This work focuses on a specific front of the malware detection arms-race, namely the detection of persistent, disk-resident malware. We exploit normalised compression distance (NCD), an information theoretic measure, applied directly to binaries. Given a zoo of labelled malware and benign-ware, we ask whether a suspect program is more similar to our malware or to our benignware. Our approach classifies malware with 97.1% accuracy and a false positive rate of 3%. We achieve our results with off-theshelf compressors and a standard machine learning classifier and without any specialised knowledge. An end-user need only collect a zoo of malware and benign-ware and then can immediately apply our techniques. We apply statistical rigour to our experiments and our selection of data. We demonstrate that accuracy can be optimised by combining NCD with the compressibility rates of the executables. We demonstrate that malware reported within a more narrow time frame of a few days is more homogenous than malware reported over a longer one of two years but that our method still classifies the latter with 95.2% accuracy and a 5% false positive rate. Due to the use of compression, the time and computation cost of our method is non-trivial. We show that simple approximation techniques can improve the time complexity of our approach by up to 63%. We compare our results to the results of applying the 59 antimalware programs used on the VirusTotal web site to our malware. Our approach does better than any single one of them as well as the 59 used collectively.
For this work, we tried the implementation of another benchmark detector based on the information theoretic approach for malware detection, similar to the ones used by REF .
Detecting Malware with Information Complexity
ArXiv
ArXiv
Mathematics Computer Science