A Comparative Analysis of the Ensemble Models for Detecting GPS Spoofing attacks on UAVs

Gasimova, Aydan; Khoei, Tala Talaei; Kaabouch, Naima

doi:10.1109/ccwc54503.2022.9720738

Cited by 13 publications

(2 citation statements)

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“…Gasimova et al [112] conducted a comparative study of three Ensemble based machine learning models. Namely, Bagging (Ba.…”

Section: B Gps Spoofing Defense Mechanisms In Uavsmentioning

confidence: 99%

GPS Spoofing Attacks in FANETs: A Systematic Literature Review

Altaweel,

Mukkath,

Kamel

2023

IEEE Access

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Flying Ad-Hoc Networks (FANETs) are groups of UAVs connected in an Ad-Hoc manner to accomplish a common mission. The widespread acceptance of UAVs due to their low cost and high efficiency has attracted malicious security attacks against them. These attacks cannot be easily prevented due to UAVs' limited computational power, short battery life, and inability to execute complex algorithms. FANETs rely on the Global Positioning System (GPS) for localization. GPS Spoofing, an easy-to-launch attack, is one of the main challenges in FANETs. In which, the legitimate and not-encrypted civilian GPS signals are overridden by counterfeit signals to deceive the UAVs to collide or to be hijacked. Researchers proposed various countermeasures to address GPS Spoofing attacks in FANETs. To further assist future research, this paper provides a systematic literature review on GPS Spoofing attacks in FANETs and their defense mechanisms. After formulating eight research questions and applying well-defined inclusion/exclusion criteria and quality assessment tools, 70 research articles were extracted. The existing defense mechanisms were classified based on their objectives (i.e., detection, mitigation, and/or prevention) and according to their basis (i.e., relying on readings from various drones' devices/sensors, processing the signals received by various sensors, employing machine learning algorithms, relying on game theory, or leveraging cryptographic techniques to authenticate and protect the confidentiality of GPS signals). The defense mechanisms were also analyzed to identify the attacker models, impacts of the attack, and detection performance. This study found that most of the proposed methods are detection approaches, rather than mitigation or prevention. Also, almost all papers used simulation experiments rather than a proof-of-concept implementation, which does not demonstrate a convincing performance under realistic mobility and propagation models. Moreover, most solutions addressed GPS Spoofing for a single UAV. Only eight articles addressed multiple UAV scenarios and none of them provided a proof-of-concept evaluation. INDEX TERMSDrones, unmanned ariel vehicle (UAV), Flying Ad-Hoc NETwork (FANET), GPS spoofing attack, wireless network security, systematic literature review. * Spoofing tools such as simulators, repeaters, etc. * Spoofing techniques such as position denial and track break. * Spoofer location such as remote, onboard, and escort. --Defense mechanisms against GPS Spoofing: the research articles that discuss various mechanisms to defend against GPS Spoofing attacks in UAVs. These articles are further classified based on their objectives as follows: * Detection of GPS Spoofing attacks. * Mitigation the damage caused by GPS Spoofing attacks. * Prevention of GPS Spoofing. The defense mechanisms are also classified according to their basis as follows: * The use of devices/sensors on the drones. * Signal processing techniques applied on various signals received by various sensors on the drone. * Cryptographic techn...

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“…Gasimova et al [112] conducted a comparative study of three Ensemble based machine learning models. Namely, Bagging (Ba.…”

Section: B Gps Spoofing Defense Mechanisms In Uavsmentioning

confidence: 99%

GPS Spoofing Attacks in FANETs: A Systematic Literature Review

Altaweel,

Mukkath,

Kamel

2023

IEEE Access

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“…Machine learning algorithms can also detect GPS spoofing of UAVs. In [17], the ensemble models of machine learning were used to detect GPS spoofing signals. A support vector machine (SVM) outperforms conventional detection methods [18].…”

Section: Gps Spoofing In Uavsmentioning

confidence: 99%

Knowledge Distillation-Based GPS Spoofing Detection for Small UAV

Ren,

Restivo,

Tan

et al. 2023

Future Internet

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As a core component of small unmanned aerial vehicles (UAVs), GPS is playing a critical role in providing localization for UAV navigation. UAVs are an important factor in the large-scale deployment of the Internet of Things (IoT) and cyber–physical systems (CPS). However, GPS is vulnerable to spoofing attacks that can mislead a UAV to fly into a sensitive area and threaten public safety and private security. The conventional spoofing detection methods need too much overhead, which stops efficient detection from working in a computation-constrained UAV and provides an efficient response to attacks. In this paper, we propose a novel approach to obtain a lightweight detection model in the UAV system so that GPS spoofing attacks can be detected from a long distance. With long-short term memory (LSTM), we propose a lightweight detection model on the ground control stations, and then we distill it into a compact size that is able to run in the control system of the UAV with knowledge distillation. The experimental results show that our lightweight detection algorithm runs in UAV systems reliably and can achieve good performance in GPS spoofing detection.

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