arXiv subjects

Mizanur Rahman

Publications and source records attributed to Mizanur Rahman.

At least 91 records · Page 5Linked to original sources

arXiv2020

Prediction-Based GNSS Spoofing Attack Detection for Autonomous Vehicles

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.

cs.RO↗

arXiv2020

Commercial Cloud Computing for Connected Vehicle Applications in Transportation Cyber-Physical Systems

This study focuses on the feasibility of commercial cloud services for connected vehicle (CV) applications in a Transportation Cyber-Physical Systems (TCPS) environment. TCPS implies that CVs, in addition to being connected with each other, communicates with the transportation and computing infrastructure to fulfill application requirements. The motivation of this study is to accelerate commercial cloud-based CV application development by presenting the lessons learned by implementing a CV mobility application using Amazon Web Services (AWS). The feasibility of the cloud-based CV application is assessed at three levels: (i) the development of a cloud-based TCPS architecture, (ii) the deployment of a cloud-based CV application using AWS, and (iii) the evaluation of the cloud-based CV application. We implemented this CV mobility application using a serverless cloud architecture and found that such a cloud-based TCPS environment could meet the permissible delay limits of CV mobility applications. Commercial cloud services, as an integral part of TCPS, could reduce costs associated with establishing and maintaining vast computing infrastructure for supporting CV applications. As the CV penetration levels on the surface transportation systems increase significantly over the next several years, scaling the backend infrastructure to support such applications is a critical issue. This study shows how commercial cloud services could automatically scale the backend infrastructure to meet the rapidly changing demands of real-world CV applications. Through real-world experiments, we demonstrate how commercial cloud services along with serverless cloud architecture could advance the transportation digital infrastructure for supporting connected mobility applications in a TCPS environment.

cs.DC↗

arXiv2020

An Evaluation Framework of End-to-End 5G Millimeter Wave Communication for Connected Vehicle Applications

The internet-of-things (IoT) environment connects different intelligent devices together and enables seamless data communication between the connected devices. Connected vehicles (CVs) are one of the primary example of the IoT, and the efficient, reliable, and safe operation of CVs demands a reliable wireless communication system, which can ensure high throughput and low communication latency. The 5G millimeter wave (5G mmWave) wireless communication network offers such benefits, which can be the enabler of CV applications, especially for dense urban areas with high number of CVs. In this study, we present a simulation-based evaluation framework of end-to-end 5G mmWave communication for CV applications. In addition, we compare the 5G mmWave with the Dedicated Short Range Communication (DSRC) technology for a CV application. The simulation framework is developed using two simulators, a network simulator and a traffic simulator. In order to develop the framework in this study, we have used Network Simulator 3 (ns-3) and SUMO, an open-source microscopic roadway traffic simulator. We have used end-to-end latency, packet loss and throughput as the performance evaluation metrics. We have found that for dense urban areas, 5G mmWave can achieve higher throughput, lower latency and lower data loss compared to DSRC. 5G mmWave can support CV applications with high throughput requirement on the downlink data flow. Through further investigation, we have found that the performance of 5G mmWave is significantly impacted by the penetration level of CVs, maximum CV speed, and CV application requirements.

cs.NI↗

arXiv2020

Change Point Models for Real-time Cyber Attack Detection in Connected Vehicle Environment

Connected vehicle (CV) systems are cognizant of potential cyber attacks because of increasing connectivity between its different components such as vehicles, roadside infrastructure, and traffic management centers. However, it is a challenge to detect security threats in real-time and develop appropriate or effective countermeasures for a CV system because of the dynamic behavior of such attacks, high computational power requirement, and a historical data requirement for training detection models. To address these challenges, statistical models, especially change point models, have potentials for real-time anomaly detections. Thus, the objective of this study is to investigate the efficacy of two change point models, Expectation Maximization (EM) and two forms of Cumulative Summation (CUSUM) algorithms (i.e., typical and adaptive), for real-time V2I cyber attack detection in a CV Environment. To prove the efficacy of these models, we evaluated these two models for three different type of cyber attack, denial of service (DOS), impersonation, and false information, using basic safety messages (BSMs) generated from CVs through simulation. Results from numerical analysis revealed that EM, CUSUM, and adaptive CUSUM could detect these cyber attacks, DOS, impersonation, and false information, with an accuracy of (99%, 100%, 100%), (98%, 10%, 100%), and (100%, 98%, 100%) respectively.

cs.CR↗

arXiv2020

Dynamic Error-bounded Lossy Compression (EBLC) to Reduce the Bandwidth Requirement for Real-time Vision-based Pedestrian Safety Applications

