User-Centric View of Unmanned Aerial Vehicle Transmission Against Smart Attacks

Xiao, Liang; Xie, Chaotian; Min, Minghui; Zhuang, Weihua

doi:10.1109/tvt.2017.2785414

Cited by 133 publications

(92 citation statements)

References 32 publications

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“…2) Proposed Analytical Approximation: Here, we propose an approximate closed-form solution for P ⋆ J for the asymptotic case with γ SU ≫ 1 and γ AU ≫ 1 along with P J ≫ P ft [7]. Under these approximations, using (4) and (5), objective function of (P3) can be reduced to:…”

Section: Optimal Jamming Powermentioning

confidence: 99%

“…Recent studies have shown interest on optimizing the EE of jamming attacker [3], [4]. Whereas optimal attacking strategies for hybrid attacker are designed in [5], [6] to achieve maximum degradation in the secrecy rate, however EE aspect has been neglected. Further, decoding-based energy-expenditures in eavesdropping and practical circuit-level consumption during jamming have been ignored while considering energy consumption at attacker [3]- [7].…”

Section: Introductionmentioning

confidence: 99%

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Optimal Green Hybrid Attacks in Secure IoT

Ahuja

Mishra

Bose

2020

IEEE Wireless Commun. Lett.

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In pursuance to understand the behavior of a potential green hybrid attacker in secure internet-of-things (IoT), this letter investigates optimal energy utilization from the attacker's viewpoint. Specifically, we propose a novel framework for optimizing the efficacy of a hybrid attacker, possessing capability to both eavesdrop and jam, while considering the underlying energy consumption. In particular, we maximize the attacker energy efficiency (AEE) in secure IoT by deriving the analytical solutions for jointly global-optimal attacking mode, eavesdropping rate, and jamming power. Numerical results, validating analytical claims, reveal that the proposed green design can offer about 45% improvement in average AEE over the relevant benchmarks.

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Section: Optimal Jamming Powermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimal Green Hybrid Attacks in Secure IoT

Ahuja

Mishra

Bose

2020

IEEE Wireless Commun. Lett.

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“…LMI conditions are derived for parameter estimation in the attack design. It is important to note that the model‐based (deep, Nash) Q ‐learning technique has been well used in the secure defense against network threat, outside of the data‐driven DoS attack design.…”

Section: Resultsmentioning

confidence: 99%

Data‐based optimal Denial‐of‐Service attack scheduling against robust control based on Q‐learning

Yang

2019

Intl J Robust & Nonlinear

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Attack optimization is an important issue in securing cyber-physical systems. This paper investigates how an attacker should schedule its denial-of-service attacks to degrade the robust performance of a closed-loop system. The measurements of system states are transmitted to a remote controller over a multichannel network. With limited resources, the attacker only has the capacity to jam sparse channels and to decide which channels should be attacked. Under an  2 framework, a data-based optimal attack strategy that uses Q-learning is proposed to maximize the effect on the closed-loop system. The Q-learning algorithm can adaptively learn the optimal attack using data sniffed over the wireless network without requiring a priori knowledge of system parameters. Simulation results sustain the performance of the proposed attack scenario. KEYWORDS Cyber-physical systems, DoS attacks, multichannel transmission, Q-learning, robust control 5178 FIGURE 1 System architecture. DoS, denial of service [Colour figure can be viewed at wileyonlinelibrary.com] on perfect communication if there is no DoS attack, the work of Qin et al 17 further studies the problem of DoS attack strategy design over unreliable networks where the packet dropouts may also occur even in the absence of attacks.Note that the aforementioned results on the optimal DoS attack design are established based on the remote state estimation framework (degrade the estimation quality). To fill this gap from the aspect of closed-loop control performance, the works by Amin et al and Zhang et al 18,19 develop the optimal DoS attack scheduling against linear quadratic Gaussian (LQG) control. Further, the work reported by Zhang and Zheng 20 considers the problem of optimal attack power dispatch for CPSs within the LQG framework. In the work of Gupta et al, 21 a game theory approach is proposed to design the jamming attack strategy, which can destroy a finite-time linear quadratic control performance.Most previous attack strategies require a large amount of exact system information, including physical system parameters and the network topology. However, adversaries are often with limited resources, and as a result, the likelihood to access the complete knowledge of system parameters is either infeasible or very difficult to achieve. 22,23 Recently, some data-driven false data injection attack design schemes have been developed. 4,24 However, there are few studies undertaking an analysis of the data-driven DoS attacks. Moreover, the system robustness, which is the ability of a CPS against exogenous interference, is also an important security metric, which is concerned by both defenders and attackers and has not been analyzed yet.Motivated by these, this paper investigates the problem of DoS attack scheduling against robust control for a CPS with multichannel transmission (see Figure 1). The measurement signals of the CPS are clustered into multiple groups and sent via multiple wireless channels. Here, we assume that the DoS attacker has no knowledge of system parameters, ...

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“…The simulation results in [118] show that the proposed DQL can improve the UAV's utility up to 13% compared with the baseline scheme [119] which uses the Win or Learn Faster-Policy Hill Climbing (WoLF-PHC) to prevent the attack. Also, the safe rate of the UAV, i.e., the probability that the UAV is attacked, obtained by the proposed DQL is 7% higher than that of the baseline.…”

Section: A Network Securitymentioning

confidence: 99%

Applications of Deep Reinforcement Learning in Communications and Networking: A Survey

Luong

Hoang

Gong

et al. 2019

IEEE Commun. Surv. Tutorials

1,305

481

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This paper presents a comprehensive literature review on applications of deep reinforcement learning in communications and networking. Modern networks, e.g., Internet of Things (IoT) and Unmanned Aerial Vehicle (UAV) networks, become more decentralized and autonomous. In such networks, network entities need to make decisions locally to maximize the network performance under uncertainty of network environment. Reinforcement learning has been efficiently used to enable the network entities to obtain the optimal policy including, e.g., decisions or actions, given their states when the state and action spaces are small. However, in complex and large-scale networks, the state and action spaces are usually large, and the reinforcement learning may not be able to find the optimal policy in reasonable time. Therefore, deep reinforcement learning, a combination of reinforcement learning with deep learning, has been developed to overcome the shortcomings. In this survey, we first give a tutorial of deep reinforcement learning from fundamental concepts to advanced models. Then, we review deep reinforcement learning approaches proposed to address emerging issues in communications and networking. The issues include dynamic network access, data rate control, wireless caching, data offloading, network security, and connectivity preservation which are all important to next generation networks such as 5G and beyond. Furthermore, we present applications of deep reinforcement learning for traffic routing, resource sharing, and data collection. Finally, we highlight important challenges, open issues, and future research directions of applying deep reinforcement learning.

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User-Centric View of Unmanned Aerial Vehicle Transmission Against Smart Attacks

Cited by 133 publications

References 32 publications

Optimal Green Hybrid Attacks in Secure IoT

Optimal Green Hybrid Attacks in Secure IoT

Data‐based optimal Denial‐of‐Service attack scheduling against robust control based on Q‐learning

Applications of Deep Reinforcement Learning in Communications and Networking: A Survey

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