Testbed for Cooperative Jamming Cancellation in Physical Layer Security

Guo, Wenbo; Zhao, Hongzhi; Tang, Youxi

doi:10.1109/lwc.2019.2950303

Cited by 32 publications

(13 citation statements)

References 11 publications

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“…Moreover, by evaluating of the limits 0 = lim ξ→0 ∂ RLB s /∂ξ and 1 = lim ξ→1 ∂ RLB s /∂ξ via utilizing L'Hôpital's rule, one can easily infer that 0 > 0 and 1 < 0, hence a unique globally optimal point of ξ exists. This fact is also validated in our simulation results in Section V. Therefore, with the aim of maximizing the ESR in (31), it suffices to invoke (32) and solve ∂ RLB s ∂ξ = 0. Toward this end, the well-known and easyto-implement bisection approach is used in this paper [27].…”

Section: Optimal Power Allocationsupporting

confidence: 65%

“…in [31] proposed a secure transmission from a multi-antenna controller to an actuator in an IoT network by deploying AN injection technique and utilizing an external multi-antenna cooperative jammer. Recently, the researchers in [32] have proposed a practical testbed to ensure secure transmission for a pointto-point communication. They utilized an external node as the cooperative jammer to overcome the passive eavesdropping.…”

Section: B Related Workmentioning

confidence: 99%

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A Lightweight Secure and Resilient Transmission Scheme for the Internet of Things in the Presence of a Hostile Jammer

Letafati

Kuhestani

Wong

et al. 2021

IEEE Internet Things J.

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In this paper, we propose a lightweight security scheme for ensuring both information confidentiality and transmission resiliency in the Internet-of-Things (IoT) communication. A single-antenna transmitter communicates with a halfduplex single-antenna receiver in the presence of a sophisticated multiple-antenna-aided passive eavesdropper and a multipleantenna-assisted hostile jammer (HJ). A low-complexity artificial noise (AN) injection scheme is proposed for drowning out the eavesdropper. Furthermore, for enhancing the resilience against HJ attacks, the legitimate nodes exploit their own local observations of the wireless channel as the source of randomness to agree on shared secret keys. The secret key is utilized for the frequency hopping (FH) sequence of the proposed communication system. We then proceed to derive a new closed-form expression for the achievable secret key rate (SKR) and the ergodic secrecy rate (ESR) for characterizing the secrecy-benefits of our proposed scheme, in terms of both information secrecy and transmission resiliency. Moreover, the optimal power sharing between the AN and the message signal is investigated with the objective of enhancing the secrecy rate. Finally, through extensive simulations, we demonstrate that our proposed system model outperforms the state-of-the-art transmission schemes in terms of secrecy and resiliency. Several numerical examples and discussions are also provided to offer further engineering insights.

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Section: Optimal Power Allocationsupporting

confidence: 65%

Section: B Related Workmentioning

confidence: 99%

A Lightweight Secure and Resilient Transmission Scheme for the Internet of Things in the Presence of a Hostile Jammer

Letafati

Kuhestani

Wong

et al. 2021

IEEE Internet Things J.

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“…In contrast, we propose a hybrid transmission policy according to the type of the source packets scheduled to be transmitted. When sending RoI-related packets, our proposed system model deploys maximum ratio transmission (MRT) beamforming together with artificial noise (AN) injection, as a practical and low cost PLS technique [22], to achieve a physical enhancement for the legitimate link, while degrading the eavesdropping ability of potential Eves. For the transmission of BG packets, we simply deploy MRT in order to save power consumption and improve the packet accumulation ability of legitimate receiver, since the BG regions require less stringent security.…”

Section: Contributionsmentioning

confidence: 99%

“…Traditionally, ( 22) could be solved via numerical algorithms leading to a large computational complexity. In contrary, we propose a learning-based scheme to solve (22) in an efficient way.…”

Section: Optimized Learning-based Image Transmissionmentioning

confidence: 99%

“…6 After that the training phase is completed, i,e, the minimization problem of ( 25) converges to a relatively low MSE, our DNN achieves an acceptable approximation for the mapping in (23). Afterward, when a new configuration, r new , is defined for the system, i.e., a new image is scheduled to be sent or a new legitimate node is signed in to the network, the corresponding transmission parameters can be obtained without the need to solve (22) again. Instead, it suffices to compute the output of the trained DNN, using a forward propagation with the new input r new in a real-time manner.…”

Section: B Training Of the Proposed Dnn: Train Offline Use Onlinementioning

confidence: 99%

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On Learning-Assisted Content-Based Secure Image Transmission for Delay-Aware Systems with Randomly-Distributed Eavesdroppers -- Extended Version

Letafati,

Behroozi,

Khalaj

et al. 2021

Preprint

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In this paper, a learning-aided content-based wireless image transmission scheme is proposed, where a multi-antenna-aided source wishes to securely deliver an image to a legitimate destination in the presence of randomly distributed eavesdroppers (Eves). We take into account the fact that not all regions of an image have the same importance from the security perspective. Hence, we propose a transmission scheme, where the source employs a hybrid method to realize both the error-free data delivery of public regions-containing less-important pixels; and an artificial noise (AN)-aided transmission scheme to provide security for the regions containing large amount of information. Moreover, in order to reinforce system's security, fountain-based packet delivery is adopted: First, the source node encodes image packets into fountain-like packets prior to sending them over the air. The secrecy of our proposed scheme will be achieved if the legitimate destination correctly receives the entire image source packets, while conforming to the latency limits of the system, before Eves can obtain the important regions.Accordingly, the secrecy performance of our scheme is characterized by deriving the closed-form expression for the quality-of-security (QoSec) violation probability. Moreover, our proposed wireless image delivery scheme leverages the deep neural network (DNN) and learns to maintain optimized transmission parameters, while achieving a low QoSec violation probability. Simulation results are provided with some useful engineering insights which illustrate that our proposed learning-assisted

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