More Like this

1. Detecting GNSS spoofing using deep learning

   https://doi.org/10.1186/s13634-023-01103-1  

   Borhani-Darian, Parisa; Li, Haoqing; Wu, Peng; Closas, Pau (January 2024, EURASIP Journal on Advances in Signal Processing)

   Abstract Global Navigation Satellite System (GNSS) is pervasively used in position, navigation, and timing (PNT) applications. As a consequence, important assets have become vulnerable to intentional attacks on GNSS, where of particular relevance is spoofing transmissions that aim at superseding legitimate signals with forged ones in order to control a receiver’s PNT computations. Detecting such attacks is therefore crucial, and this article proposes to employ an algorithm based on deep learning to achieve the task. A data-driven classifier is considered that has two components: a deep learning model that leverages parallelization to reduce its computational complexity and a clustering algorithm that estimates the number and parameters of the spoofing signals. Based on the experimental results, it can be concluded that the proposed scheme exhibits superior performance compared to the existing solutions, especially under moderate-to-high signal-to-noise ratios. 

   more » « less
2. Detection of Induced GNSS Spoofing Using S-Curve-Bias

   https://doi.org/10.3390/s19040922  

   Wang, Wenyi; Li, Na; Wu, Renbiao; Closas, Pau (February 2019, Sensors)

   In Global Navigation Satellite System (GNSS), a spoofing attack consists of forged signals which possibly cause the attacked receivers to deduce a false position, a false clock, or both. In contrast to simplistic spoofing, the induced spoofing captures the victim tracking loops by gradually adjusting it’s parameters, e.g., code phase and power. Then the victims smoothly deviates from the correct position or timing. Therefore, it is more difficult to detect the induced spoofing than the simplistic one. In this paper, by utilizing the dynamic nature of such gradual adjustment process, an induced spoofing detection method is proposed based on the S-curve-bias (SCB). Firstly, SCB in the inducing process is theoretically derived. Then, in order to detect the induced spoofing, a detection metric is defined. After that, a series of experiments using the Texas spoofing test battery (TEXBAT) are performed to demonstrate the effectiveness of the proposed algorithm. 

   more » « less
3. Instance-based Supervised Machine Learning Models for Detecting GPS Spoofing Attacks on UAS

   https://doi.org/10.1109/CCWC54503.2022.9720888  

   Aissou, Ghilas; Benouadah, Selma; El Alami, Hassan; Kaabouch, Naima (January 2022, IEEE Annual Computing and Communication Workshop and Conference (CCWC))

   Unmanned Aerial Systems (UAS) heavily depend on the Global Positioning System (GPS) for navigation. However, the unencrypted civilian GPS signals are subject to different types of threats, including GPS spoofing attacks. In this paper, we evaluate five instance-based learning models for GPS spoofing detection in UAS, namely K Nearest Neighbor, Radius Neighbor, Linear Support Vector Machine (SVM), C-SVM, and Nu-SVM. We used software-defined radio units to collect and extract features from satellite signals. Then, we simulated three types of GPS spoofing attacks specifically the simplistic, intermediate, and sophisticated attacks. The evaluation results show that Nu-SVM outperforms the other instance learning classifiers in terms of accuracy, probability of detection, probability of false alarm, and probability of misdetection. In addition, the model shows good computational performance regarding memory usage and processing time in the detection phase. 

   more » « less
4. Security of GPS/INS Based On-road Location Tracking Systems

   https://doi.org/10.1109/SP.2019.00068  

   Narain, Sashank; Ranganathan, Aanjhan; Noubir, Guevara (May 2019, 2019 IEEE Symposium on Security and Privacy (SP))

   Location information is critical to a wide variety of navigation and tracking applications. GPS, today's de-facto outdoor localization system has been shown to be vulnerable to signal spoofing attacks. Inertial Navigation Systems (INS) are emerging as a popular complementary system, especially in road transportation systems as they enable improved navigation and tracking as well as offer resilience to wireless signals spoofing and jamming attacks. In this paper, we evaluate the security guarantees of INS-aided GPS tracking and navigation for road transportation systems. We consider an adversary required to travel from a source location to a destination and monitored by an INS-aided GPS system. The goal of the adversary is to travel to alternate locations without being detected. We develop and evaluate algorithms that achieve this goal, providing the adversary significant latitude. Our algorithms build a graph model for a given road network and enable us to derive potential destinations an attacker can reach without raising alarms even with the INS-aided GPS tracking and navigation system. The algorithms render the gyroscope and accelerometer sensors useless as they generate road trajectories indistinguishable from plausible paths (both in terms of turn angles and roads curvature). We also design, build and demonstrate that the magnetometer can be actively spoofed using a combination of carefully controlled coils. To experimentally demonstrate and evaluate the feasibility of the attack in real-world, we implement a first real-time integrated GPS/INS spoofer that accounts for traffic fluidity, congestion, lights, and dynamically generates corresponding spoofing signals. Furthermore, we evaluate our attack on ten different cities using driving traces and publicly available city plans. Our evaluations show that it is possible for an attacker to reach destinations that are as far as 30 km away from the actual destination without being detected. We also show that it is possible for the adversary to reach almost 60--80% of possible points within the target region in some cities. Such results are only a lower-bound, as an adversary can adjust our parameters to spend more resources (e.g., time) on the target source/destination than we did for our performance evaluations of thousands of paths. We propose countermeasures that limit an attacker's ability, without the need for any hardware modifications. Our system can be used as the foundation for countering such attacks, both detecting and recommending paths that are difficult to spoof. 

   more » « less
5. Direct Aerial Visual Geolocalization Using Deep Neural Networks

   https://doi.org/10.3390/rs13194017  

   Harvey, Winthrop; Rainwater, Chase; Cothren, Jackson (October 2021, Remote Sensing)

   Unmanned aerial vehicles (UAVs) must keep track of their location in order to maintain flight plans. Currently, this task is almost entirely performed by a combination of Inertial Measurement Units (IMUs) and reference to GNSS (Global Navigation Satellite System). Navigation by GNSS, however, is not always reliable, due to various causes both natural (reflection and blockage from objects, technical fault, inclement weather) and artificial (GPS spoofing and denial). In such GPS-denied situations, it is desirable to have additional methods for aerial geolocalization. One such method is visual geolocalization, where aircraft use their ground facing cameras to localize and navigate. The state of the art in many ground-level image processing tasks involve the use of Convolutional Neural Networks (CNNs). We present here a study of how effectively a modern CNN designed for visual classification can be applied to the problem of Absolute Visual Geolocalization (AVL, localization without a prior location estimate). An Xception based architecture is trained from scratch over a >1000 km2 section of Washington County, Arkansas to directly regress latitude and longitude from images from different orthorectified high-altitude survey flights. It achieves average localization accuracy on unseen image sets over the same region from different years and seasons with as low as 115 m average error, which localizes to 0.004% of the training area, or about 8% of the width of the 1.5 × 1.5 km input image. This demonstrates that CNNs are expressive enough to encode robust landscape information for geolocalization over large geographic areas. Furthermore, discussed are methods of providing uncertainty for CNN regression outputs, and future areas of potential improvement for use of deep neural networks in visual geolocalization. 

   more » « less