Deep Learning Detection of GPS Spoofing

Jullian, Olivia; Otero, Beatriz; Stojilović, Mirjana; Costa, Juan José; Verdú, Javier; Pajuelo, Manuel Alejandro

doi:10.1007/978-3-030-95467-3_38

Cited by 11 publications

(8 citation statements)

References 15 publications

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“…To date, recent papers have not classified attack models and explicitly presented attack methods with respect to EKF sensor fusion's spoofing detection in PX4. Especially, the recent spoofing detection for PX4 mainly depends on machine learning [5], [6], [12]. In this paper, we classified the attack model and analyzed attack methods avoiding EKF sensor fusion detection in PX4.…”

Section: Discussionmentioning

confidence: 99%

“…Thus, the detection of the attack only depends on using UAV's internal system like the IMU sensor, and EKF algorithm. Many works [5], [6], [12], [16], [17], [19] have been conducted on the detection of GPS spoofing attacks using the estimated by IMU sen-sor or machine learning. Currently, PX4 has a function that does not reflect the position observed by GPS if there is a difference between the position estimated by EKF (mainly using IMU) and the position observed by GPS above a certain level.…”

Section: B Ekf Sensor Fusionmentioning

confidence: 99%

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An Analysis of GPS Spoofing Attack and Efficient Approach to Spoofing Detection in PX4

Jung,

Hong,

Choi

et al. 2024

IEEE Access

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Unmanned Aerial Vehicles (UAVs) are aerial vehicles that can go to a particular position without human control or with remote human control. It is because unmanned control mainly relies on position estimation that a lot of research has been studied on GPS spoofing attacks. Detection methods against GPS spoofing attacks mainly include monitoring RF signals or IMU sensor values inside UAVs. In this work, we analyze GPS attack and detection in an advanced autopilot system, PX4 without excluding RF detection. Recent studies have shown that GPS spoofing is unable to evade the detection using EKF sensor fusion. Therefore, this paper experiment whether the detection could be evaded by strengthening the attacker model. This paper classifies attacker models according to whether an attacker knows the true position of UAVs or the estimated position by UAV and proposed attack methods depending on each model in PX4. We inject GPS value which our attack method intends to UAV during mission flight in simulation environment. By manipulating the GPS driver code of PX4 controller, we inject GPS value the attack method wants into UAV in physical environment. Finally, we observed the innovation test ratio during GPS spoofing and demonstrate that GPS spoofing attack is possible keeping the innovation test ratio under the anomaly detection criterion. After that, we proposed our approach which scales the EKF's Kalman gain randomly to increase the detection method's efficiency and experiment with the attack methods. This detection method has little effect on the test ratio without the attack. But during the attack, the innovation test ratio was increased higher than the anomaly detection criterion so that the attack could be detected.

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Section: Discussionmentioning

confidence: 99%

Section: B Ekf Sensor Fusionmentioning

confidence: 99%

An Analysis of GPS Spoofing Attack and Efficient Approach to Spoofing Detection in PX4

Jung,

Hong,

Choi

et al. 2024

IEEE Access

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“…Scalability concerns for complex spoofing scenarios; real-world validation required. [42] 2022 TEXBAT dataset and MAVLINK dataset…”

Section: Deep Ensemble Learning Methodsmentioning

confidence: 99%

“…When two deep learning models were evaluated, the researchers discovered that MLP performed better than LSTM. Their method accurately identified GPS spoofing attacks with accuracies of 83.2%(TEXBAT dataset) and 99.9% (MAVLINK dataset) [42]. Dang et al [41] investigated the effectiveness of statistics from the base stations for spoofing attack detection on cellular UAVs.…”

Section: Gps Spoofing Attacks Classification Using Deep Learning Modelsmentioning

confidence: 99%

Enhanced Machine Learning Ensemble Approach for Securing Small Unmanned Aerial Vehicles From GPS Spoofing Attacks

Eshmawi,

Umer,

Ashraf

et al. 2024

IEEE Access

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Unmanned aerial vehicles (UAVs) substantially rely on the utilization of global positioning systems (GPS) to navigate. A simulator for commercial GPS applications with false GPS signals can lead to the deviation of a GPS-guided drone from its planned path. As a result, an anti-spoofing technology is required to assure UAV operating safety. Several approaches have been developed to detect GPS spoofing, however, predominantly such methods rely on additional hardware. Using additional hardware might not be an ideal solution for small and low-capacity UAVs. Detecting signal spoofing attacks in small UAVS has significant importance. This study presents a stacked ensemble approach to detect GPS signal spoofing within the context of small UAVs. The initial phase involves outlining the sequential procedures for obtaining and preparing the GPS signal dataset, including details about the UAV hardware, blocker, data collection timing, environmental factors, and the utilization of z-score normalization for preprocessing. Then controlled simulation tests with varying experimental conditions are conducted and the model is built using a support vector machine and convolutional neural network. Additionally, a comprehensive comparative assessment is conducted to analyze the efficacy of the proposed model against traditional machine learning models. Experimental results demonstrate notably good performance by the proposed model with a 99.74% accuracy, showing its superior performance in the context of GPS signal spoofing in small UAVs.

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“…Attackers exploit vulnerabilities in IoT networks, aiming to compromise critical or sensitive data through activities such as data interception, modification, or corruption [56]. The resource limitations of IoT devices hinder the deployment of complex security mechanisms that necessitate substantial memory and computational power [23]. Consequently, IoT devices remain susceptible to various cyber-attacks.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Deep Learningbased Intrusion Detection System for Internet of Things Networks with Hybrid Feature Reduction and Data Balancing Techniques

Karamollaoğlu,

Doğru,

Yücedağ

2024

ITC

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With the increasing use of Internet of Things (IoT) technologies, cyber-attacks on IoT devices are also increasing day by day. Detecting attacks on IoT networks before they cause any damage is crucial for ensuring the security of the devices on these networks. In this study, a novel Intrusion Detection System (IDS) was developed for IoT networks. The IoTID20 and BoT-IoT datasets were utilized during the training phase and performance testing of the proposed IDS. A hybrid method combining the Principal Component Analysis (PCA) and the Bat Optimization (BAT) algorithm was proposed for dimensionality reduction on the datasets. The Synthetic Minority Over-SamplingTechnique (SMOTE) was used to address the problem of data imbalance in the classes of the datasets. The Convolutional Neural Networks (CNN) model, a deep learning method, was employed for attack classification. The proposed IDS achieved an accuracy rate of 99.97% for the IoTID20 dataset and 99.98% for the BoT-IoT dataset in attack classification. Furthermore, detailed analyses were conducted to determine the effects of the dimensionality reduction and data balancing models on the classification performance of the proposed IDS.

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Deep Learning Detection of GPS Spoofing

Cited by 11 publications

References 15 publications

An Analysis of GPS Spoofing Attack and Efficient Approach to Spoofing Detection in PX4

An Analysis of GPS Spoofing Attack and Efficient Approach to Spoofing Detection in PX4

Enhanced Machine Learning Ensemble Approach for Securing Small Unmanned Aerial Vehicles From GPS Spoofing Attacks

An Efficient Deep Learningbased Intrusion Detection System for Internet of Things Networks with Hybrid Feature Reduction and Data Balancing Techniques

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