Detecting malware with information complexity

Malware concealment is the predominant strategy for malware propagation. Black hats create variants of malware based on polymorphism and metamorphism. Malware variants, by definition, share some information. Although the concealment strategy alters this information, there are still patterns on the software. Given a zoo of labelled malware and benign-ware, we ask whether a suspect program is more similar to our malware or to our benign-ware. Normalized Compression Distance (NCD) is a generic metric that measures the shared information content of two strings. This measure opens a new front in the malware arms race, one where the countermeasures promise to be more costly for malware writers, who must now obfuscate patterns as strings qua strings, without reference to execution, in their variants. Our approach classifies disk-resident malware with 97.4% accuracy and a false positive rate of 3%. We demonstrate that its accuracy can be improved by combining NCD with the compressibility rates of executables using decision forests, paving the way for future improvements. We demonstrate that malware reported within a narrow time frame of a few days is more homogeneous than malware reported over two years, but that our method still classifies the latter with 95.2% accuracy and a 5% false positive rate. Due to its use of compression, the time and computation cost of our method is nontrivial. We show that simple approximation techniques can improve its running time by up to 63%. We compare our results to the results of applying the 59 anti-malware programs used on the VirusTotal website to our malware. Our approach outperforms each one used alone and matches that of all of them used collectively

Malware: the never-ending arm race

"Antivirus is death"' and probably every detection system that focuses on a single strategy for indicators of compromise. This famous quote that Brian Dye --Symantec's senior vice president-- stated in 2014 is the best representation of the current situation with malware detection and mitigation. Concealment strategies evolved significantly during the last years, not just like the classical ones based on polimorphic and metamorphic methodologies, which killed the signature-based detection that antiviruses use, but also the capabilities to fileless malware, i.e. malware only resident in volatile memory that makes every disk analysis senseless. This review provides a historical background of different concealment strategies introduced to protect malicious --and not necessarily malicious-- software from different detection or analysis techniques. It will cover binary, static and dynamic analysis, and also new strategies based on machine learning from both perspectives, the attackers and the defenders