As camera quality improves and their deployment moves to areas with limited bandwidth, communication bottlenecks can impair real-time constraints of an ITS application, such as video-based real-time pedestrian detection. Video compression reduces the bandwidth requirement to transmit the video but degrades the video quality. As the quality level of the video decreases, it results in the corresponding decreases in the accuracy of the vision-based pedestrian detection model. Furthermore, environmental conditions (e.g., rain and darkness) alter the compression ratio and can make maintaining a high pedestrian detection accuracy more difficult. The objective of this study is to develop a real-time error-bounded lossy compression (EBLC) strategy to dynamically change the video compression level depending on different environmental conditions in order to maintain a high pedestrian detection accuracy. We conduct a case study to show the efficacy of our dynamic EBLC strategy for real-time vision-based pedestrian detection under adverse environmental conditions. Our strategy selects the error tolerances dynamically for lossy compression that can maintain a high detection accuracy across a representative set of environmental conditions. Analyses reveal that our strategy increases pedestrian detection accuracy up to 14% and reduces the communication bandwidth up to 14x for adverse environmental conditions compared to the same conditions but without our dynamic EBLC strategy. Our dynamic EBLC strategy is independent of detection models and environmental conditions allowing other detection models and environmental conditions to be easily incorporated in our strategy.

cs.CV↗

arXiv2020

Development of a Connected and Automated Vehicle Longitudinal Control Model

It is envisioned that, in the future, most vehicles on our roadway will be controlled autonomously and will be connected via vehicle to everything (V2X) wireless communication networks. Developing a connected and automated vehicle (CAV) longitudinal controller, which will consider safety, comfort and operational efficiency simultaneously, is a challenge. A CAV longitudinal controller is a complex system where a vehicle senses immediate upstream vehicles using its sensors and receives information about its surroundings via wireless connectivity, and move forward accordingly. In this study, we develop an information-aware driver model (IADM) that utilizes information regarding an immediate upstream vehicle of a subject CAV through CAV sensors and V2X connectivity while considering passenger comfort and operational efficiency along with maintaining safety gap for longitudinal vehicle motion of the autonomous vehicle. Unlike existing driver models for longitudinal control, the IADM intelligently fuses data received from in vehicle sensors, and immediate upstream vehicles of the subject CAV through wireless connectivity, and IADM parameters do not need to be calibrated for different traffic states, such as congested and non congested traffic conditions. It only requires defining the subject CAVs maximum acceleration and deceleration limit, and computation time that is needed to update the subject CAVs trajectory from its previous state. Our analyses suggest that the IADM (i) is able to maintain safety using a newly defined safe gap function depending on the speed and reaction time of a CAV; (ii) shows local stability and string stability and (iii) provides riding comfort for a range of autonomous driving aggressiveness depending on the passenger preferences.

eess.SY↗

arXiv2019

Grey Models for Short-Term Queue Length Predictions for Adaptive Traffic Signal Control

Traffic congestion at a signalized intersection greatly reduces the travel time reliability in urban areas. Adaptive signal control system (ASCS) is the most advanced traffic signal technology that regulates the signal phasing and timings considering the patterns in real-time in order to reduce congestion. Real-time prediction of queue lengths can be used to adjust the phasing and timings for different movements at an intersection with ASCS. The accuracy of the prediction varies based on the factors, such as the stochastic nature of the vehicle arrival rates, time of the day, weather and driver characteristics. In addition, accurate prediction for multilane, undersaturated and saturated traffic scenarios is challenging. Thus, the objective of this study is to develop queue length prediction models for signalized intersections that can be leveraged by ASCS using four variations of Grey systems: (i) the first order single variable Grey model (GM(1,1)); (ii) GM(1,1) with Fourier error corrections; (iii) the Grey Verhulst model (GVM), and (iv) GVM with Fourier error corrections. The efficacy of the GM is that they facilitate fast processing; as these models do not require a large amount of data; as would be needed in artificial intelligence models; and they are able to adapt to stochastic changes, unlike statistical models. We have conducted a case study using queue length data from five intersections with ASCS on a calibrated roadway network in Lexington, South Carolina. GM were compared with linear, nonlinear time series models, and long short-term memory (LSTM) neural network. Based on our analyses, we found that EGVM reduces the prediction error over closest competing models (i.e., LSTM and time series models) in predicting average and maximum queue lengths by 40% and 42%, respectively, in terms of Root Mean Squared Error, and 51% and 50%, respectively, in terms of Mean Absolute Error.

