On Normalized Compression Distance and Large Malware
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Abstract (arXiv)
Normalized Compression Distance (NCD) is a popular tool that uses compression algorithms to cluster and classify data in a wide range of applications. Existing discussions of NCD's theoretical merit rely on certain theoretical properties of compression algorithms. However, we demonstrate that many popular compression algorithms don't seem to satisfy these theoretical properties. We explore the relationship between some of these properties and file size, demonstrating that this theoretical problem is actually a practical problem for classifying malware with large file sizes, and we then introduce some variants of NCD that mitigate this problem.
Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). First posted on arXiv 2 Sep 2015.
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Citing papers per year: 2021: 1; 2022: 0; 2023: 1; 2024: 0; 2025: 2; 2026: 1.
5 papers.
- 2021Black-Box Testing of Deep Neural Networks Through Test Case Diversitycited by 18
- 2023Recasting Self-Attention with Holographic Reduced Representationscited by 14
- 2025Zipf-Gramming: Scaling Byte N-Grams Up to Production Sized Malware Corporacited by 2
- 2025Context Steering: A New Paradigm for Compression-based Embeddings by Synthesizing Relevant Information Featurescited by 0
- 2026An Introduction to Compression-Based Machine Learningcited by 0
- 2026An Introduction to Compression-Based Machine Learning18 Sep 2026
- 2025Zipf-Gramming: Scaling Byte N-Grams Up to Production Sized Malware Corpora17 Nov 2025
- 2025Context Steering: A New Paradigm for Compression-based Embeddings by Synthesizing Relevant Information Features20 Aug 2025
- 2023Recasting Self-Attention with Holographic Reduced Representations31 May 2023
- 2021Black-Box Testing of Deep Neural Networks Through Test Case Diversity20 Dec 2021