Zobrazeno 1 - 10
of 402
pro vyhledávání: '"Michalak, Tomasz"'
In this work, we reexamine the vulnerability of Payment Channel Networks (PCNs) to bribing attacks, where an adversary incentivizes blockchain miners to deliberately ignore a specific transaction to undermine the punishment mechanism of PCNs. While p
Externí odkaz:
http://arxiv.org/abs/2402.01363
Autor:
Polevoy, Gleb, Michalak, Tomasz
Consider spies infiltrating a network or dissidents secretly organising under a dictatorship. Such scenarios can be cast as adversarial social network analysis problems involving nodes connecting while evading network analysis tools, e.g., centrality
Externí odkaz:
http://arxiv.org/abs/2312.01394
Graph anomaly detection (GAD) has achieved success and has been widely applied in various domains, such as fraud detection, cybersecurity, finance security, and biochemistry. However, existing graph anomaly detection algorithms focus on distinguishin
Externí odkaz:
http://arxiv.org/abs/2308.01063
Autor:
Lai, Yuni, Waniek, Marcin, Li, Liying, Wu, Jingwen, Zhu, Yulin, Michalak, Tomasz P., Rahwan, Talal, Zhou, Kai
Random Walks-based Anomaly Detection (RWAD) is commonly used to identify anomalous patterns in various applications. An intriguing characteristic of RWAD is that the input graph can either be pre-existing or constructed from raw features. Consequentl
Externí odkaz:
http://arxiv.org/abs/2307.14387
Payment channel networks (PCNs) are a promising solution to the scalability problem of cryptocurrencies. Any two users connected by a payment channel in the network can theoretically send an unbounded number of instant, costless transactions between
Externí odkaz:
http://arxiv.org/abs/2306.16006
Adversarial social network analysis studies how graphs can be rewired or otherwise manipulated to evade social network analysis tools. While there is ample literature on manipulating simple networks, more sophisticated network types are much less und
Externí odkaz:
http://arxiv.org/abs/2302.02687
Autor:
Mesgaribarzi Niusha, Djenouri Youcef, Belbachir Ahmed Nabil, Michalak Tomasz, Srivastava Gautam
Publikováno v:
Nanotechnology Reviews, Vol 13, Iss 1, Pp 992-8 (2024)
Combining deep learning (DL) with nanotechnology holds promise for transforming key facets of nanoscience and technology. This synergy could pave the way for groundbreaking advancements in the creation of novel materials, devices, and applications, u
Externí odkaz:
https://doaj.org/article/b22aee55d8eb4e958a40abdc43907172
Signed social networks are widely used to model the trust relationships among online users in security-sensitive systems such as cryptocurrency trading platforms, where trust prediction plays a critical role. In this paper, we investigate how attacke
Externí odkaz:
http://arxiv.org/abs/2206.13104
Autor:
Djenouri, Youcef, Belbachir, Ahmed Nabil, Michalak, Tomasz, Belhadi, Asma, Srivastava, Gautam
Publikováno v:
In Engineering Applications of Artificial Intelligence July 2024 133 Part D
Deep reinforcement learning (RL) has recently shown great promise in robotic continuous control tasks. Nevertheless, prior research in this vein center around the centralized learning setting that largely relies on the communication availability amon
Externí odkaz:
http://arxiv.org/abs/2112.13937