Considered is a multi-channel wireless network for secret communication that uses the signal-to-interference-plus-noise ratio (SINR) as the performance measure. An eavesdropper can intercept encoded messages through a degraded channel of each legitimate transmitter-receiver communication pair. A friendly interferer, on the other hand, may send cooperative jamming signals to enhance the secrecy performance of the whole network. Besides, the state information of the eavesdropping channel may not be known completely. The transmitters and the friendly interferer have to cooperatively decide on the optimal jamming power allocation strategy that balances the secrecy performance with the cost of employing intentional interference, while the eavesdropper tries to maximize her eavesdropping capacity. To solve this problem, we propose and analyze a non-zero-sum game between the network defender and the eavesdropper who can only attack a limited number of channels. We show that the Nash equilibrium strategies for the players are of threshold type. We present an algorithm to find the equilibrium strategy pair. Numerical examples demonstrate the equilibrium and contrast it to baseline strategies.
Maintaining Information Freshness under Jamming
In UAV communication with a ground control station, mission success requires maintaining the freshness of the received information, especially when the communication faces hostile interference. We model this problem as a game between a UAV transmitter and an adversarial interferer. We prove that in contrast with the Nash equilibrium, multiple Stackelberg equilibria could arise. This allows us to show that reducing interference activity in the Stackelberg game is achieved by higher sensitivity of the transmitter in the Stackelberg equilibrium strategy to network parameters relative to the Nash equilibrium strategy. All the strategies are derived in closed form and we establish the condition for when multiple strategies arise.
- Award ID(s):
- Publication Date:
- NSF-PAR ID:
- Journal Name:
- IEEE INFOCOM 2019 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)
- Sponsoring Org:
- National Science Foundation
More Like this
Modern Public Safety Networks (PSNs) are assisted by Unmanned Aerial Vehicles (UAVs) to provide a resilient communication paradigm during catastrophic events. In this context, we propose a distributed user-centric risk-aware resource management framework in UAV-assisted PSNs supported by both a static UAV and a mobile UAV. The mobile UAV is entitled to a larger portion of the available spectrum due to its capability and flexibility to re-position itself, and therefore establish better communication channel conditions to the users, compared to the static UAV. However, the potential over-exploitation of the mobile UAV-based communication by the users may lead to the mobile UAV’s failure to serve the users due to the increased levels of interference, consequently introducing risk in the user decisions. To capture this uncertainty, we follow the principles of Prospect Theory and design a user’s prospect-theoretic utility function that reflects user’s risk-aware behavior regarding its transmission power investment to the static and/or mobile UAV-based communication option. A non-cooperative game among the users is formulated, where each user determines its power investment strategy to the two available communication choices in order to maximize its expected prospect-theoretic utility. The existence and uniqueness of a Pure Nash Equilibrium (PNE) is proven and themore »
Artificial Intelligence (AI) based techniques are typically used to model decision making in terms of strategies and mechanisms that can result in optimal payoffs for a number of interacting entities, often presenting antagonistic behaviors. In this paper, we propose an AI-enabled multi-access edge computing (MEC) framework, supported by computing-equipped Unmanned Aerial Vehicles (UAVs) to facilitate IoT applications. Initially, the problem of determining the IoT nodes optimal data offloading strategies to the UAV-mounted MEC servers, while accounting for the IoT nodes' communication and computation overhead, is formulated based on a game-theoretic model. The existence of at least one Pure Nash Equilibrium (PNE) point is shown by proving that the game is submodular. Furthermore, different operation points (i.e. offloading strategies) are obtained and studied, based either on the outcome of Best Response Dynamics (BRD) algorithm, or via alternative reinforcement learning approaches (i.e. gradient ascent, log-linear, and Q-learning algorithms), which explore and learn the environment towards determining the users' stable data offloading strategies. The corresponding outcomes and inherent features of these approaches are critically compared against each other, via modeling and simulation.
Most of the cybersecurity research focus on either presenting a specific vulnerability %or hacking technique, or proposing a specific defense algorithm to defend against a well-defined attack scheme. Although such cybersecurity research is important, few have paid attention to the dynamic interactions between attackers and defenders, where both sides are intelligent and will dynamically change their attack or defense strategies in order to gain the upper hand over their opponents. This 'cyberwar' phenomenon exists among most cybersecurity incidents in the real world, which warrants special research and analysis. In this paper, we propose a dynamic game theoretic framework (i.e., hyper defense) to analyze the interactions between the attacker and the defender as a non-cooperative security game. The key idea is to model attackers/defenders to have multiple levels of attack/defense strategies that are different in terms of effectiveness, strategy costs, and attack gains/damages. Each player adjusts his strategy based on the strategy's cost, potential attack gain/damage, and effectiveness in anticipating of the opponent's strategy. We study the achievable Nash equilibrium for the attacker-defender security game where the players employ an efficient strategy according to the obtained equilibrium. Furthermore, we present case studies of three different types of network attacks and putmore »
We consider computational games, sequences of games G = (G1,G2,...) where, for all n, Gn has the same set of players. Computational games arise in electronic money systems such as Bitcoin, in cryptographic protocols, and in the study of generative adversarial networks in machine learning. Assuming that one-way functions exist, we prove that there is 2-player zero-sum computational game G such that, for all n, the size of the action space in Gn is polynomial in n and the utility function in Gn is computable in time polynomial in n, and yet there is no ε-Nash equilibrium if players are restricted to using strategies computable by polynomial-time Turing machines, where we use a notion of Nash equilibrium that is tailored to computational games. We also show that an ε-Nash equilibrium may not exist if players are constrained to perform at most T computational steps in each of the games in the sequence. On the other hand, we show that if players can use arbitrary Turing machines to compute their strategies, then every computational game has an ε-Nash equilibrium. These results may shed light on competitive settings where the availability of more running time or faster algorithms can lead to amore »