Leveraging Jamming to Help Drones Complete Their Mission

Tedeschi, Pietro; Oligeri, Gabriele; Pietro, Roberto Di

doi:10.1109/access.2019.2963105

Cited by 24 publications

(10 citation statements)

References 45 publications

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“…Regarding UAVs, with a firmware-coded safety mode, an attacker can force a drone to land when both the GPS signal and C2 link are jammed ( Tedeschi, Oligeri & Di Pietro, 2020 ). In 2012, a small UAV resulted in a crash due to GPS jamming, costing a human life ( Krishna & Murphy, 2017 ).…”

Section: Gps Threat Landscapementioning

confidence: 99%

“…For mitigating the effects of jamming on UAV, Tedeschi, Oligeri & Di Pietro (2020) presented an anti-jamming navigational algorithm by leveraging the jamming signal for localization of the source through Received Signal Strength (RSS). The proposed approach requires considerable software and hardware upgradations for implementing anti-jamming features, including an on-board SDR for jammer’s localization and a custom-built firmware.…”

Section: Gps Threat Landscapementioning

confidence: 99%

See 1 more Smart Citation

On GPS spoofing of aerial platforms: a review of threats, challenges, methodologies, and future research directions

Khan

Mohsin

Iqbal³

2021

PeerJ Computer Science

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Unmanned Aerial Systems (UAVs, Drones), initially known only for their military applications, are getting increasingly popular in the civil sector as well. Over the military canvas, drones have already proven themselves as a potent force multiplier through unmanned, round-the-clock, long-range and high-endurance missions for surveillance, reconnaissance, search and rescue, and even armed combat applications. With the emergence of the Internet of Things (IoT), commercial deployments of drones are also growing exponentially, ranging from cargo and taxi services to agriculture, disaster relief, risk assessment and monitoring of critical infrastructures. Irrespective of the deployment sector, drones are often entrusted to conduct safety, time and liability critical tasks, thus requiring secure, robust and trustworthy operations. In contrast, the rise in UAVs’ demand, coupled with market pressure to reduce size, weight, power and cost (SwaP-C) parameters, has caused vendors to often ignore security aspects, thus inducing serious safety and security threats. As UAVs rely on Global Positioning System (GPS) for positioning and navigation, they can fall prey to GPS jamming and spoofing attacks. The vulnerability of GPS to spoofing has serious implications for UAVs, as victim drones using civil GPS can be misdirected or even completely hijacked for malicious intents, as already demonstrated in several academic research efforts using commercially available GPS spoofing hardware. Beside UAVs, GPS spoofing attacks are equally applicable to other GPS-dependent platforms, including manned aircraft, ground vehicles, and cellular systems. This paper conducts a comprehensive review of GPS spoofing threats, with a special focus on their applicability over UAVs and other GPS-dependent mobile platforms. It presents a novel taxonomy of GPS spoofing attacks and critically analyzes different spoofing techniques based upon placement of spoofing device, attack stealthiness, attack methodologies, and objectives of the attacker. We also discuss some of the recent experiments from open literature which utilized commercially available hardware for successfully conducting spoofing attacks.

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Section: Gps Threat Landscapementioning

confidence: 99%

Section: Gps Threat Landscapementioning

confidence: 99%

On GPS spoofing of aerial platforms: a review of threats, challenges, methodologies, and future research directions

Khan

Mohsin

Iqbal³

2021

PeerJ Computer Science

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“…This work focuses on learningbased anti-jamming for spectral retreat strategy against both jamming attacks and interference among nodes. Antijamming learning-based systems have been extensively studied in different approaches which, recently, focus on improving the learning algorithm's performance by solving jamming in multi-dimensional or multi-layers [12]- [15], handling the lack of meaningful or clear jammer's information [16]- [18], or improving on the slowness of the algorithms [19]- [22].…”

