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Sensor Fusion-based GNSS Spoofing Attack Detection Framework for Autonomous Vehicles
TL;DR: In this article, a sensor fusion based GNSS spoofing attack detection framework is presented that consists of three concurrent strategies for an autonomous vehicle (AV): (i) prediction of location shift, (ii) detection of turns (left or right), and (iii) recognition of motion state (including standstill state).
Abstract: In this study, a sensor fusion based GNSS spoofing attack detection framework is presented that consists of three concurrent strategies for an autonomous vehicle (AV): (i) prediction of location shift, (ii) detection of turns (left or right), and (iii) recognition of motion state (including standstill state). Data from multiple low-cost in-vehicle sensors (i.e., accelerometer, steering angle sensor, speed sensor, and GNSS) are fused and fed into a recurrent neural network model, which is a long short-term memory (LSTM) network for predicting the location shift, i.e., the distance that an AV travels between two consecutive timestamps. We have then combined k-Nearest Neighbors (k-NN) and Dynamic Time Warping (DTW) algorithms to detect turns using data from the steering angle sensor. In addition, data from an AV's speed sensor is used to recognize the AV's motion state including the standstill state. To prove the efficacy of the sensor fusion-based attack detection framework, attack datasets are created for three unique and sophisticated spoofing attacks turn by turn, overshoot, and stop using the publicly available real-world Honda Research Institute Driving Dataset (HDD). Our analysis reveals that the sensor fusion-based detection framework successfully detects all three types of spoofing attacks within the required computational latency threshold.
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TL;DR: This paper briefly analyzes the common interference types of satellite navigation and then focuses on spoofing, and proposes a new classification standard and analyzes and compares the implementation difficulty, effect and adaptability of the current main spoofing detection technologies.
Abstract: With the development of satellite navigation technology, the research focus of GNSS has shifted from improving positioning accuracy to expanding system application and improving system performance. At the same time, improving the survivability of satellite navigation systems has become a research hotspot in the field of navigation, especially with regard to anti-spoofing. This paper first briefly analyzes the common interference types of satellite navigation and then focuses on spoofing. We analyze the characteristics and technical mechanism of satellite navigation and the positioning signal. Spoofing modes are classified and introduced separately according to signal generation, implementation stage and deployment strategy. After an introduction of GNSS spoofing technology, we summarize the research progress of GNSS anti-spoofing technology over the last decade. For anti-spoofing technology, we propose a new classification standard and analyze and compare the implementation difficulty, effect and adaptability of the current main spoofing detection technologies. Finally, we summarize with considerations, prospective challenges and development trends of GNSS spoofing and anti-spoofing technology in order to provide a reference for future research.
9 citations
TL;DR: In this paper , a low-cost framework for GPS spoofing detection is proposed, which combines several software-based methods to monitor NMEA-0183 data and evaluate its effectiveness using simulations supported by real-world experiments.
Abstract: Today’s maritime transportation relies on global navigation satellite systems (GNSSs) for accurate navigation. The high-precision GNSS receivers on board modern vessels are often considered trustworthy. However, due to technological advances and malicious activities, this assumption is no longer always true. Numerous incidents of tampered GNSS signals have been reported. Furthermore, researchers have demonstrated that manipulations can be carried out even with inexpensive hardware and little expert knowledge, lowering the barrier for malicious attacks with far-reaching consequences. Hence, exclusive trust in GNSS is misplaced, and methods for reliable detection are urgently needed. However, many of the proposed solutions require expensive replacement of existing hardware. In this paper, therefore, we present MAritime Nmea-based Anomaly detection (MANA), a novel low-cost framework for GPS spoofing detection. MANA monitors NMEA-0183 data and advantageously combines several software-based methods. Using simulations supported by real-world experiments that generate an extensive dataset, we investigate our approach and finally evaluate its effectiveness.
2 citations
TL;DR: In this paper , the authors investigate the resilience of mobile robot networks in time-varying formation tracking under deception attacks on global positioning and propose a localization based on extended information filters.
Abstract: This paper investigates the resilient control, analysis, recovery, and operation of mobile robot networks in time-varying formation tracking under deception attacks on global positioning. Local and global tracking control algorithms are presented to ensure redundancy of the mobile robot network and to retain the desired functionality for better resilience. Lyapunov stability analysis is utilized to show the boundedness of the formation tracking error and the stability of the network under various attack modes. A performance index is designed to compare the efficiency of the proposed formation tracking algorithms in situations with or without positioning attacks. Subsequently, a communication-free decentralized cooperative localization approach based on extended information filters is presented for positioning estimate recovery where the identification of positioning attacks is based on Kullback–Leibler divergence. A gain-tuning resilient operation is proposed to strategically synthesize formation control and cooperative localization for accurate and rapid system recovery from positioning attacks. The proposed methods are tested using both numerical simulation and experimental validation with a team of quadrotors.
TL;DR: In this article , the influence of mandatory factors like data pre-processing and data fusion along with situation awareness toward effective decision-making in the AVs are analyzed from various perceptive, to pick the major hiccups.
