Incremental clustering of malware packers using features based on transformed CFG
Packer detection is an important topic because most malware is packed and this allows it to avoid detection based on static analysis. Identifying classes of packers is the key to effective detection because it makes it easier to determine from a static analysis whether further analysis is needed or whether a decision is already possible. Thus in this work we propose new features to cluster packers from their unpacking function. This method makes it possible to effectively cluster packers, and is able, by clustering, to identify packer classes used by malware. It is a step towards a larger data clustering allowing to identify custom packers.