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1. Performance analysis of capsule networks for detecting GPS spoofing attacks on unmanned aerial vehicles

   https://doi.org/10.1007/s10207-024-00978-x  

   Talaei Khoei, Tala; Al Shamaileh, Khair; Devabhaktuni, Vijaya Kumar; Kaabouch, Naima (February 2025, International Journal of Information Security)

   NA (Ed.)

   Unmanned aerial vehicles (UAVs) are prone to several cyber-attacks, including global positioning system (GPS) spoofing. The use of machine learning and deep learning are becoming increasingly common for UAV GPS spoofing attack detection; however, these approaches have some limitations, such as a high rate of false alarm and misdetection. We propose using capsule networks to detect and classify UAV-focused GPS spoofing attacks. This paper compares simple capsule networks, efficient capsule networks, dual attention capsule networks, and convolutional neural network in terms of accuracy, probability of detection, probability of misdetection, probability of false alarm, prediction time, training time per sample, and memory size. The results indicate that the Efficient-capsule network outperforms the other models, as demonstrated by an accuracy of 99.1%, a probability of detection of 99.9%, a probability of misdetection of 0.1%, a probability of false alarm of 0.37%, a prediction time of 0.5 seconds, a training time per sample of 0.2 seconds, and a memory size of 123 mebibytes for binary classification. 

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2. A Comparative Analysis of Supervised and Unsupervised Models for Detecting GPS Spoofing Attack on UAVs

   https://doi.org/10.1109/eIT53891.2022.9813826  

   Khoei, T. Talaei; Gasimova, A.; Ahajjam, M. A.; Shamaileh, K. Al; Devabhaktuni, V.; Kaabouch, N. (May 2022, A Comparative Analysis of Supervised and Unsupervised Models for Detecting GPS Spoofing Attack on UAVs)

   With the increasing use of Unmanned Aerial Vehicles in military and civilian applications, the security of this technology has become one of the critical concerns. UAVs’ positioning and navigation activities are highly dependent on Global Positioning Systems as they provide accurate locations for these vehicles. However, due to the civilian GPS signals being open and unencrypted, malicious users can target them in multiple ways, including by launching Global Positioning System spoofing attacks. To address this security issue, numerous techniques have been proposed to detect and classify these attacks, including supervised machine learning techniques. However, no studies have focused on unsupervised models to detect these attacks. In this paper, we compare the performance of several supervised models with that of unsupervised models in terms of accuracy, probability of detection, probability of misdetection, probability of false alarm, processing time, training time, prediction time, and memory size. The supervised models are Gaussian Naïve Bayes, Classification and Regression Decision Tree, Logistic Regression, Random Forest, Linear-Support Vector Machine, and Artificial Neural Network. The unsupervised models are Principal Component Analysis, K-means clustering, and Autoencoder. The results show that the Classification and Regression Decision Tree model outperforms the other supervised and unsupervised models in detecting and classifying GPS spoofing attacks. 

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

   https://doi.org/10.1109/CCWC54503.2022.9720738  

   Gasimova, Aydan; Khoei, Tala Talaei; Kaabouch, Naima (January 2022, IEEE Annual Computing and Communication Workshop and Conference)

   Unmanned Aerial Vehicles have been widely used in military and civilian areas. The positioning and return-to-home tasks of UAVs deliberately depend on Global Positioning Systems (GPS). However, the civilian GPS signals are not encrypted, which can motivate numerous cyber-attacks on UAVs, including Global Positioning System spoofing attacks. In these spoofing attacks, a malicious user transmits counterfeit GPS signals. Numerous studies have proposed techniques to detect these attacks. However, these techniques have some limitations, including low probability of detection, high probability of misdetection, and high probability of false alarm. In this paper, we investigate and compare the performances of three ensemble-based machine learning techniques, namely bagging, stacking, and boosting, in detecting GPS attacks. The evaluation metrics are the accuracy, probability of detection, probability of misdetection, probability of false alarm, memory size, processing time, and prediction time per sample. The results show that the stacking model has the best performance compared to the two other ensemble models in terms of all the considered evaluation metrics. 

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4. Supervised Deep Learning Models for Detecting GPS Spoofing Attacks on Unmanned Aerial Vehicles

   https://doi.org/10.1109/eIT57321.2023.10187274  

   Khoei, Tala Talaei; Aissou, Ghilas; Al Shamaileh, Khair; Devabhaktuni, Vijaya Kumar; Kaabouch, Naima (October 2023, IEEE International Conference on Electro Information Technology (eIT))

   Unmanned Aerial Networks (UAVs) are prone to several cyber-attacks, including Global Positioning Spoofing attacks. For this purpose, numerous studies have been conducted to detect, classify, and mitigate these attacks, using Artificial Intelligence techniques; however, most of these studies provided techniques with low detection, high misdetection, and high bias rates. To fill this gap, in this paper, we propose three supervised deep learning techniques, namely Deep Neural Network, U Neural Network, and Long Short Term Memory. These models are evaluated in terms of Accuracy, Detection Rate, Misdetection Rate, False Alarm Rate, Training Time per Sample, Prediction Time, and Memory Size. The simulation results indicated that the U Neural Network outperforms other models with an accuracy of 98.80%, a probability of detection of 98.85%, a misdetection of 1.15%, a false alarm of 1.8%, a training time per sample of 0.22 seconds, a prediction time of 0.2 seconds, and a memory size of 199.87 MiB. In addition, these results depicted that the Long Short-Term Memory model provides the lowest performance among other models for detecting these attacks on UAVs. 

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5. Adversarial Data-Augmented Resilient Intrusion Detection System for Unmanned Aerial Vehicles

   Asif, M; Rahman, M; Akkaya, K; Shahriar, H; Cuzzocrea, A (December 2023, IEEE Big Data 2023)

   With the growing adoption of unmanned aerial vehicles (UAVs) across various domains, the security of their operations is paramount. UAVs, heavily dependent on GPS navigation, are at risk of jamming and spoofing cyberattacks, which can severely jeopardize their performance, safety, and mission integrity. Intrusion detection systems (IDSs) are typically employed as defense mechanisms, often leveraging traditional machine learning techniques. However, these IDSs are susceptible to adversarial attacks that exploit machine learning models by introducing input perturbations. In this work, we propose a novel IDS for UAVs to enhance resilience against such attacks using generative adversarial networks (GAN). We also comprehensively study several evasion-based adversarial attacks and utilize them to compare the performance of the proposed IDS with existing ones. The resilience is achieved by generating synthetic data based on the identified weak points in the IDS and incorporating these adversarial samples in the training process to regularize the learning. The evaluation results demonstrate that the proposed IDS is significantly robust against adversarial machine learning based attacks compared to the state-of-the-art IDSs while maintaining a low false positive rate. 

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