Abstract: Autonomous driving of higher automation levels asks for optimal execution of critical maneuvers in all environments. A crucial prerequisite for such optimal decision-making instances is accurate situation awareness of automated and connected vehicles. For this, vehicles rely on the sensory data captured from onboard sensors and information collected through V2X communication. The classical onboard sensors exhibit different capabilities and hence a heterogeneous set of sensors is required to create better situation awareness. Fusion of the sensory data from such a set of heterogeneous sensors poses critical challenges when it comes to creating an accurate environment context for effective decision-making in AVs. Hence this exclusive survey analyses the influence of mandatory factors like data pre-processing preferably data fusion along with situation awareness toward effective decision-making in the AVs. A wide range of recent and related articles are analyzed from various perceptive, to pick the major hiccups, which can be further addressed to focus on the goals of higher automation levels. A section of the solution sketch is provided that directs the readers to the potential research directions for achieving accurate contextual awareness. To the best of our knowledge, this survey is uniquely positioned for its scope, taxonomy, and future directions.
References
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TL;DR: Differential GPS and Integrity Monitoring Differential GPS Pseudolites Wide Area differential GPS Wide Area Augmentation System Receiver Autonomous Integrity Monitoring Integrated Navigation Systems Integration of GPS and Loran-C GPS and Inertial Integration Receiver Autonomic Integrity Monitoring Availability for GPS Augmented with Barometric Altimeter Aiding and Clock Coasting GPS and Global Navigation Satellite System (GLONASS) GPS Navigation Applications Land Vehicle Navigation and Tracking Marine Applications Applications of the GPS to Air Traffic Control GPS Applications in General Aviation Aircraft Automatic Approach and Landing of Aircraft Using Integrity Beacons Spacecraft Attitude
Abstract: Differential GPS and Integrity Monitoring Differential GPS Pseudolites Wide Area Differential GPS Wide Area Augmentation System Receiver Autonomous Integrity Monitoring Integrated Navigation Systems Integration of GPS and Loran-C GPS and Inertial Integration Receiver Autonomous Integrity Monitoring Availability for GPS Augmented with Barometric Altimeter Aiding and Clock Coasting GPS and Global Navigation Satellite System (GLONASS) GPS Navigation Applications Land Vehicle Navigation and Tracking Marine Applications Applications of the GPS to Air Traffic Control GPS Applications in General Aviation Aircraft Automatic Approach and Landing Using GPS Precision Landing of Aircraft Using Integrity Beacons Spacecraft Attitude Control Using GPS Carrier Phase Special Applications GPS for Precise Time and Time Interval Measurement Surveying with the Global Position System Attitude Determination Geodesy Orbit Determination Test Range Instrumentation.
2,275 citations
01 Jan 2004
TL;DR: This paper introduces FastDTW, an approximation of DTW that has a linear time and space complexity that uses a multilevel approach that recursively projects a solution from a coarse resolution and refines the projected solution.
Abstract: The dynamic time warping (DTW) algorithm is able to find the optimal alignment between two time series It is often used to determine time series similarity, classification, and to find corresponding regions between two time series DTW has a quadratic time and space complexity that limits its use to only small time series data sets In this paper we introduce FastDTW, an approximation of DTW that has a linear time and space complexity FastDTW uses a multilevel approach that recursively projects a solution from a coarse resolution and refines the projected solution We prove the linear time and space complexity of FastDTW both theoretically and empirically We also analyze the accuracy of FastDTW compared to two other existing approximate DTW algorithms: Sakoe-Chuba Bands and Data Abstraction Our results show a large improvement in accuracy over the existing methods
524 citations
457 citations
TL;DR: This paper presents a simple technique for time series classification that exploits DTW’s strength on this task but instead of directly using DTW as a distance measure to find nearest neighbors, the technique uses DTW to create new features which are then given to a standard machine learning method.
Abstract: Dynamic time warping (DTW) has proven itself to be an exceptionally strong distance measure for time series. DTW in combination with one-nearest neighbor, one of the simplest machine learning methods, has been difficult to convincingly outperform on the time series classification task. In this paper, we present a simple technique for time series classification that exploits DTW's strength on this task. But instead of directly using DTW as a distance measure to find nearest neighbors, the technique uses DTW to create new features which are then given to a standard machine learning method. We experimentally show that our technique improves over one-nearest neighbor DTW on 31 out of 47 UCR time series benchmark datasets. In addition, this method can be easily extended to be used in combination with other methods. In particular, we show that when combined with the symbolic aggregate approximation (SAX) method, it improves over it on 37 out of 47 UCR datasets. Thus the proposed method also provides a mechanism to combine distance-based methods like DTW with feature-based methods like SAX. We also show that combining the proposed classifiers through ensembles further improves the performance on time series classification.
268 citations
14 Dec 2018
TL;DR: The Honda Research Institute Driving Dataset (HDD) as discussed by the authors is a dataset of 104 hours of real human driving in the San Francisco Bay Area collected using an instrumented vehicle equipped with different sensors.
Abstract: Driving Scene understanding is a key ingredient for intelligent transportation systems. To achieve systems that can operate in a complex physical and social environment, they need to understand and learn how humans drive and interact with traffic scenes. We present the Honda Research Institute Driving Dataset (HDD), a challenging dataset to enable research on learning driver behavior in real-life environments. The dataset includes 104 hours of real human driving in the San Francisco Bay Area collected using an instrumented vehicle equipped with different sensors. We provide a detailed analysis of HDD with a comparison to other driving datasets. A novel annotation methodology is introduced to enable research on driver behavior understanding from untrimmed data sequences. As the first step, baseline algorithms for driver behavior detection are trained and tested to demonstrate the feasibility of the proposed task.
236 citations