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pro vyhledávání: '"Mentens, Nele"'
Deploying machine learning-based intrusion detection systems (IDSs) on hardware devices is challenging due to their limited computational resources, power consumption, and network connectivity. Hence, there is a significant need for robust, deep lear
Externí odkaz:
http://arxiv.org/abs/2311.04194
Autor:
Scherrer, Simon, Vliegen, Jo, Sateesan, Arish, Hsiao, Hsu-Chun, Mentens, Nele, Perrig, Adrian
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 algorithms are considered sufficiently accurate for usage in DDoS defense.
Externí odkaz:
http://arxiv.org/abs/2306.14328
Publikováno v:
In Applied Soft Computing November 2024 165
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 designs use the complete addition formula to make the point addition
Externí odkaz:
http://arxiv.org/abs/2201.01158
Low-Rate Overuse Flow Tracer (LOFT): An Efficient and Scalable Algorithm for Detecting Overuse Flows
Autor:
Scherrer, Simon, Wu, Che-Yu, Chiang, Yu-Hsi, Rothenberger, Benjamin, Asoni, Daniele E., Sateesan, Arish, Vliegen, Jo, Mentens, Nele, Hsiao, Hsu-Chun, Perrig, Adrian
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 utilizing 50-100% more than their permitted bandwidth). Thus, these sy
Externí odkaz:
http://arxiv.org/abs/2102.01397
Akademický článek
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Publikováno v:
In Journal of Information Security and Applications June 2023 75
Publikováno v:
In Microprocessors and Microsystems April 2023 98
Publikováno v:
In Microprocessors and Microsystems September 2022 93
Publikováno v:
Journal of Cryptographic Engineering; Sep2024, Vol. 14 Issue 3, p463-474, 12p