An FPGA-Accelerated Decision Tree Model for Ransomware Detection in Pervasive Environments

• Camilly de Melo Cruvinel UFU https://orcid.org/0009-0000-1994-1677
• Radharani Santos Rocha UFU https://orcid.org/0009-0003-3176-0272
• Camilla Charão Borchhardt Quincozes UFU https://orcid.org/0009-0004-7398-3551
• Diego Nunes Molinos UFU https://orcid.org/0000-0002-7455-7624

DOI: https://doi.org/10.5753/sbcup.2026.21471

Resumo

Ransomware poses a threat to pervasive computing environments, where resource-constrained devices require low-latency detection mechanisms. This work presents an FPGA-based hardware classifier that translates a trained decision tree into a fixed-point combinational architecture, enabling deterministic single-cycle inference. The proposed architecture achieves up to 99% accuracy with a constant latency of 20 ns per sample. Compared to a software-based ML implementation, the FPGA solution delivers speedups of up to 2.46 × 10³ while utilizing less than 1% of the available logic elements, demonstrating the feasibility of ransomware detection in resource-constrained environments.

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