ITect: Scalable Information Theoretic Similarity for Malware Detection

Sukriti Bhattacharya; Hector D. Menendez; Earl Barr; David Clark

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arXiv · 1609.02404

ITect: Scalable Information Theoretic Similarity for Malware Detection

Sukriti Bhattacharya·Hector D. Menendez·Earl Barr·David Clark

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Abstract

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.

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BibTeX RIS

Sukriti Bhattacharya, Hector D. Menendez, Earl Barr, David Clark. 2016-09-08. ITect: Scalable Information Theoretic Similarity for Malware Detection. https://arxiv.org/abs/1609.02404

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