Automatic generation of string signatures for malware detection

Abstract

Scanning files for signatures is a proven technology, but exponential growth in unique malware programs has caused an explosion in signature database sizes. One solution to this problem is to use string signatures, each of which is a contiguous byte sequence that potentially can match many variants of a malware family. However, it is not clear how to automatically generate these string signatures with a sufficiently low false positive rate. Hancock is the first string signature generation system that takes on this challenge on a large scale. To minimize the false positive rate, Hancock features a scalable model that estimates the occurrence probability of arbitrary byte sequences in goodware programs, a set of library code identification techniques, and diversity-based heuristics that ensure the contexts in which a signature is embedded in containing malware files are similar to one another. With these techniques combined, Hancock is able to automatically generate string signatures with a false positive rate below 0.1%. © 2009 Springer Berlin Heidelberg.