eess.SY↗

arXiv2019

Simultaneous measurement of anisotropic thermal conductivity and thermal boundary conductance of 2-dimensional materials

The rapidly increasing number of 2-dimensional (2D) materials that have been isolated or synthesized provides an enormous opportunity to realize new device functionalities. Whereas their optical and electrical characterization have been more readily reported, quantitative thermal characterization is more challenging due to the difficulties with localizing heat flow. Optical pump-probe techniques that are well-established for the study of bulk materials or thin-films have limited sensitivity to in-plane heat transport, and the characterization of the thermal anisotropy that is common in 2D materials is therefore challenging. Here we present a new approach to quantify the thermal properties based on the magneto-optical Kerr effect that yields quantitative insight into cross-plane and in-plane heat transport. The use of a very thin magnetic material as heater/thermometer increases in-plane thermal gradients without complicating the data analysis in spite of the layer being optically semi-transparent. The approach has the added benefit that it does not require the sample to be suspended, providing insight of thermal transport in supported, device-like environments. We apply this approach to measure the thermal properties of a range of 2D materials which are of interest for device applications, including single layer graphene, few-layer h-BN, single and few layer MoS2, and bulk MoSe2 crystal. The measured thermal properties will have important implications for thermal management in device applications.

physics.app-ph↗

arXiv2019

Long Short-Term Memory Neural Networks for False Information Attack Detection in Software-Defined In-Vehicle Network

A modern vehicle contains many electronic control units (ECUs), which communicate with each other through the in-vehicle network to ensure vehicle safety and performance. Emerging Connected and Automated Vehicles (CAVs) will have more ECUs and coupling between them due to the vast array of additional sensors, advanced driving features and Vehicle-to-Everything (V2X) connectivity. Due to the connectivity, CAVs will be more vulnerable to remote attackers. In this study, we developed a software-defined in-vehicle Ethernet networking system that provides security against false information attacks. We then created an attack model and attack datasets for false information attacks on brake-related ECUs. After analyzing the attack dataset, we found that the features of the dataset are time-series that have sequential variation patterns. Therefore, we subsequently developed a long short term memory (LSTM) neural network based false information attack/anomaly detection model for the real-time detection of anomalies within the in-vehicle network. This attack detection model can detect false information with an accuracy, precision and recall of 95%, 95% and 87%, respectively, while satisfying the real-time communication and computational requirements.

cs.CY↗

arXiv2019

Vision-based Pedestrian Alert Safety System (PASS) for Signalized Intersections

Although Vehicle-to-Pedestrian (V2P) communication can significantly improve pedestrian safety at a signalized intersection, this safety is hindered as pedestrians often do not carry hand-held devices (e.g., Dedicated short-range communication (DSRC) and 5G enabled cell phone) to communicate with connected vehicles nearby. To overcome this limitation, in this study, traffic cameras at a signalized intersection were used to accurately detect and locate pedestrians via a vision-based deep learning technique to generate safety alerts in real-time about possible conflicts between vehicles and pedestrians. The contribution of this paper lies in the development of a system using a vision-based deep learning model that is able to generate personal safety messages (PSMs) in real-time (every 100 milliseconds). We develop a pedestrian alert safety system (PASS) to generate a safety alert of an imminent pedestrian-vehicle crash using generated PSMs to improve pedestrian safety at a signalized intersection. Our approach estimates the location and velocity of a pedestrian more accurately than existing DSRC-enabled pedestrian hand-held devices. A connected vehicle application, the Pedestrian in Signalized Crosswalk Warning (PSCW), was developed to evaluate the vision-based PASS. Numerical analyses show that our vision-based PASS is able to satisfy the accuracy and latency requirements of pedestrian safety applications in a connected vehicle environment.

cs.CV↗

arXiv2019

Real time Traffic Flow Parameters Prediction with Basic Safety Messages at Low Penetration of Connected Vehicles

The expected low market penetration of connected vehicles (CVs) in the near future could be a constraint in estimating traffic flow parameters, such as average travel speed of a roadway segment and average space headway between vehicles from the CV broadcasted data. This estimated traffic flow parameters from low penetration of connected vehicles become noisy compared to 100 percent penetration of CVs, and such noise reduces the real time prediction accuracy of a machine learning model, such as the accuracy of long short term memory (LSTM) model in terms of predicting traffic flow parameters. The accurate prediction of the parameters is important for future traffic condition assessment. To improve the prediction accuracy using noisy traffic flow parameters, which is constrained by limited CV market penetration and limited CV data, we developed a real time traffic data prediction model that combines LSTM with Kalman filter based Rauch Tung Striebel (RTS) noise reduction model. We conducted a case study using the Enhanced Next Generation Simulation (NGSIM) dataset, which contains vehicle trajectory data for every one tenth of a second, to evaluate the performance of this prediction model. Compared to a baseline LSTM model performance, for only 5 percent penetration of CVs, the analyses revealed that combined LSTM and RTS model reduced the mean absolute percentage error (MAPE) from 19 percent to 5 percent for speed prediction and from 27 percent to 9 percent for space-headway prediction. The statistical significance test with a 95 percent confidence interval confirmed no significant difference in predicted average speed and average space headway using this LSTM and RTS combination with only 5 percent CV penetration rate.

