mlsanityData to 14 Oct 2026

MalVol-25: A Diverse, Labelled and Detailed Volatile Memory Dataset for Malware Detection and Response Testing and Validation

arXiv 2507.03993 ↗First posted 5 Jul 2025cs.CR cs.ET cs.LGcited by 0

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Abstract (arXiv)

This paper addresses the critical need for high-quality malware datasets that support advanced analysis techniques, particularly machine learning and agentic AI frameworks. Existing datasets often lack diversity, comprehensive labelling, and the complexity necessary for effective machine learning and agent-based AI training. To fill this gap, we developed a systematic approach for generating a dataset that combines automated malware execution in controlled virtual environments with dynamic monitoring tools. The resulting dataset comprises clean and infected memory snapshots across multiple malware families and operating systems, capturing detailed behavioural and environmental features. Key design decisions include applying ethical and legal compliance, thorough validation using both automated and manual methods, and comprehensive documentation to ensure replicability and integrity. The dataset's distinctive features enable modelling system states and transitions, facilitating RL-based malware detection and response strategies. This resource is significant for advancing adaptive cybersecurity defences and digital forensic research. Its scope supports diverse malware scenarios and offers potential for broader applications in incident response and automated threat mitigation.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 6 of its 24 references that are in the map. First posted on arXiv 5 Jul 2025.

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6 of 24 references.

References · 6

  1. 2018Learning to Evade Static PE Machine Learning Malware Models via Reinforcement Learningcited by 61
  2. 2021MalNet: A Large-Scale Image Database of Malicious Softwarecited by 11
  3. 2024AI-Enabled System for Efficient and Effective Cyber Incident Detection and Response in Cloud Environmentscited by 7
  4. 2024Advanced Persistent Threats (APT) Attribution Using Deep Reinforcement Learningcited by 6
  5. 2021Virus-MNIST: A Benchmark Malware Datasetcited by 5
  6. 2025MalVis: A Large-Scale Image-Based Framework and Dataset for Advancing Android Malware Classificationcited by 2

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  1. 2024Reinforcement Learning for an Efficient and Effective Malware Investigation during Cyber Incident Response
  2. 2024A Novel Reinforcement Learning Model for Post-Incident Malware Investigations
  3. 2025DRMD: Deep Reinforcement Learning for Malware Detection under Concept Drift
  4. 2026Building an Adversarial Malware Dataset by Family and Type: Generation, Evasion, and Poisoning Evaluation
  5. 2025Is "Knowing It's Malicious Enough?" Evaluating LLMs for Fine-Grained Malware Behavior Auditing
  6. 2026Trident: Improving Malware Detection with LLMs and Behavioral Features
  7. 2026MaliciousSkillBench: A Comprehensive Benchmark for Malicious Agent Skill Detection
  8. 2026LCC-LLM: Leveraging Code-Centric Large Language Models for Malware Attribution