Related papers: Jamming Bandits
In wireless security, cognitive adversaries are known to inject jamming energy on the victim's frequency band and monitor the same band for countermeasures thereby trapping the victim. Under the class of cognitive adversaries, we propose a…
Active learning methods have shown great promise in reducing the number of samples necessary for learning. As automated learning systems are adopted into real-time, real-world decision-making pipelines, it is increasingly important that…
We consider a setting where multiple players sequentially choose among a common set of actions (arms). Motivated by a cognitive radio networks application, we assume that players incur a loss upon colliding, and that communication between…
We study a wireless jamming problem consisting of the competition between a legitimate receiver and a jammer, as a zero-sum game where the value to maximize/minimize is the channel capacity at the receiver's side. Most of the approaches…
Wireless systems must be resilient to jamming attacks. Existing mitigation methods require knowledge of the jammer's transmit characteristics. However, this knowledge may be difficult to acquire, especially for smart jammers that attack…
Communication networks shared by many users are a widespread challenge nowadays. In this paper we address several aspects of this challenge simultaneously: learning unknown stochastic network characteristics, sharing resources with other…
In this paper, we investigate jamming-resilient UAV path planning strategies for data collection in Internet of Things (IoT) networks, in which the typical UAV can learn the optimal trajectory to elude such jamming attacks. Specifically,…
In this paper, we introduce DeepFake, a novel deep reinforcement learning-based deception strategy to deal with reactive jamming attacks. In particular, for a smart and reactive jamming attack, the jammer is able to sense the channel and…
In this article, we introduce a novel deception strategy which is inspired by the "Borrowing Arrows with Thatched Boats", one of the most famous military tactics in the history, in order to defeat reactive jamming attacks for low-power IoT…
As the applications of deep reinforcement learning (DRL) in wireless communications grow, sensitivity of DRL based wireless communication strategies against adversarial attacks has started to draw increasing attention. In order to address…
In this article, the anti-jamming communication problem is investigated from a game-theoretic learning perspective. By exploring and analyzing intelligent anti-jamming communication, we present the characteristics of jammers and the…
Broadcasting in wireless networks is vulnerable to adversarial jamming. To thwart such behavior, \emph{resource competitive analysis} is proposed. In this framework, sending, listening, or jamming on one channel for one time slot costs one…
The fast deployment of cognitive radar to counter jamming remains a critical challenge in modern warfare, where more efficient deployment leads to quicker detection of targets. Existing methods are primarily based on evolutionary…
In this paper, we consider how the uplink transmission of a spatially correlated massive multiple-input multiple-output (MIMO) system can be protected from a jamming attack. To suppress the jamming, we propose a novel framework including a…
The multi-armed bandit (MAB) problem is an active learning framework that aims to select the best among a set of actions by sequentially observing rewards. Recently, it has become popular for a number of applications over wireless networks,…
In this paper, we explore the benefit of cooperation in adversarial bandit settings. As a motivating example, we consider the problem of wireless network selection. Mobile devices are often required to choose the right network to associate…
In this work we consider the communication of information in the presence of an online adversarial jammer. In the setting under study, a sender wishes to communicate a message to a receiver by transmitting a codeword x=x_1,...,x_n…
This paper considers adaptive radar electronic counter-counter measures (ECCM) to mitigate ECM by an adversarial jammer. Our ECCM approach models the jammer-radar interaction as a Principal Agent Problem (PAP), a popular economics framework…
The multi-armed bandit problem is a core framework for sequential decision-making under uncertainty, but classical algorithms often fail in environments with hidden, time-varying states that confound reward estimation and optimal action…
Malicious jamming launched by smart jammer, which attacks legitimate transmissions has been regarded as one of the critical security challenges in wireless communications. Thus, this paper exploits intelligent reflecting surface (IRS) to…