Sagar Dasgupta

Bio: Sagar Dasgupta is an academic researcher from University of Alabama. The author has contributed to research in topics: Computer science & GNSS applications. The author has an hindex of 1, co-authored 5 publications receiving 5 citations.

Prediction-Based GNSS Spoofing Attack Detection for Autonomous Vehicles.

Abstract: Global Navigation Satellite System (GNSS) provides Positioning, Navigation, and Timing (PNT) services for autonomous vehicles (AVs) using satellites and radio communications. Due to the lack of encryption, open-access of the coarse acquisition (C/A) codes, and low strength of the signal, GNSS is vulnerable to spoofing attacks compromising the navigational capability of the AV. A spoofed attack is difficult to detect as a spoofer (attacker who performs spoofing attack) can mimic the GNSS signal and transmit inaccurate location coordinates to an AV. In this study, we have developed a prediction-based spoofing attack detection strategy using the long short-term memory (LSTM) model, a recurrent neural network model. The LSTM model is used to predict the distance traveled between two consecutive locations of an autonomous vehicle. In order to develop the LSTM prediction model, we have used a publicly available real-world comma2k19 driving dataset. The training dataset contains different features (i.e., acceleration, steering wheel angle, speed, and distance traveled between two consecutive locations) extracted from the controlled area network (CAN), GNSS, and inertial measurement unit (IMU) sensors of AVs. Based on the predicted distance traveled between the current location and the immediate future location of an autonomous vehicle, a threshold value is established using the positioning error of the GNSS device and prediction error (i.e., maximum absolute error) related to distance traveled between the current location and the immediate future location. Our analysis revealed that the prediction-based spoofed attack detection strategy can successfully detect the attack in real-time.

Sensor Fusion-based GNSS Spoofing Attack Detection Framework for Autonomous Vehicles

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.

Improving the Environmental Perception of Autonomous Vehicles using Deep Learning-based Audio Classification

Abstract: Sense of hearing is crucial for autonomous vehicles (AVs) to better perceive its surrounding environment. Although visual sensors of an AV, such as camera, lidar, and radar, help to see its surrounding environment, an AV cannot see beyond those sensors’ line-of-sight. On the other hand, an AV’s sense of hearing cannot be obstructed by line-of-sight. For example, an AV can identify an emergency vehicle’s siren through audio classification even though the emergency vehicle is not within the line-of-sight of the AV. Thus, auditory perception is complementary to the camera, lidar, and radar-based perception systems. This paper presents a deep learning based robust audio classification framework, aiming to achieve improved environmental perception for AVs. The presented framework leverages a deep Convolution Neural Network (CNN) to classify different audio classes. UrbanSound8k, an urban environment dataset, is used to train and test the developed framework. Seven audio classes—i.e., air conditioner, car horn, children playing, dog bark, engine idling, gunshot, and siren, are identified from the UrbanSound8k dataset because of their relevancy related to AVs. Our framework can classify different audio classes with 97.82% accuracy. Moreover, the audio classification accuracies with all ten classes are presented, which proves that our framework performed better in the case of AV-related sounds compared to the existing audio classification frameworks.

Audio Analytics-based Human Trafficking Detection Framework for Autonomous Vehicles

Abstract: Human trafficking is a universal problem, persistent despite numerous efforts to combat globally. Individuals of any age, race, ethnicity, sex, gender identity, sexual orientation, nationality, immi-gration status, cultural background, religion, socio-economic class, and education can be a victim of human trafficking. With the advancements in technology and the introduction of autonomous vehicles (AVs), human traffickers will adopt new ways to transport victims, which could acceler-ate the growth of organized human trafficking networks, whcih can make detection of trafficking in persons more challenging for law enforcement agencies. The objective of this study is to develop an innovative audio analytics-based human trafficking detection framework for autonomous vehicles. The primary contributions of this study are to: (i) define four non-trivial, feasible, and realistic human trafficking scenarios for AVs; (ii) create a new and comprehensive audio dataset related to human trafficking with five classes—i.e., crying, screaming, car door banging, car noise, and conversation; and (iii) develop a deep 1-D Convolution Neural Network (CNN) architecture for audio data classification related to human trafficking. We have also conducted a case study using the new audio dataset and evaluate the audio classification performance of the deep 1-D CNN. Our analyses reveal that the deep 1-D CNN can distinguish sound coming from a human trafficking victim from a non-human trafficking sound with an accuracy of 95%, which proves the efficacy of our framework.

