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Rounding Dynamic Matchings Against an Adaptive Adversary [article]

David Wajc
2020 arXiv   pre-print
We present a new dynamic matching sparsification scheme. From this scheme we derive a framework for dynamically rounding fractional matchings against adaptive adversaries.  ...  Plugging in known dynamic fractional matching algorithms into our framework, we obtain numerous randomized dynamic matching algorithms which work against adaptive adversaries (the first such algorithms  ...  Cohen, Bernhard Haeupler, Roie Levin, Seffi Naor and the anonymous reviewers for comments on an earlier draft of this paper, which helped improve its presentation.  ... 
arXiv:1911.05545v2 fatcat:w32zxkzbbjchnevsykrsb7ryim

Strategic Evolution of Adversaries Against Temporal Platform Diversity Active Cyber Defenses [article]

Michael L. Winterrose, Kevin M. Carter
2014 arXiv   pre-print
In this study, we develop a set of tools to model the adaptive strategy formulation of an intelligent actor against an active cyber defensive system.  ...  Adversarial dynamics are a critical facet within the cyber security domain, in which there exists a co-evolution between attackers and defenders in any given threat scenario.  ...  The central issue in this problem class is the existence of an intelligent, adaptable adversary able to actively counter defensive moves.  ... 
arXiv:1408.0023v1 fatcat:ja437cndkja6ddpznzevvjap3m

Global Information Sharing under Network Dynamics [article]

Chinmoy Dutta and Gopal Pandurangan and Rajmohan Rajaraman and Zhifeng Sun and Emanuele Viola
2014 arXiv   pre-print
We first consider the strongly adaptive adversary model where in each round, each node first chooses a token to broadcast to all its neighbors (without knowing who they are), and then an adversary chooses  ...  We study how to spread k tokens of information to every node on an n-node dynamic network, the edges of which are changing at each round.  ...  An important intermediate model between the offline setting and the adaptive adversary models is the oblivious adversary model in which the adversary lays the dynamic network in advance (as in the offline  ... 
arXiv:1409.7771v1 fatcat:wmtju26lizcq7hixmbjkszivxu

Dynamic Algorithms Against an Adaptive Adversary: Generic Constructions and Lower Bounds [article]

Amos Beimel, Haim Kaplan, Yishay Mansour, Kobbi Nissim, Thatchaphol Saranurak, Uri Stemmer
2021 arXiv   pre-print
We give a general reduction transforming a dynamic algorithm against an oblivious adversary to a dynamic algorithm robust against an adaptive adversary.  ...  dynamic algorithm that solves them against an oblivious adversary.  ...  against an adaptive adversary.  ... 
arXiv:2111.03980v1 fatcat:ovd2fifhjvdurjqtpgosnc3nb4

Beating the Multiplicative Weights Update Algorithm [article]

Abhinav Aggarwal, José Abel Castellanos Joo, Diksha Gupta
2017 arXiv   pre-print
Specifically, we focus our attention on two adversarial strategies: (1) non-adaptive, in which the adversary chooses a fixed set of experts a priori and corrupts their advice in each round; and (2) adaptive  ...  Finally, we briefly discuss the extension of these adversarial strategies for a general MWU algorithm and provide an outline for the framework in that setting.  ...  Note that this dynamic program runs in time O(τ c r ) = O(τ ) in round r.  ... 
arXiv:1708.04668v1 fatcat:l3uqbvfzt5hb5nh2nhwzqw5ztu

On the Complexity of Information Spreading in Dynamic Networks [chapter]

Chinmoy Dutta, Gopal Pandurangan, Rajmohan Rajaraman, Zhifeng Sun, Emanuele Viola
2013 Proceedings of the Twenty-Fourth Annual ACM-SIAM Symposium on Discrete Algorithms  
a strongly adaptive adversary in o(nk/polylog(n)) rounds.  ...  We first consider the strongly adaptive adversary model where in each round, each node first chooses a token to broadcast to all its neighbors (without knowing who they are), and then an adversary chooses  ...  An important intermediate model between the offline setting and the adaptive adversary models is the oblivious adversary model in which the adversary lays the dynamic network in advance (as in the offline  ... 
doi:10.1137/1.9781611973105.52 dblp:conf/soda/DuttaPRSV13 fatcat:dtqtpi2yzjaztbamql35ggmonm

Data Poisoning Won't Save You From Facial Recognition [article]

Evani Radiya-Dixit, Sanghyun Hong, Nicholas Carlini, Florian Tramèr
2022 arXiv   pre-print
which point they are scraped) and must thereafter fool all future models -- including models trained adaptively against the users' past attacks, or models that use technologies discovered after the attack  ...  We further show that an adversary with black-box access to the attack can (i) train a robust model that resists the perturbations of collected pictures and (ii) detect poisoned pictures uploaded online  ...  This robust training approach differs from adversarial training (Szegedy et al., 2013; Madry et al., 2017) . Adversarial training makes a model robust against an attack that depends on the model.  ... 
arXiv:2106.14851v2 fatcat:bgor6b6tnnewvhpzbwjuu5hhba

Adversarial Scheduling in Evolutionary Game Dynamics [article]

