An FPGA-Accelerated Decision Tree Model for Ransomware Detection in Pervasive Environments - Cruvinel, Camilly de Melo; Rocha, Radharani Santos; Quincozes, Camilla Charão Borchhardt; Molinos, Diego Nunes (Anais do Simpósio Brasileiro de Computação Ubíqua e Pervasiva (SBCUP); 2026: Anais do XVIII Simpósio Brasileiro de Computação Ubíqua e PervasivaDO - 10.5753/sbcup.2026.21471, 2026)
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 × 10 3 while utilizing less than 1% of the available logic elements, demonstrating the feasibility of ransomware detection in resource-constrained environments.