Mal-ONE: A unified framework for fast and efficient malware detection
Charles Ci-Wen Lim, Kalamullah Ramli · 2014
With continuous increase rate of malware growth, detecting malware using conventional, signature-based method has failed to detect new or unknown malware. The new proposed framework is able to detect evasive malware and integrate key static and dynamic features to detect malware more accurately and efficiently. Our early experiments, based on 1603 malware samples, showed that the proposed system can analyze malware with the rate of about 144 seconds per binary code analyzed. Mal-One framework exhibits comparable overall time taken to detect and analyze the binary code to determine whether a binary code is malware or benign.