cs.LG↗

arXiv2019

A Review of Sensing and Communication, Human Factors, and Controller Aspects for Information-Aware Connected and Automated Vehicles

Information-aware connected and automated vehicles (CAVs) have drawn great attention in recent years due to its potentially significant positive impacts on roadway safety and operational efficiency. In this paper, we conduct an in-depth review of three basic and key interrelated aspects of a CAV: sensing and communication technologies, human factors, and information-aware controller design. First, different vehicular sensing and communication technologies and their protocol stacks, to provide reliable information to the information-aware CAV controller, are thoroughly discussed. Diverse human factor issues, such as user comfort, preferences, and reliability, to design the CAV systems for mass adaptation are also discussed. Then, different layers of a CAV controller (route planning, driving mode execution, and driving model selection) considering human factors and information through connectivity are reviewed. In addition, critical challenges for the sensing and communication technologies, human factors, and information-aware controller are identified to support the design of a safe and efficient CAV system while considering user acceptance and comfort. Finally, promising future research directions of these three aspects are discussed to overcome existing challenges to realize a safe and operationally efficient CAV.

cs.NI↗

arXiv2019

Real-time Pedestrian Detection Approach with an Efficient Data Communication Bandwidth Strategy

Vehicle-to-Pedestrian (V2P) communication can significantly improve pedestrian safety at a signalized intersection. It is unlikely that pedestrians will carry a low latency communication enabled device and activate a pedestrian safety application in their hand-held device all the time. Because of this limitation, multiple traffic cameras at the signalized intersection can be used to accurately detect and locate pedestrians using deep learning and broadcast safety alerts related to pedestrians to warn connected and automated vehicles around a signalized intersection. However, unavailability of high-performance computing infrastructure at the roadside and limited network bandwidth between traffic cameras and the computing infrastructure limits the ability of real-time data streaming and processing for pedestrian detection. In this paper, we develop an edge computing based real-time pedestrian detection strategy combining pedestrian detection algorithm using deep learning and an efficient data communication approach to reduce bandwidth requirements while maintaining a high object detection accuracy. We utilize a lossy compression technique on traffic camera data to determine the tradeoff between the reduction of the communication bandwidth requirements and a defined object detection accuracy. The performance of the pedestrian-detection strategy is measured in terms of pedestrian classification accuracy with varying peak signal-to-noise ratios. The analyses reveal that we detect pedestrians by maintaining a defined detection accuracy with a peak signal-to-noise ratio (PSNR) 43 dB while reducing the communication bandwidth from 9.82 Mbits/sec to 0.31 Mbits/sec, a 31x reduction.

cs.CV↗

arXiv2018

Connected Vehicle Application Development Platform (CVDeP) for Edge-centric Cyber-Physical Systems

Connected vehicle (CV) application developers need a development platform to build, test and debug CV applications, such as safety, mobility, and environmental applications, in an edge-centric Cyber-Physical Systems. Our study objective is to develop and evaluate a scalable and secure CV application development platform (CVDeP) that enables the CV application developers to build, test and debug CV applications in real-time. CVDeP ensures that the functional requirements of the CV applications meet the latency requirements imposed by corresponding CV applications. We conducted a case study to evaluate the efficacy of CVDeP using two CV applications (one safety and one mobility application) and validated them through a field evaluation at the Clemson University Connected and Autonomous Vehicle Testbed (CU-CAVT). The analysis outcome proves the efficacy of CVDeP, which satisfies the functional requirements (e.g., latency, throughput) of a CV application while maintaining scalability, and security of the platform and applications.

cs.NI↗

arXiv2018

Change Point Models for Real-time V2I Cyber Attack Detection in a Connected Vehicle Environment

