Advertisement
Published in:
2017 | OriginalPaper | Chapter
Method of Detecting Malware Through Analysis of Opcodes Frequency with Machine Learning Technique
Authors : Sang-Uk Woo, Dong-Hee Kim, Tai-Myoung Chung
Published in: Advances in Computer Science and Ubiquitous Computing
Publisher: Springer Singapore
Activate our intelligent search to find suitable subject content or patents.
Select sections of text to find matching patents with Artificial Intelligence. powered by
Select sections of text to find additional relevant content using AI-assisted search. powered by
Abstract
As the evolution of malware, vast damages are occurred in various industry fields. For this reason, research on malware detection has conducted actively. To improve the security of the network, SDN Quarantined Network (SQN) has been proposed. In this paper, we developed one of malware detection modules in first quarantine station in SQN by using the fact that benign and malicious files have different opcode frequency. And we applied machine learning technique as different way compare to conventional method. we verified that our module is valuable as one of detection modules and our final aim is to mount this module on the SQN system. Therefore, it would be possible more accurate inspection for new type of security attack with multiple detection modules.