mlsanityData to 14 Oct 2026

AI-based Malware and Ransomware Detection Models

arXiv 2207.02108 ↗First posted 5 Jul 2022cs.CR cs.AIcited by 7

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Abstract (arXiv)

Cybercrime is one of the major digital threats of this century. In particular, ransomware attacks have significantly increased, resulting in global damage costs of tens of billion dollars. In this paper, we train and test different Machine Learning and Deep Learning models for malware detection, malware classification and ransomware detection. We introduce a novel and flexible solution that combines two optimized models for malware and ransomware detection. Our results demonstrate some improvements both in terms of detection performances and flexibility. In particular, our combined models pave the way for easier future enhancements using specialized and thus interchangeable detection modules.

Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). Builds on 8 of its 24 references that are in the map. First posted on arXiv 5 Jul 2022.

Builds on

8 of 24 references.

References · 8

  1. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  2. 2018EMBER: An Open Dataset for Training Static PE Malware Machine Learning Modelscited by 165
  3. 2020A Survey of Machine Learning Methods and Challenges for Windows Malware Classificationcited by 33
  4. 2017Survey of Machine Learning Techniques for Malware Analysiscited by 31
  5. 2022MERLIN -- Malware Evasion with Reinforcement LearnINgcited by 11
  6. 2021A Survey on Ransomware: Evolution, Taxonomy, and Defense Solutionscited by 9
  7. 2021Malware Analysis with Artificial Intelligence and a Particular Attention on Results Interpretabilitycited by 9
  8. 2020Cyber Security in the Age of COVID-19: A Timeline and Analysis of Cyber-Crime and Cyber-Attacks during the Pandemiccited by 4

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 3; 2024: 2; 2025: 1; 2026: 1.

7 papers.

  1. 2023Quantum Machine Learning for Malware Classificationcited by 3
  2. 2025Semantic Preprocessing for LLM-based Malware Analysiscited by 3
  3. 2023Towards an in-depth detection of malware using distributed QCNNcited by 1
  4. 2024Use of Multi-CNNs for Section Analysis in Static Malware Detectioncited by 1
  5. 2023Ransomware Detection and Classification using Machine Learningcited by 0
  6. 2024A Lean Transformer Model for Dynamic Malware Analysis and Detectioncited by 0
  7. 2026ThreatVisionAI: A Hybrid CNN-ViT Framework for Image-Based Malware Classificationcited by 0