Abstract:
As companies in every industry sector around the
globe have lost, stolen or leaked their sensitive data to
the outside world every year, the security of
confidential information is increasingly important.
Remote Access Trojans (RATs) are used to invade a
victim’s PC through targeted attacks. In the previous
works features for detection of RATs were selected by
the author who may be an expert in the related
domain, and any feature selection method was not
used. In this paper one of the feature selection
methods, Information Gain is applied for evaluating
and ranking features. It aims not only to reduce costs
and resources for building detection system of remote
Access Trojan but also to add the advantages of using
feature selection method and propose new features
while maintaining high accuracy. Our approach
achieves 99% accuracy together with the FNR of
0.091 by Decision Trees algorithm, and this
experimental result shows that our proposed features
are effective to detection system of RATs.
