Journal Article10.1109/TNSM.2021.3067775
Network Protection Against Node Attacks Based on Probabilistic Availability Measures
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TL;DR: A network security and configuration management problem of locating service controllers so as to maximize availability of services in case of targeted attacks on the network infrastructure is considered and a counter-part optimization model is formulated that allows the network operator to derive the optimal placement of controllers.
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Abstract: We consider a network security and configuration management problem of locating service controllers so as to maximize availability of services in case of targeted attacks on the network infrastructure. Assuming that the attacker has full knowledge of the network topology but can only try to predict controller locations, we model the attacker’s behavior introducing a set of probabilistic network availability measures and formulating an optimization problem model that determines the potentially most dangerous attacks the attacker might launch. We also formulate a counter-part optimization model that allows the network operator to derive the optimal placement of controllers, which maximizes availability of services with respect to a given set of network attacks. We explain the models and illustrate our considerations using a running small, intuitive network example. And we also perform extensive numerical experiments with a realistic network data to evaluate and compare the potential effectiveness of different attack strategies, and the effectiveness of the counter-measures that the network operator can adopt.
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Citations
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Hypernetwork Dismantling via Deep Reinforcement Learning.
TL;DR: Zhang et al. as discussed by the authors formulate the hypernetwork dismantling problem as a node sequence decision problem and propose a deep reinforcement learning (DRL)-based hyper-network dismantling framework to ensure the transferability to various real-world hypernetworks.
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Game-Theoretic Approach to Attack Planning and Controller Placement in Software Defined Networks
Konstanty Junosza-Szaniawski,Dariusz Nogalski +1 more
- 16 May 2023
TL;DR: The paper addresses a game-theoretic approach to controller placement and targeted attack planning problems and presents two mixed-strategy models, which allow for efficient controller placement using mixed strategies to maximize the expected availability measure and to defend against mixed-Strategy attacks.
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Max-Min Optimization of Controller Placements vs. Min-Max Optimization of Attacks on Nodes in Service Networks
TL;DR: The paper deals with two complementary optimization problems related to the resilience of communication networks against targeted node attacks, where the proper functioning of the network requires that the nodes are connected to the so called controllers that are placed in selected node locations.
2
Hypernetwork Dismantling via Deep Reinforcement Learning
TL;DR: Zhang et al. as mentioned in this paper formulate the hypernetwork dismantling problem as a node sequence decision problem and propose a deep reinforcement learning (DRL)-based hyper network dismantling framework, which first generates small-scale synthetic hypernetworks and embeds the nodes and hyper-networks into a low dimensional vector space to represent the action and state space.
The K Critical Path Sets to Protect in Interdiction Networks under Limited Defensive Resources
TL;DR: In this paper , the defender of the network attempts to identify which disjoint K-critical path sets to be fortified under limited resources in order to better resist to attacks, and the solution method maximizes the smallest survival probability over the K breakthrough paths to select under a budget constraint.
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