Exploiting Power Side-Channel Vulnerabilities in XGBoost Accelerator

XGBoost (eXtreme Gradient Boosting), a widelyused decision tree algorithm, plays a crucial role in applications such as ransomware and fraud detection. While its performance is well-established, its security against model extraction on hardware platforms like Field Programmable Gate Arrays (FPGAs) h...

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Veröffentlicht in:	2025 62nd ACM/IEEE Design Automation Conference (DAC) S. 1 - 7
Hauptverfasser:	Xiao, Yimeng, Gajjar, Archit, Aysu, Aydin, Franzon, Paul
Format:	Tagungsbericht
Sprache:	Englisch
Veröffentlicht:	IEEE 22.06.2025
Schlagworte:	Data models Decision trees Feature extraction Field programmable gate arrays FPGAs hardware security HLS Machine learning algorithms Monitoring Power demand Protection Ransomware side-channel attack Side-channel attacks XGBoost
Online-Zugang:	Volltext
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