56	96.12	1.00	0.93	0.96
RIPPER	99.55	94.61	96.88	0.98	0.92	0.94
Boosted RIPPER	99.61	93.52	97.43	1.00	0.96	0.99
RBF	99.86	90.88	92.95	1.00	0.90	0.96
Boosted RBF	99.97	92.96	96.47	1.00	0.96	0.98
C4.5	99.39	92.87	96.78	0.98	0.87	0.95
Boosted C4.5	99.71	93.32	97.58	1.00	0.95	0.99
-NN	99.13	93.05	96.20	1.00	0.96	0.98
Boosted -NN	99.46	92.09	96.25	0.99	0.95	0.97

Dataset_1: adware + benign, Dataset_2: Trojan + benign, and Dataset_3: worm + benign.