Drones in Distress: A Game-Theoretic Countermeasure for Protecting UAVs Against GPS Spoofing

Eldosouky, AbdelRahman; Ferdowsi, Aidin; Saad, Walid

doi:10.1109/jiot.2019.2963337

Cited by 115 publications

(53 citation statements)

References 30 publications

(54 reference statements)

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“…Stackelberg games [47] can be used to obtain flight routes for uavs operating in areas with malicious parties carrying out gps spoofing attacks aimed to divert uavs from their original flight paths [12]. In a Stackelberg game between a uav operator acting as the game leader and a gps spoofer, the leader chooses a group of uavs to protect, after which the spoofer determines its actions after observing the decision of the leader.…”

Section: Game Theory Applications For Swarms and Uavsmentioning

confidence: 99%

Artificial intelligence and game theory controlled autonomous UAV swarms

Kusyk

Samoylov

et al. 2020

Evol. Intel.

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Autonomous unmanned aerial vehicles (uavs) operating as a swarm can be deployed in austere environments, where cyber electromagnetic activities often require speedy and dynamic adjustments to swarm operations. Use of central controllers, uav synchronization mechanisms or pre-planned set of actions to control a swarm in such deployments would hinder its ability to deliver expected services. We introduce artificial intelligence and game theory based flight control algorithms to be run by each autonomous uav to determine its actions in near real-time, while relying only on local spatial, temporal and electromagnetic (em) information. Each uav using our flight control algorithms positions itself such that the swarm maintains mobile ad-hoc network (manet) connectivity and uniform asset distribution over an area of interest. Typical tasks for swarms using our algorithms include detection, localization and tracking of mobile em transmitters. We present a formal analysis showing that our algorithms can guide a swarm to maintain a connected manet, promote a uniform network spreading, while avoiding overcrowding with other swarm members. We also prove that they maintain manet connectivity and, at the same time, they can lead a swarm of autonomous uavs to follow or avoid an em transmitter. Simulation experiments in opnet modeler verify the results of formal analysis that our algorithms are capable of providing an adequate area coverage over a mobile em source and maintain manet connectivity. These algorithms are good candidates for civilian and military applications that require agile responses to the changes in dynamic environments for tasks such as detection, localization and tracking mobile em transmitters.

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Section: Game Theory Applications For Swarms and Uavsmentioning

confidence: 99%

Artificial intelligence and game theory controlled autonomous UAV swarms

Kusyk

Samoylov

et al. 2020

Evol. Intel.

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“…We introduce a simple flight guidance system which lets terrestrial network nodes cooperatively guide a single or multiple UAVs. As [43] already presented, the Ground Positioning System (GPS) might be insecure if attackers falsify global location information. In the case in which a UAV cannot trust its GPS device, we considered a possibility to obtain the correct direction by requesting to the nearby nodes.…”

Section: Uav Flight Guidance Systemmentioning

confidence: 99%

Devising a Distributed Co-Simulator for a Multi-UAV Network

Park

Lee

et al. 2020

Sensors

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Practical evaluation of the Unmanned Aerial Vehicle (UAV) network requires a lot of money to build experiment environments, which includes UAVs, network devices, flight controllers, and so on. To investigate the time-sensitivity of the multi-UAV network, the influence of the UAVs’ mobility should be precisely evaluated in the long term. Although there are some simulators for UAVs’ physical flight, there is no explicit scheme for simulating both the network environment and the flight environments simultaneously. In this paper, we propose a novel co-simulation scheme for the multiple UAVs network, which performs the flight simulation and the network simulation simultaneously. By considering the dependency between the flight status and networking situations of UAV, our work focuses on the consistency of simulation state through synchronization among simulation components. Furthermore, we extend our simulator to perform multiple scenarios by exploiting distributed manner. We verify our system with respect to the robustness of time management and propose some use cases which can be solely simulated by this.

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“…This makes hardware trojans a serious security risk to an IC. Moreover, the impact of HTs is exacerbated when the infected ICs are used in cyber physical systems, e.g., cognitive radios [7], IoT health systems [8], robotics [9], and unmanned aerial vehicles [10], as HTs can facilitate cyber-physical attacks in such systems.…”

Section: Introductionmentioning

confidence: 99%

Think Smart, Play Dumb: Analyzing Deception in Hardware Trojan Detection Using Game Theory

Das¹

2020

Preprint

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In recent years, integrated circuits (ICs) have become<br>significant for various industries and their security has<br>been given greater priority, specifically in the supply chain.<br>Budgetary constraints have compelled IC designers to offshore manufacturing to third-party companies. When the designer gets the manufactured ICs back, it is imperative to test for potential threats like hardware trojans (HT). In this paper, a novel multilevel game-theoretic framework is introduced to analyze the interactions between a malicious IC manufacturer and the tester. In particular, the game is formulated as a non-cooperative, zerosum, repeated game using prospect theory (PT) that captures different players’ rationalities under uncertainty. The repeated game is separated into a learning stage, in which the defender<br><div>learns about the attacker’s tendencies, and an actual game stage, where this learning is used. Experiments show great incentive for the attacker to deceive the defender about their actual rationality by “playing dumb” in the learning stage (deception). This scenario is captured using hypergame theory to model the attacker’s view of the game. The optimal deception rationality of the attacker is analytically derived to maximize utility gain. For the defender, a first-step deception mitigation process is proposed to thwart the effects of deception. Simulation results show that the attacker can profit from the deception as it can successfully insert HTs in the manufactured ICs without being detected.</div><div><br></div><div>This paper has been accepted for publication in <b>IEEE Cyber Science Conference 2020</b><br></div>

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Drones in Distress: A Game-Theoretic Countermeasure for Protecting UAVs Against GPS Spoofing

Cited by 115 publications

References 30 publications

Artificial intelligence and game theory controlled autonomous UAV swarms

Artificial intelligence and game theory controlled autonomous UAV swarms

Devising a Distributed Co-Simulator for a Multi-UAV Network

Think Smart, Play Dumb: Analyzing Deception in Hardware Trojan Detection Using Game Theory

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