G
Gyorgy Dan
Researcher at Royal Institute of Technology
Publications - 175
Citations - 3643
Gyorgy Dan is an academic researcher from Royal Institute of Technology. The author has contributed to research in topics: Cache & Computer science. The author has an hindex of 29, co-authored 158 publications receiving 3007 citations. Previous affiliations of Gyorgy Dan include Instituto Superior Técnico & Middle East Technical University.
Papers
More filters
Posted Content
A Bayesian Nash equilibrium-based moving target defense against stealthy sensor attacks
TL;DR: In this paper, a moving target defense strategy was proposed to reduce the impact of stealthy sensor attacks on feedback systems, where the defender periodically and randomly switches between thresholds from a discrete set to increase the uncertainty for the attacker and make stealthy attacks detectable.
Journal ArticleDOI
TECoSA – Trends, Drivers, and Strategic Directions for Trustworthy Edge Computing in Industrial Applications
James Gross,Martin Törngren,Gyorgy Dan,David Broman,Erik Herzog,Iolanda Leite,Raksha Ramakrishna,Rebecca Stower,Haydn A. Thompson +8 more
TL;DR: TECoSA as discussed by the authors is a university-based research center in collaboration with industry, focusing on Trustworthy Edge Computing Systems and Applications, which summarizes and assesses the current trends and drivers regarding edge computing.
Mitigating Denial of Service Attacks using Anonymity Networks: Relationship Anonymity-Communication Overhead Trade-off
TL;DR: It is shown that, contrary to intuition, increased overhead does not always improve anonymity and the impact of the system's parameters on anonymity and on the optimal anonymity network parameters, and the sensitivity of anonymity to the misestimation of the number of attackers is investigated.
Proceedings ArticleDOI
An Active Learning Approach to Dynamic Alert Prioritization for Real-time Situational Awareness
Yeongwoo Kim,Gyorgy Dan +1 more
TL;DR: In this paper , the authors propose to use the entropy of the belief of the security state as a proxy for the mean squared error (MSE) of a belief, and develop two computationally tractable policies for choosing alerts to investigate that minimize the entropy, taking into account the potential uncertainty of the investigations' results.
Journal ArticleDOI
Inferring Class Label Distribution of Training Data from Classifiers: An Accuracy-Augmented Meta-Classifier Attack
Raksha Ramakrishna,Gyorgy Dan +1 more
TL;DR: In this article , a meta-classifier is trained on the parameters of the shadow classifiers augmented with the accuracy of the classifiers on auxiliary data to infer the class label distribution of the training data from parameters of ML classifiers.