Prediction‐discrepancy based on innovative particle filter for estimating UAV true position in the presence of the GPS spoofing attacks

Majidi, Mohammad Ali; Khaloozadeh, Hamid

doi:10.1049/iet-rsn.2019.0520

Cited by 22 publications

(10 citation statements)

References 21 publications

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“…Predictiondiscrepancy based [18] • Reduces the effects of GPS spoofing errors and estimates the true position of the UAV in the presence of GPS spoofing attacks.…”

Section: Artificial Intelligence Methodsmentioning

confidence: 99%

“…The real-time detection scheme derives the current UAV location from IMU and compares it to the location information received by the GPS receiver to determine if the UAV system was experiencing a GPS spoofing attack. In [18], the authors proposed a new algorithm to handle GPS spoofing attacks that caused unknown sudden system state variable changes. The compensation of the GPS spoofing effect was manipulated using a prediction discrepancy based on a particle filter algorithm.…”

Section: Related Workmentioning

confidence: 99%

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Dynamic Selection Techniques for Detecting GPS Spoofing Attacks on UAVs

Khoei

Ismail

Kaabouch

2022

Sensors

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Unmanned aerial vehicles are prone to several cyber-attacks, including Global Positioning System spoofing. Several techniques have been proposed for detecting such attacks. However, the recurrence and frequent Global Positioning System spoofing incidents show a need for effective security solutions to protect unmanned aerial vehicles. In this paper, we propose two dynamic selection techniques, Metric Optimized Dynamic selector and Weighted Metric Optimized Dynamic selector, which identify the most effective classifier for the detection of such attacks. We develop a one-stage ensemble feature selection method to identify and discard the correlated and low importance features from the dataset. We implement the proposed techniques using ten machine-learning models and compare their performance in terms of four evaluation metrics: accuracy, probability of detection, probability of false alarm, probability of misdetection, and processing time. The proposed techniques dynamically choose the classifier with the best results for detecting attacks. The results indicate that the proposed dynamic techniques outperform the existing ensemble models with an accuracy of 99.6%, a probability of detection of 98.9%, a probability of false alarm of 1.56%, a probability of misdetection of 1.09%, and a processing time of 1.24 s.

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“…Predictiondiscrepancy based [18] • Reduces the effects of GPS spoofing errors and estimates the true position of the UAV in the presence of GPS spoofing attacks.…”

Section: Artificial Intelligence Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Dynamic Selection Techniques for Detecting GPS Spoofing Attacks on UAVs

Khoei

Ismail

Kaabouch

2022

Sensors

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“…The ISAC protocol, system architecture design [8], signal-sharing [8][9][10][11][12][13], time-sharing [14], array-sharing [15], spectrum-sharing [16] and power-sharing [17] have been studied to realize the ISAC system. In signal-sharing, due to no significant difference between radar and communication in the time and spatial domains, both the radar detection and the wireless communication use the same waveform [8][9][10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

“…Time-sharing means that different tasks are allocated at different time slots, and either the communication task or the detection task is performed in one time slot [14]. Array-sharing is used in array system, where the different functions are realized in different sub-arrays [15]. Recently, a distributed dual-function radar-communication (DFRC), multiple input and multiple output (MIMO) system was proposed using both signal and power sharing [17].…”

Section: Introductionmentioning

confidence: 99%

Reinforcement Learning-Based UAVs Resource Allocation for Integrated Sensing and Communication (ISAC) System

et al. 2022

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Due to the limited ability of a single unmanned aerial vehicle (UAV), group unmanned aerial vehicles (UAVs) have attracted more attention in communication and radar fields. The use of an integrated sensing and communication (ISAC) system can make communication and radar modules share a radar module’s resources, coupled with efficient resource allocation methods. It can effectively solve the problem of inadequate UAV resources and the low utilization rate of resources. In this paper, the resource allocation problem is addressed for group UAVs to achieve a trade-off between the detection and communication performance, where the ISAC system is equipped in group UAVs. The resource allocation problem is described by an optimization problem, but with group UAVs, the problem is complex and cannot be solved efficiently. Compared with the traditional resource allocation scheme, which needs a lot of calculation or sample set problems, a novel reinforcement-learning-based method is proposed. We formulate a new reward function by combining mutual information (MI) and the communication rate (CR). The MI describes the radar detection performance, and the CR is for wireless communication. Simulation results show that compared with the traditional Kuhn Munkres (KM) or the deep neural network (DNN) methods, this method has better performance with the increase in problem complexity. Additionally, the execution time of this scheme is close to that of the DNN scheme, and it is better than the KM algorithm.

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“…The other major concern about the security of UAVs is an attacker interfering with the UAVs in various ways to either take control of the UAVs or disabling the communication between the UAVs and the ground control station (GCS). Different attacks, which can be launched against UAVs to either take control or disable the communication, include jamming [8], spoofing [9], and false data injection [10] attacks.…”

Section: Introductionmentioning

confidence: 99%

Survey of Security Protocols and Vulnerabilities in Unmanned Aerial Vehicles

2021

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With the rapid growth in technology, the use of Unmanned Aerial Vehicles (UAVs) have increased in civil and military applications including rescue operations, disaster recovery, and military operations. Despite the utility and advantages of UAVs, they may lead to major security breaches in the context of hardware, software, and communication channel, due their ease of use and availability. UAVs are vulnerable to various types of attacks such as spoofing, false data injection, jamming, fuzzing, availability, confidentiality, and integrity attacks. To overcome these security threats, researchers have been investigating strong security protocols to keep UAVs safe from the attackers. Nevertheless, there are many flaws in the developed protocols which can be exploited by hackers. Therefore, it is becomes crucial to study and analyze the existing security protocols used in UAVs to discover and address their vulnerabilities and weaknesses. The purpose of this study is to explore the vulnerabilities in the security protocols and propose guidelines to improve the security and provide future research directions.

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Prediction‐discrepancy based on innovative particle filter for estimating UAV true position in the presence of the GPS spoofing attacks

Cited by 22 publications

References 21 publications

Dynamic Selection Techniques for Detecting GPS Spoofing Attacks on UAVs

Dynamic Selection Techniques for Detecting GPS Spoofing Attacks on UAVs

Reinforcement Learning-Based UAVs Resource Allocation for Integrated Sensing and Communication (ISAC) System

Survey of Security Protocols and Vulnerabilities in Unmanned Aerial Vehicles

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