9 citations · 10 across the 5 of their papers we have counts for
5 papers
P2W: From Power Traces to Weights Matrix -- An Unconventional Transfer Learning Approach
Roozbeh Siyadatzadeh, Fatemeh Mehrafrooz, Nele Mentens +1
The rapid growth of deploying machine learning (ML) models within embedded systems on a chip (SoCs) has led to transformative shifts in fields like healthcare and autonomous vehicl…
Quantization-aware Neural Architectural Search for Intrusion Detection
Rabin Yu Acharya, Laurens Le Jeune, Nele Mentens +2
Deploying machine learning-based intrusion detection systems (IDSs) on hardware devices is challenging due to their limited computational resources, power consumption, and network…
ALBUS: a Probabilistic Monitoring Algorithm to Counter Burst-Flood Attacks
Simon Scherrer, Jo Vliegen, Arish Sateesan +3
Modern DDoS defense systems rely on probabilistic monitoring algorithms to identify flows that exceed a volume threshold and should thus be penalized. Commonly, classic sketch algo…
Breaking a fully Balanced ASIC Coprocessor Implementing Complete Addition Formulas on Weierstrass Elliptic Curves
Ievgen Kabin, Zoya Dyka, Dan Klann +3
In this paper we report on the results of selected horizontal SCA attacks against two open-source designs that implement hardware accelerators for elliptic curve cryptography. Both…
Low-Rate Overuse Flow Tracer (LOFT): An Efficient and Scalable Algorithm for Detecting Overuse Flows
Simon Scherrer, Che-Yu Wu, Yu-Hsi Chiang +7
Current probabilistic flow-size monitoring can only detect heavy hitters (e.g., flows utilizing 10 times their permitted bandwidth), but cannot detect smaller overuse (e.g., flows…