Gabriel Istrate, Madhav V. Marathe, S. S. Ravi
2008 arXiv   pre-print
Consider a system in which players at nodes of an underlying graph G repeatedly play Prisoner's Dilemma against their neighbors.  ...  With this restriction even an adaptive scheduler is not significantly more powerful than the random scheduler, provided it is "reasonably fair".  ...  This is also true for the adversarial model in the case of non-adaptive (1-fair) daemons. This is in case with the case of an edge daemon, when even non-adaptive daemons could preclude stabilization.  ... 
arXiv:0812.1194v1 fatcat:xmgy4ad4b5hblhy5h7pp7hzqle

Adaptive Attacker Strategy Development Against Moving Target Cyber Defenses [article]

M. L. Winterrose, K. M. Carter, N. Wagner, W. W. Streilein
2014 arXiv   pre-print
Adaptive attacker response strategies are modeled by finite state machine (FSM) constructs that evolve during simulated play against defender strategies via an evolutionary algorithm.  ...  A model of strategy formulation is used to study how an adaptive attacker learns to overcome a moving target cyber defense.  ...  We demonstrate that the degree to which a defense policy is optimal against an adaptive adversary changes as the duration of conflict varies.  ... 
arXiv:1407.8540v1 fatcat:qdqhxr3a5bhv7pkpvw2bzrcuti

The Communication Cost of Information Spreading in Dynamic Networks [article]

Mohamad Ahmadi and Fabian Kuhn and Shay Kutten and Anisur Rahaman Molla and Gopal Pandurangan
2018 arXiv   pre-print
We consider two types of adversaries that arbitrarily rewire the network while keeping it connected: the adaptive adversary that is aware of the status of all the nodes and the algorithm (including the  ...  to every node on an n-node network.  ...  In this paper, we distinguish between a strongly adaptive adversary and an oblivous adversary.  ... 
arXiv:1806.09847v1 fatcat:xqroxuzco5aedh4g4c3d4zqo3q

Some lower bounds in dynamic networks with oblivious adversaries

Irvan Jahja, Haifeng Yu, Yuda Zhao
2019 Distributed computing  
This paper considers several closely-related problems in synchronous dynamic networks with oblivious adversaries, and proves novel Ω(d + poly(m)) lower bounds on their time complexity (in rounds).  ...  Here d is the dynamic diameter of the dynamic network and m is the total number of nodes.  ...  of P on this node against the reference adversary in round r .  ... 
doi:10.1007/s00446-019-00360-4 fatcat:ahvhnuckg5gtjlvcf53xsgmt2m

Adaptive Regret Minimization in Bounded-Memory Games [chapter]

Jeremiah Blocki, Nicolas Christin, Anupam Datta, Arunesh Sinha
2013 Lecture Notes in Computer Science  
Roughly, a hypothetical k-adaptive adversary adapts her strategy to the defender's actions exactly as the real adversary would within each window of k rounds.  ...  To account for this generality, we introduce the notion of k-adaptive regret, which compares the reward obtained by playing actions prescribed by the algorithm against a hypothetical k-adaptive adversary  ...  If D can minimize k-adaptive regret against any k-adaptive adversary then D can minimize k-adaptive regret against any oblivious adversary becauseR 0 =R k whenever the adversary is oblivious. 3.  ... 
doi:10.1007/978-3-319-02786-9_5 fatcat:iweii3oqazdyxesanax7tnecwi

Adversarial queuing on the multiple-access channel

Bogdan S. Chlebus, Dariusz R. Kowalski, Mariusz A. Rokicki
2006 Proceedings of the twenty-fifth annual ACM symposium on Principles of distributed computing - PODC '06  
We study deterministic protocols competing against adversaries restricted by injection rate and burstiness of traffic.  ...  A station running an adaptive protocol can rely on a global clock, may add control bits to be piggybacked on messages, and may store the complete history of the feedback from the channel during an execution  ...  channel against adversary A.  ... 
doi:10.1145/1146381.1146398 dblp:conf/podc/ChlebusKR06 fatcat:rcbafufwtjervl5fdkkdnwdvzy

Adversarial Queuing on the Multiple Access Channel

Bogdan S. Chlebus, Dariusz R. Kowalski, Mariusz A. Rokicki
2012 ACM Transactions on Algorithms  
We study deterministic protocols competing against adversaries restricted by injection rate and burstiness of traffic.  ...  A station running an adaptive protocol can rely on a global clock, may add control bits to be piggybacked on messages, and may store the complete history of the feedback from the channel during an execution  ...  channel against adversary A.  ... 
doi:10.1145/2071379.2071384 fatcat:iawqgfoyczgpxm7wq52y46yuna

Measuring player skill using dynamic difficulty adjustment

Simon Demediuk, Marco Tamassia, William L. Raffe, Fabio Zambetta, Florian "Floyd" Mueller, Xiaodong Li
2018 Proceedings of the Australasian Computer Science Week Multiconference on - ACSW '18  
This is done by measuring the effort of a Dynamic Difficult Adjustment agent, without the need for direct competition between players.  ...  Through the application of the Adaptive Training Framework, this work presents a novel method to determine the skill level of the player after each interaction with the video game.  ...  Additionally, Dynamic Difficulty Adjustment can be applied in a variety of different forms, not just in control of a adversarial agent.  ... 
doi:10.1145/3167918.3167939 dblp:conf/acsw/DemediukTRZML18 fatcat:3bguh5m5jra5doa5xf7b6bxbxi
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