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AI-based Malware and Ransomware Detection Models

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arxiv 2207.02108 v2 pith:75UV2Z2E submitted 2022-07-05 cs.CR cs.AI

classification cs.CRcs.AI
keywords detectionmalwaremodelsransomwarelearningparticularai-basedattacks
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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Cited by 1 Pith paper

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  1. ThreatVisionAI: A Hybrid CNN-ViT Framework for Image-Based Malware Classification

    cs.CR 2026-07 conditional novelty 4.5 of 10

    A three-branch CNN-wavelet-ViT ensemble with soft voting reaches 98.01% accuracy and 0.9742 weighted F1 on Malimg, with wavelet features improving discrimination of similar families.

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