Jamming games for power controlled medium access with dynamic traffic

Sagduyu, Yalin E.; Berry, Randall A.; Ephremides, Anthony

doi:10.1109/isit.2010.5513413

Cited by 17 publications

(6 citation statements)

References 10 publications

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“…However, in many practical scenarios, data arrival at the users can be random. The game-theoretic framework has been used to explore the role of random arrival of data in mitigating jamming attacks in [23].…”

Section: A Related Workmentioning

confidence: 99%

Performance Analysis of a MIMO System with Bursty Traffic in the presence of Energy Harvesting Jammer

Allipuram¹,

Mohapatra²,

Παππάς³

et al. 2021

Preprint

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This paper explores the role of multiple antennas in mitigating jamming attacks for the Rayleigh fading environment with exogenous random traffic arrival. The jammer is assumed to have energy harvesting ability where energy arrives according to Bernoulli process. The outage probabilities are derived with different assumptions on the number of antennas at the transmitter and receiver. The outage probability for the Alamouti space-time code is also derived. The work characterizes the average service rate for different antenna configurations taking into account of random arrival of data and energy at the transmitter and jammer, respectively. In many practical applications, latency and timely updates are of importance, thus, delay and Average Age of Information (AAoI) are the meaningful metrics to be considered. The work characterizes these metrics under jamming attack. The impact of finite and infinite energy battery size at the jammer on various performance metrics is also explored. Two optimization problems are considered to explore the interplay between AAoI and delay under jamming attack. Furthermore, our results show that Alamouti code can significantly improve the performance of the system even under jamming attack, with less power budget. The paper also demonstrates how the developed results can be useful for multiuser scenarios.

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Section: A Related Workmentioning

confidence: 99%

Performance Analysis of a MIMO System with Bursty Traffic in the presence of Energy Harvesting Jammer

Allipuram¹,

Mohapatra²,

Παππάς³

et al. 2021

Preprint

View full text Add to dashboard Cite

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“…A typical example of wireless attacks is the jamming attack that aims to disrupt wireless communications by imposing interference at receivers (e.g., see [63]) and causing denial of service (DoS) [64]. These attacks use different communication means (e.g., power control [65] or random access [66]) and apply at different levels of prior information on attacker's intent [67]. As radios become smarter by performing more sophisticated tasks, they also become vulnerable to advanced attacks that target their underlying tasks.…”

Section: Synthetic Data 𝐺(𝒛 𝒚)mentioning

confidence: 99%

Deep Learning for Wireless Communications

Erpek

O’Shea

Sagduyu

et al. 2019

Studies in Computational Intelligence

131

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Existing communication systems exhibit inherent limitations in translating theory to practice when handling the complexity of optimization for emerging wireless applications with high degrees of freedom. Deep learning has a strong potential to overcome this challenge via data-driven solutions and improve the performance of wireless systems in utilizing limited spectrum resources. In this chapter, we first describe how deep learning is used to design an end-to-end communication system using autoencoders. This flexible design effectively captures channel impairments and optimizes transmitter and receiver operations jointly in single-antenna, multiple-antenna, and multiuser communications. Next, we present the benefits of deep learning in spectrum situation awareness ranging from channel modeling and estimation to signal detection and classification tasks. Deep learning improves the performance when the model-based methods fail. Finally, we discuss how deep learning applies to wireless communication security. In this context, adversarial machine learning provides novel means to launch and defend against wireless attacks. These applications demonstrate the power of deep learning in providing novel means to design, optimize, adapt, and secure wireless communications.

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“…The data receiving at a node or base station (BS) varies dynamically, and if the jammer fail in knowing the status of the queue, the randomness of the data entry is used to improve system performance. In the study [1,2], a theoretical framework on games is examined to evaluate the effect of random data arrival in alleviating the jamming attack. In [3,4], a game theory examines the influence of jamming over a collusive channel.…”

Section: Introductionmentioning

confidence: 99%

Generative Adversarial Networks for Secure Data Transmission in Wireless Network

Jayabalan¹,

Pugazendi²

2023

Intelligent Automation &Amp; Soft Computing

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In this paper, a communication model in cognitive radios is developed and uses machine learning to learn the dynamics of jamming attacks in cognitive radios. It is designed further to make their transmission decision that automatically adapts to the transmission dynamics to mitigate the launched jamming attacks. The generative adversarial learning neural network (GALNN) or generative dynamic neural network (GDNN) automatically learns with the synthesized training data (training) with a generator and discriminator type neural networks that encompass minimax game theory. The elimination of the jamming attack is carried out with the assistance of the defense strategies and with an increased detection rate in the generative adversarial network (GAN). The GDNN with game theory is designed to validate the channel condition with the cross entropy loss function and back-propagation algorithm, which improves the communication reliability in the network. The simulation is conducted in NS2.34 tool against several performance metrics to reduce the misdetection rate and false alarm rates. The results show that the GDNN obtains an increased rate of successful transmission by taking optimal actions to act as a defense mechanism to mislead the jammer, where the jammer makes high misclassification errors on transmission dynamics.

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Jamming games for power controlled medium access with dynamic traffic

Cited by 17 publications

References 10 publications

Performance Analysis of a MIMO System with Bursty Traffic in the presence of Energy Harvesting Jammer

Performance Analysis of a MIMO System with Bursty Traffic in the presence of Energy Harvesting Jammer

Deep Learning for Wireless Communications

Generative Adversarial Networks for Secure Data Transmission in Wireless Network

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