Survey in Static Detection of Malware
Survey in Static Detection of Malware is based on the literature review in my 2010 Masters thesis.
Abstract - Malware continues to be a significant problem facing computer use in today’s world. Historically Antivirus software has employed the use of static signatures to detect instances of known malware. Signature based detection has fallen out of favour to many, and detection techniques based on identifying malicious program behavior are now part of the Antivirus toolkit. However, static approaches to malware detection have been heavily researched and can employ modern fingerprints that significantly improve on the simple string signatures used in the past. Instance- based learning can allow the detection of an entire family of malware variants based on a single signature of static features. Statistical machine learning can turn the features extracted into a predictive Antivirus system able to detect novel and previouslyunseen malware samples. This paper surveys the approaches and techniques used in static malware detection.