Malware creators have been getting their way for too long now. String-based similarity measures can leverage ground truth in a scalable way and can operate at a level of abstraction that is difficult to combat from the code level. We introduce ITect, a scalable approach to malware similarity detection based on information theory. ITect targets file entropy patterns in different ways to achieve 100% precision with 90% accuracy but it could target 100% recall instead. It outperforms VirusTotal for precision and accuracy on combined Kaggle and VirusShare malware.
@article{arxiv.1609.02404,
title = {ITect: Scalable Information Theoretic Similarity for Malware Detection},
author = {Sukriti Bhattacharya and Hector D. Menendez and Earl Barr and David Clark},
journal= {arXiv preprint arXiv:1609.02404},
year = {2016}
}