ClarAVy: A Tool for Scalable and Accurate Malware Family Labeling
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Abstract (arXiv)
Determining the family to which a malicious file belongs is an essential component of cyberattack investigation, attribution, and remediation. Performing this task manually is time consuming and requires expert knowledge. Automated tools using that label malware using antivirus detections lack accuracy and/or scalability, making them insufficient for real-world applications. Three pervasive shortcomings in these tools are responsible: (1) incorrect parsing of antivirus detections, (2) errors during family alias resolution, and (3) an inappropriate antivirus aggregation strategy. To address each of these, we created our own malware family labeling tool called ClarAVy. ClarAVy utilizes a Variational Bayesian approach to aggregate detections from a collection of antivirus products into accurate family labels. Our tool scales to enormous malware datasets, and we evaluated it by labeling ≈40 million malicious files. ClarAVy has 8 and 12 percentage points higher accuracy than the prior leading tool in labeling the MOTIF and MalPedia datasets, respectively.
Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). First posted on arXiv 4 Feb 2025.
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Citing papers per year: 2025: 4; 2026: 6.
10 papers.
- 2025EMBER2024 -- A Benchmark Dataset for Holistic Evaluation of Malware Classifierscited by 18
- 2025RawMal-TF: Raw Malware Dataset Labeled by Type and Familycited by 4
- 2025Zipf-Gramming: Scaling Byte N-Grams Up to Production Sized Malware Corporacited by 2
- 2025MASCOT: Analyzing Malware Evolution Through A Well-Curated Source Code Datasetcited by 2
- 2026Cybersecurity is the True Frontier for Generative AI Success or Failurecited by 2
- 2026Automated Malware Family Classification using Weighted Hierarchical Ensembles of Large Language Modelscited by 1
- 2026FARM: Few-shot Adaptive Malware Family Classification under Concept Driftcited by 0
- 2026Building an Adversarial Malware Dataset by Family and Type: Generation, Evasion, and Poisoning Evaluationcited by 0
- 2026MalTree: Tracing Malware Evolution from Embeddings at Scalecited by 0
- 2026Hamm-Grams: An Algorithm for Mining Regular Expressions of Bytescited by 0
- 2026Hamm-Grams: An Algorithm for Mining Regular Expressions of Bytes1 Jul 2026
- 2026Cybersecurity is the True Frontier for Generative AI Success or Failure27 Jun 2026
- 2026MalTree: Tracing Malware Evolution from Embeddings at Scale4 Jun 2026
- 2026Building an Adversarial Malware Dataset by Family and Type: Generation, Evasion, and Poisoning Evaluation25 May 2026
- 2026Automated Malware Family Classification using Weighted Hierarchical Ensembles of Large Language Models2 Apr 2026
- 2026FARM: Few-shot Adaptive Malware Family Classification under Concept Drift25 Jan 2026
- 2025MASCOT: Analyzing Malware Evolution Through A Well-Curated Source Code Dataset30 Nov 2025
- 2025Zipf-Gramming: Scaling Byte N-Grams Up to Production Sized Malware Corpora17 Nov 2025
- 2025RawMal-TF: Raw Malware Dataset Labeled by Type and Family30 Jun 2025
- 2025EMBER2024 -- A Benchmark Dataset for Holistic Evaluation of Malware Classifiers5 Jun 2025