A Data Mining Classification Approach for Behavioral Malware Detection

Table 1

The statistical analysis of data set 1 for specified classification methods.

Algorithms	⁢Results
	Correctly Classified Instances; Number, %	Incorrectly Classified Instances; Number, %	Mean absolute error	Relative absolute error	Kappa statistic	Root mean squared error	Root relative squared error	Total number of instances
NaiveBayes	1107, 27.5099%	2917, 72.4901%	0.0069	90.0871%	0.2526	0.0754	122.8107%	4024
BayesNet	2662, 66.1531%	1362, 33.8469%	0.0032	42.4047%	0.5979	0.0479	78.1282%	4024
IB1	2802, 69.6322%	1222, 30.3678%	0.0028	37.2325%	0.6199	0.0533	86.8274%	4024
J48	2908, 72.2664%	1116, 27.7336%	0.0032	41.6312%	0.6379	0.0454	73.9957%	4024
Regression	3051, 75.8201%	973, 24.1799%	0.0011	21.0201%	0.6859	0.0392	63.9686%	4024
SVM	2251, 64.1571%	1773, 35.8429%	0.0039	42.0019%	0.5743	0.4758	84.9596%	4024