Title :
Header information in malware families and impact on automated classifiers
Author :
Walenstein, Andrew ; Hefner, Daniel J. ; Wichers, Jeffery
Author_Institution :
Dept. of Comput. Sci., Univ. of Louisiana at Lafayette, Lafayette, LA, USA
Abstract :
The metadata embedded in program executables provides information that can be useful for automated malware detection or classification. With potentially tens of thousands of variants per malware family, it is unclear how much consistency there is in the metadata, and whether different families exhibit different consistencies. Header information from multiple variants of recent malware was studied to understand the variability of the header information within and among malware families. Classification accuracy extracted using multiple common classifiers showed that, even for rapidly mutating malware families, classifiers trained on header information can outperform ones trained on the program bodies. The results also show that some families have highly consistent header information; this fact suggests limited evolutionary pressure from defense systems. The results indicate that care is needed when evaluating classifiers operating on header as well as program body information.
Keywords :
information analysis; invasive software; meta data; pattern classification; automated classifier; automated malware detection; defense system; evolutionary pressure; header information; Accuracy; Data mining; Decision trees; Malware; Niobium; Storms; Support vector machines;
Conference_Titel :
Malicious and Unwanted Software (MALWARE), 2010 5th International Conference on
Conference_Location :
Nancy, Lorraine
Print_ISBN :
978-1-4244-9353-1
DOI :
10.1109/MALWARE.2010.5665799