Connected vehicle (CV) systems are cognizant of potential cyber attacks because of increasing connectivity between its different components such as vehicles, roadside infrastructure and traffic management centers. However, it is a challenge to detect security threats in real-time and develop appropriate/effective countermeasures for a CV system because of the dynamic behavior of such attacks, high computational power requirement and a historical data requirement for training detection models. To address these challenges, statistical models, especially change point models, have potentials for real-time anomaly detections. Thus, the objective of this study is to investigate the efficacy of two change point models, Expectation Maximization (EM) and Cumulative Sum (CUSUM), for real-time V2I cyber attack detection in a CV Environment. To prove the efficacy of these models, we evaluated these two models for three different type of cyber attack, denial of service (DOS), impersonation, and false information, using basic safety messages (BSMs) generated from CVs through simulation. Results from numerical analysis revealed that EM and CUSUM could detect these cyber attacks, DOS, impersonation, and false information, with an accuracy of 99\%, 100\%, and 98\%, and 100\%, 100\% and 98\%, respectively.

cs.CR↗

arXiv2018

Towards Secure Infrastructure-based Cooperative Adaptive Cruise Control

Cooperative Adaptive Cruise Control (CACC) is a pivotal vehicular application that would allow transportation field to achieve its goals of increased traffic throughput and roadway capacity. This application is of paramount interest to the vehicular technology community with a large body of literature dedicated to research within different aspects of CACC, including but not limited to security with CACC. Of all available literature, the overwhelming focus in on CACC utilizing vehicle-to-vehicle (V2V) communication. In this work, we assert that a qualitative increase in vehicle-to-infrastructure (V2I) and infrastructure-to-vehicle (I2V) involvement has the potential to add greater value to CACC. In this study, we developed a strategy for detection of a denial-of-service (DoS) attack on a CACC platoon where the system edge in the vehicular network plays a central role in attack detection. The proposed security strategy is substantiated with a simulation-based evaluation using the ns-3 discrete event network simulator. Empirical evidence obtained through simulation-based results illustrate successful detection of the DoS attack at four different levels of attack severity using this security strategy.

cs.CR↗

arXiv2018

Feasibility of 5G mm-wave communication for connected autonomous vehicles

The internet-of-things (IoT) environment holds different intelligent components networked together and will enable seamless data communication between the connected components. Connected autonomous vehicles or CAVs are major components of the IoT, and the smooth, reliable, and safe operation of CAVs demands a reliable wireless communication system, which can ensure high connectivity, high throughput and low communication latency. The 5G millimeter-wave or mm-wave communication network offers such benefits, which can be the enabler of CAVs, especially for dense congested areas. In this research, we have evaluated the 5G mm-wave and Dedicated Short Range Communication (DSRC) for different CAV applications in a Network Simulator-3 (ns-3). For the CAV applications, we have evaluated the end-to-end latency, packet loss, and data rate (for both the CAV receiver and transmitter) of the 5G mm-wave. We have found that 5G mm-wave can support CAV safety applications by ensuring lower latency compared to the required minimum latency of 200 milliseconds for the forward collision warning application. For mobility applications, we have found that 5G mm-wave can support multiple CAVs with a high data receive rate, which is enough for real-time high definition video streaming for in-vehicle infotainment, with mean packet delay of 13 milliseconds. The findings from this study establish that 5G mm-wave can be the enabler of future CAVs in congested areas. Using the evaluation framework developed in this paper, public agencies can evaluate 5G mm-wave to support CAVs in congested areas, such as heavy pedestrian areas like downtown, commercial zones, under their jurisdiction.

cs.NI↗

arXiv2018

Search Rank Fraud De-Anonymization in Online Systems

We introduce the fraud de-anonymization problem, that goes beyond fraud detection, to unmask the human masterminds responsible for posting search rank fraud in online systems. We collect and study search rank fraud data from Upwork, and survey the capabilities and behaviors of 58 search rank fraudsters recruited from 6 crowdsourcing sites. We propose Dolos, a fraud de-anonymization system that leverages traits and behaviors extracted from these studies, to attribute detected fraud to crowdsourcing site fraudsters, thus to real identities and bank accounts. We introduce MCDense, a min-cut dense component detection algorithm to uncover groups of user accounts controlled by different fraudsters, and leverage stylometry and deep learning to attribute them to crowdsourcing site profiles. Dolos correctly identified the owners of 95% of fraudster-controlled communities, and uncovered fraudsters who promoted as many as 97.5% of fraud apps we collected from Google Play. When evaluated on 13,087 apps (820,760 reviews), which we monitored over more than 6 months, Dolos identified 1,056 apps with suspicious reviewer groups. We report orthogonal evidence of their fraud, including fraud duplicates and fraud re-posts.

cs.SI↗