Anti-jamming Strategy Versus a Low-Power Jamming Attack When Intelligence of Adversary’s Attack Type is Unknown

Garnaev, Andrey; Liu, Ying; Trappe, Wade

doi:10.1109/tsipn.2015.2506038

Cited by 39 publications

(22 citation statements)

References 29 publications

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“…In each scenario, the mobile device chooses a communication strategy x = [P s , φ], and observes the resulting SINR of the signals and the utility. Receive the SINR (k) and ψ (k+1) on the feedback channel 20 Obtain u (k) and s (k+1) = SINR (k) , ψ (k+1)…”

Section: Communication Schemementioning

confidence: 99%

Two-dimensional anti-jamming communication based on deep reinforcement learning

Han

Xiao

Poor

2017

2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

175

108

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By using smart radio devices, a jammer can dynamically change its jamming policy based on opposing security mechanisms; it can even induce the mobile device to enter a specific communication mode and then launch the jamming policy accordingly. On the other hand, mobile devices can exploit spread spectrum and user mobility to address both jamming and interference. In this paper, a two-dimensional anti-jamming mobile communication scheme is proposed in which a mobile device leaves a heavily jammed/interfered-with frequency or area. It is shown that, by applying reinforcement learning techniques, a mobile device can achieve an optimal communication policy without the need to know the jamming and interference model and the radio channel model in a dynamic game framework. More specifically, a hotbooting deep Q-network based two-dimensional mobile communication scheme is proposed that exploits experiences in similar scenarios to reduce the exploration time at the beginning of the game, and applies deep convolutional neural network and macro-action techniques to accelerate the learning speed in dynamic situations. Several real-world scenarios are simulated to evaluate the proposed method. These simulation results show that our proposed scheme can improve both the signal-to-interference-plus-noise ratio of the signals and the utility of the mobile devices against cooperative jamming compared with benchmark schemes.

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Section: Communication Schemementioning

confidence: 99%

Two-dimensional anti-jamming communication based on deep reinforcement learning

Han

Xiao

Poor

2017

2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

175

108

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“…In the last few years, a lot of research work on jamming in WSNs emerged . Some of the existing literature are discussed in this section.…”

Section: Related Workmentioning

confidence: 99%

“…Some of the existing literature are discussed in this section. Using Bayesian game, Garnaev et al studied jammer type identification to determine whether a jammer is a random jammer or an intelligent jammer. The authors formulated the problem as a dual linear programming problem.…”

Section: Related Workmentioning

confidence: 99%

Topology control in the presence of jammers for wireless sensor networks

Bhavathankar

Mondal

Misra

2017

Int J Communication

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In this paper, the problem of ensuring packet delivery ratio and high network lifetime in wireless sensor networks in the presence of single or multiple jammers is studied using single-leader-multiple-followers Stackelberg game theory. A topology control scheme is proposed, in which the sink node, which acts as the leader, identifies the set of jamming affected nodes. On the other hand, the sensor nodes, which act as followers, need to decide an optimum transmission power level, while ensuring an optimal set of neighbor nodes covered. A scheme, named TC-JAM, for ensuring packet delivery ratio, while avoiding jammers and increasing network lifetime in wireless sensor networks, is proposed. In existing literatures, the sensor nodes are envisioned to be equipped with multiple interfaces, while having access for multiple channels. However, in TC-JAM, the sensor nodes have simple hardware with single interface for communication, ie, the sensor nodes have single channel for communication. Additionally, in the proposed scheme, TC-JAM, each sensor node has a provision to vary its transmission power according to the chosen strategies. Using TC-JAM, the energy consumption of the overall network reduces by up to 62%, and the network lifetime increases by 56% to 73%. KEYWORDSDynamic transmission power level model, jammer, network lifetime, stackelberg game, topology control, wireless sensor network 1 Int J Commun Syst. 2017;30:e3289.wileyonlinelibrary.com/journal/dac

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“…Optimal power allocation schemes for jamming attacks were studied in [17]. Assuming a low-power jammer, Bayesian game based anti-jamming strategies were reported in [8], [18]. However, for IoT devices with limited battery capacity, excess power consumption is not always affordable.…”

Section: Introductionmentioning

confidence: 99%

Secure Mobile Edge Computing in IoT via Collaborative Online Learning

Chen

Giannakis

2019

IEEE Trans. Signal Process.

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To accommodate heterogeneous tasks in Internet of Things (IoT), a new communication and computing paradigm termed mobile edge computing emerges that extends computing services from the cloud to edge, but at the same time exposes new challenges on security. The present paper studies online securityaware edge computing under jamming attacks. Leveraging online learning tools, novel algorithms abbreviated as SAVE-S and SAVE-A are developed to cope with the stochastic and adversarial forms of jamming, respectively. Without utilizing extra resources such as spectrum and transmission power to evade jamming attacks, SAVE-S and SAVE-A can select the most reliable server to offload computing tasks with minimal privacy and security concerns. It is analytically established that without any prior information on future jamming and server security risks, the proposed schemes can achieve O √ T regret. Information sharing among devices can accelerate the security-aware computing tasks. Incorporating the information shared by other devices, SAVE-S and SAVE-A offer impressive improvements on the sublinear regret, which is guaranteed by what is termed "value of cooperation." Effectiveness of the proposed schemes is tested on both synthetic and real datasets.

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Anti-jamming Strategy Versus a Low-Power Jamming Attack When Intelligence of Adversary’s Attack Type is Unknown

Cited by 39 publications

References 29 publications

Two-dimensional anti-jamming communication based on deep reinforcement learning

Two-dimensional anti-jamming communication based on deep reinforcement learning

Topology control in the presence of jammers for wireless sensor networks

Secure Mobile Edge Computing in IoT via Collaborative Online Learning

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