منابع مشابه
Enhancing network robustness against malicious attacks.
In a recent work [Schneider et al., Proc. Natl. Acad. Sci. USA 108, 3838 (2011)], the authors proposed a simple measure for network robustness under malicious attacks on nodes. Using a greedy algorithm, they found that the optimal structure with respect to this quantity is an onion structure in which high-degree nodes form a core surrounded by rings of nodes with decreasing degree. However, in ...
متن کاملEnhancing network robustness for malicious attacks
In a recent work [Proc. Natl. Acad. Sci. USA 108, 3838 (2011)], the authors proposed a simple measure for network robustness under malicious attacks on nodes. With a greedy algorithm, they found the optimal structure with respect to this quantity is an onion structure in which high-degree nodes form a core surrounded by rings of nodes with decreasing degree. However, in real networks the failur...
متن کاملEnhancing FPGA Robustness via Generic Monitoring IP Cores
Today, state of the art technology allows a very dense integration of embedded HW/SW designs. As a consequence, more errors are introduced in these circuits that have to be observed during run-time. Adding monitors to a design enables the recognition of and the reaction to these threats, but, usually, monitors have to be developed for every individual FPGA design. Our approach provides generic ...
متن کاملEnhancing Robustness of Machine Learning Systems via Data Transformations
We propose the use of data transformations as a defense against evasion attacks on ML classifiers. We present and investigate strategies for incorporating a variety of data transformations including dimensionality reduction via Principal Component Analysis and data ‘anti-whitening’ to enhance the resilience of machine learning, targeting both the classification and the training phase. We empiri...
متن کاملEnhancing neural-network performance via assortativity
The performance of attractor neural networks has been shown to depend crucially on the heterogeneity of the underlying topology. We take this analysis a step further by examining the effect of degree-degree correlations--assortativity--on neural-network behavior. We make use of a method recently put forward for studying correlated networks and dynamics thereon, both analytically and computation...
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ژورنال
عنوان ژورنال: IEEE/ACM Transactions on Networking
سال: 2017
ISSN: 1063-6692,1558-2566
DOI: 10.1109/tnet.2017.2689019