Section: Related Work a Anti-jammingmentioning

confidence: 99%

“…Meanwhile, whenever communicating nodes have no information about the jammer, anti-jamming activities may require more strategies. Therefore, to deal with the lack of information about the jammer, [16] developed an approach that takes advantage of the jamming signal to achieve the mission where the communicating node, a drone, leverages the jammer signal. In [18], they study anti-jamming in an unknown and changing environment by utilizing the waterfall spectrum information directly; thus, constructing an anti-jamming Q-network.…”

Section: Related Work a Anti-jammingmentioning

confidence: 99%

Deep Multi-Task Conditional and Sequential Learning for Anti-Jamming

Basomingera¹,

Choi

2021

IEEE Access

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Multi-task learning provides plenty of room for performance improvement to single-task learning, when learned tasks are related and learned with mutual information. In this work, we analyze the efficiency of using a single-task reinforcement learning algorithm to mitigate jamming attacks with frequency hopping strategy. Our findings show that single-task learning implementations do not always guarantee optimal cumulative reward when some jammer's parameters are unknown, notably the jamming time-slot length in this case. Therefore, to maximize packet transmission in the presence of a jammer whose parameters are unknown, we propose deep multi-task conditional and sequential learning (DMCSL), a multi-task learning algorithm that builds a transition policy to optimize conditional and sequential tasks. For the antijamming system, the proposed model learns two tasks: sensing time and transmission channel selection. DMCSL is a composite of the state-of-the-art reinforcement learning algorithms, multi-armed bandit and an extended deep-Q-network. To improve the chance of convergence and optimal cumulative reward of the algorithm, we also propose a continuous action-space update algorithm for sensing time action-space. The simulation results show that DMCSL guarantees better performance than single-task learning by relying on a logarithmically increased action-space sample. Against a random dynamic jamming time-slot, DMCSL achieves about three times better cumulative reward, and against a periodic dynamic jamming time-slot, it improves by 10% the cumulative reward.INDEX TERMS Ad hoc network, deep learning, jamming attack, multi-armed bandit, spectrum sensing.

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“…The current technology has many drawbacks, as illustrated by the following examples (Cureton, 2020): -The jammer device can cut off the radio-frequency links with drones, but it absolutely cannot neutralise autonomous pilot drones (Tedeschi, Oligeri and Di Pietro, 2020); -Radar cannot detect low cross-section devices (Yaacoub et al, 2020); -The jammer cannot stop drones working with a frequency link range higher than 6.2 GHz (Chamola et al, 2021);…”

Section: Introductionmentioning

confidence: 99%

High power microwave for knocking out programmable suicide drones

Chaari¹

2021

Security and Defence Quarterly

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The primary research objective is to reduce the dangers of rogue drones in our lives and the consequences of extremist groups, drug dealers, and organised criminals using them. The growing number of incidents involving modified drones proves the weakness of existing technology in stopping and neutralising errant drones such as the hand-held gun jammer, trained eagle, R.F. jammer, and others. This technology is not very likely to able to knock out a rogue drone and is incapable of stopping programmable drones. This article aims to examine the directed energy of HPM (high power microwaves) in using the electromagnetic field strength energy to damage the drone's structure or burn its PCB board electronics. It goes on to analyse electronic attack using microwave power with high frequency to immediately switch off drones. The effectiveness of high microwave power for disrupting drones at different distances and in different weather conditions is evaluated. A study of the conical horn antenna of the magnetron coupling system, which has an operating frequency of 2.45 GHz, is also included.

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Leveraging Jamming to Help Drones Complete Their Mission

Cited by 24 publications

References 45 publications

On GPS spoofing of aerial platforms: a review of threats, challenges, methodologies, and future research directions

On GPS spoofing of aerial platforms: a review of threats, challenges, methodologies, and future research directions

Deep Multi-Task Conditional and Sequential Learning for Anti-Jamming

High power microwave for knocking out programmable suicide drones

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