A Reinforcement Learning Approach for Global Navigation Satellite System Spoofing Attack Detection in Autonomous Vehicles

Abstract: A resilient and robust positioning, navigation, and timing (PNT) system is a necessity for the navigation of autonomous vehicles (AVs). Global Navigation Satelite System (GNSS) provides satellite-based PNT services. However, a spoofer can temper an authentic GNSS signal and could transmit wrong position information to an AV. Therefore, a GNSS must have the capability of real-time detection and feedback-correction of spoofing attacks related to PNT receivers, whereby it will help the end-user (autonomous vehicle in this case) to navigate safely if it falls into any compromises. This paper aims to develop a deep reinforcement learning (RL)-based turn-by-turn spoofing attack detection using low-cost in-vehicle sensor data. We have utilized Honda Driving Dataset to create attack and non-attack datasets, develop a deep RL model, and evaluate the performance of the RL-based attack detection model. We find that the accuracy of the RL model ranges from 99.99% to 100%, and the recall value is 100%. However, the precision ranges from 93.44% to 100%, and the f1 score ranges from 96.61% to 100%. Overall, the analyses reveal that the RL model is effective in turn-by-turn spoofing attack detection.

A Landmark Based Shortest Path Detection by Using A* and Haversine Formula

Abstract: In 1900, less than 20 percent of the world populace lived in cities, in 2007, fair more than 50 percent of the world populace lived in cities. In 2050, it has been anticipated that more than 70 percent of the worldwide population (about 6.4 billion individuals) will be city tenants. There's more weight being set on cities through this increment in population [1]. With approach of keen cities, data and communication technology is progressively transforming the way city regions and city inhabitants organize and work in reaction to urban development. In this paper, we create a nonspecific plot for navigating a route throughout city A asked route is given by utilizing combination of A* Algorithm and Haversine equation. Haversine Equation gives least distance between any two focuses on spherical body by utilizing latitude and longitude. This least distance is at that point given to A* calculation to calculate minimum distance. The method for identifying the shortest path is specify in this paper.

A Survey of GNSS Spoofing and Anti-Spoofing Technology

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.

Anomaly Detection using IoT Sensor-Assisted ConvLSTM Models for Connected Vehicles

Abstract: The safety and security of connected autonomous vehicles’ (CAVs) passengers are crucial for the autonomous vehicle industry. A zero-loss and accident-free strategy is leading the way, not only considering a luxurious development and design of the automotive industry but also avoiding the cyber attacks against the vehicle intrusion detection system (IDS) that may be at its best performance keeping attackers from maliciously altering or corrupting the flow of data within the vehicle’s internal communication bus. In the Vehicle-To-Everything (V2X) age, there is no guarantee, , that faulty data streams out of critical Electronic Control Units (ECUs) can be kept from leading the autonomous vehicle astray without external help from different sets of IoT sensors in pedestrian-held devices, passenger-held devices, or side-road infrastructure. Therefore, in this research, ConvLSTM deep learning models are proposed to recognize corrupted data streams bundled together with the autonomous vehicle navigation and powertrain data. The efficiency and generalization of the supervised ConvLSTM deep learning models are tested on anomalies within a stack of different in-vehicle sensors assisted by IoT navigation sensor data (e.g. Global Navigation Satellite System (GNSS) location, motion, and orientation data). The proposed models are evaluated using a dataset that consists of many IoT sensors that, although, was not collected through an IoT network infrastructure, being collected through real-world vehicle ride can very well represent a benchmark for any V2X IoT streaming bundle that is used for autonomous vehicle positioning applications in the future. In this paper, we achieved anomaly detection with 0.98 F1-score using different ConvLSTM model designs which is much higher than most state-of-the-art approaches and on par with state-of-the-art deep learning LSTM models.

Sensor Fusion-based GNSS Spoofing Attack Detection Framework for Autonomous Vehicles

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.

Detecting Maritime GPS Spoofing Attacks Based on NMEA Sentence Integrity Monitoring

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.