How to jam without getting caught: Analysis and empirical study of stealthy periodic jamming

DeBruhl, Bruce; Tague, Patrick

doi:10.1109/sahcn.2013.6645021

Cited by 12 publications

(11 citation statements)

References 16 publications

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“…The closest work to ours is [7,8], where the authors investigate energy-optimal jamming strategies to achieve stealthiness and increase effectiveness. Specifically, DeBruhl and Tague [8] show that energy-efficient short-form periodic jamming can seriously degrade communication capabilities without compromising the jammer's activity. DeBruhl et al [7] investigate finite-energy jamming games, where jammers choose among different actions (i.e., sleep, power and channel).…”

Section: Related Workmentioning

confidence: 99%

“…To understand how harmful a jammer could be, researchers have studied the worst-case jamming attack with fine-tuned temporal pattern (e.g., pulse jamming [25]), frequency-pattern (e.g., frequency-hopping jamming [26]), and so on. During the last few years, a new family of advanced jamming has gained momentum [2,7,8,15,27,28], where the target component of the jammed transmission is the main objective to optimize.…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, at the link (MAC) layer, acknowledgement (ACK) jamming has been proposed to disrupt medium access control operations [3,21,24]. Since advanced jamming activities are restricted to a specific period of time, an advanced jammer can degrade the network throughput with a comparatively lower energy budget yet with lower probability of being detected [7,8].…”

Section: Introductionmentioning

confidence: 99%

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Jam Sessions

Zhang

Restuccia

Melodia

et al. 2019

Proceedings of the Twentieth ACM International Symposium on Mobile Ad Hoc Networking and Computing

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Recent research advances in wireless security have shown that advanced jamming can significantly decrease the performance of wireless communications. In advanced jamming, the adversary intentionally concentrates the available energy budget on specific critical components (e.g., pilot symbols, acknowledgement packets, etc.) to (i) increase the jamming effectiveness, as more targets can be jammed with the same energy budget; and (ii) decrease the likelihood of being detected, as the channel is jammed for a shorter period of time. These key aspects make advanced jamming very stealthy yet exceptionally effective in practical scenarios. One of the fundamental challenges in designing defense mechanisms against an advanced jammer is understanding which jamming strategies yields the lowest throughput, for a given channel condition and a given amount of energy. To the best of our knowledge, this problem still remains unsolved, as an analytic model to quantitatively compare advanced jamming schemes is still missing in existing literature. To fill this gap, in this paper we conduct a comparative analysis of several most viable advanced jamming schemes in the widely-used MIMO networks. We first mathematically model a number of advanced jamming schemes at the signal processing level, so that a quantitative relationship between the jamming energy and the jamming effect is established. Based on the model, theorems are derived on the optimal advanced jamming scheme for an arbitrary channel condition. The theoretical findings are validated through extensive simulations and experiments on a 5-radio 2x2 MIMO testbed. Our results show that the theorems are able to predict jamming efficiency with high accuracy. Moreover, to further demonstrate that the theoretical findings are applicable to address crucial real-world jamming problems, we show that the theorems can be incorporated to state-of-art reinforcement-learning based jamming algorithms and boost the action exploration phase so that a faster convergence is achieved.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Jam Sessions

Zhang

Restuccia

Melodia

et al. 2019

Proceedings of the Twentieth ACM International Symposium on Mobile Ad Hoc Networking and Computing

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“…Efficient static strategies include random [40], periodic [10], and deceptive jamming [40]. Both random and periodic jamming alternate between attacking and sleeping in an attempt to attack in an efficient manner.…”

Section: Related Workmentioning

confidence: 99%

“…amples of recent energy-conscious attacks include periodic jamming [10] and random jamming [40] that alternate between jamming and sleeping to save energy; control channel jamming [8] and similar attacks that leverage protocol structure for efficiency; and reactive jamming [39], adaptive jamming [9,31], and mesh jamming [22] which respond to observed activity instead of attacking statically.…”

Section: Introductionmentioning

confidence: 99%

Power napping with loud neighbors

DeBruhl

Kroer

Datta

et al. 2014

Proceedings of the 2014 ACM Conference on Security and Privacy in Wireless &Amp; Mobile Networks

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The openness of wireless communication and the recent development of software-defined radio technology, respectively, provide a low barrier and a wide range of capabilities for misbehavior, attacks, and defenses against attacks. In this work we present finite-energy jamming games, a game model that allows a jammer and sender to choose (1) whether to transmit or sleep, (2) a power level to transmit with, and (3) what channel to transmit on. We also allow the jammer to choose on how many channels it simultaneously attacks. A major addition in finite-energy jamming games is that the jammer and sender both have a limited amount of energy which is drained according to the actions a player takes.We develop a model of our system as a zero-sum finitehorizon stochastic game with deterministic transitions. We leverage the zero-sum and finite-horizon properties of our model to design a simple polynomial-time algorithm to compute optimal randomized strategies for both players. The utility function of our game model can be decoupled into a recursive equation. Our algorithm exploits this fact to use dynamic programming to construct solutions in a bottomup fashion. For each state of energy levels, a linear program is solved to find Nash equilibrium strategies for the subgame. With these techniques, our algorithm has only a linear dependence on the number of states, and quadratic dependence on the number of actions, allowing us to solve very large instances.By computing Nash equilibria for our game models, we explore what kind of performance guarantees can be achieved both for the sender and jammer, when playing against an optimal opponent. We also use the optimal strategies to simulate finite-energy jamming games and provide insights into robust communication among reconfigurable, yet energylimited, radio systems. To test the performance of the optimal strategies we compare their performance with a random and adaptive strategy. Matching our intuition, the aggressiveness of an attacker is related to how much of a discount is placed on data delay. This results in the defender often choosing to sleep despite the latency implication, because the threat of jamming is high. We also present several other findings from simulations where we vary the strategies for one or both of the players.

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A Countermeasure Against Random Pulse Jamming in Time Domain Based on Reinforcement Learning

Zhou

Niu

2020

IEEE Access

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Pulse jamming is one of the common malicious jamming patterns that can significantly reduce the of wireless communication's reliability. This paper investigates the problem of anti-jamming communication in a random pulse jamming environment. In order to obtain the countermeasure in time domain, the Markov decision process (MDP) is employed to model and analyze the above problem, and a time-domain anti-pulse jamming algorithm (TDAA) based on reinforcement learning is proposed. The proposed algorithm learns from the dynamic interaction with the jamming environment to gradually approximate the optimal time-domain strategy. The optimal strategy enables the transmitter to switch between two states, i.e. ''active'' and ''silent'', to avoid random pulse jamming. In addition, a state estimation and adjustment method for the random pulse jamming environment is introduced to improve the robustness of the proposed TDAA. Simulation results show that, compared with continuous transmission, the proposed TDAA can effectively reduce the jamming collision ratio and significantly improve the normalized throughput. And compared with transmitting terminal Q-learning algorithm (TTQA), the proposed TDAA has higher time utilization ratio and normalized throughput.INDEX TERMS Anti-jamming, Markov decision process, Q-learning, reinforcement learning, random pulse jamming.

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How to jam without getting caught: Analysis and empirical study of stealthy periodic jamming

Cited by 12 publications

References 16 publications

Jam Sessions

Jam Sessions

Power napping with loud neighbors

A Countermeasure Against Random Pulse Jamming in Time Domain Based on Reinforcement Learning

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