Mhafuzul Islam

Bio: Mhafuzul Islam is an academic researcher from Clemson University. The author has contributed to research in topics: Pedestrian detection & Computer science. The author has an hindex of 6, co-authored 28 publications receiving 124 citations.

Lessons Learned from the Real-World Deployment of a Connected Vehicle Testbed:

Abstract: The connected vehicle (CV) system promises unprecedented safety, mobility, environmental, economic, and social benefits, which can be unlocked using the enormous amount of data shared between vehic...

Long Short-Term Memory Neural Network-Based Attack Detection Model for In-Vehicle Network Security

Abstract: In this letter, we create two types of attacks to investigate in-vehicle network security: replay attack and amplitude-shift attack. We use these two attacks to create attack datasets from two attack-free in-vehicle controller area network bus datasets (dataset-I and dataset-II), which represent a collection of correlated time series data. We develop a long short-term memory (LSTM) neural-network-based model for detecting replay attack and amplitude-shift attack. For attacks on the dataset-I, the LSTM detection model achieves accuracy of 87.8% and 87.9%, and area under the precision-recall curve (AUPRC) of 0.63 and 0.88, for replay attack and amplitude-shift attack, respectively. For attacks on the dataset-II, the LSTM detection model achieves accuracy of 83.7% and 83.8%, and AUPRC of 0.53 and 0.75, for replay attack and amplitude-shift attack, respectively. Overall, the LSTM detection model shows improvement in accuracy, precision, recall, and AUPRC over the baseline detection models considered in this letter.

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

Abstract: Vehicle-to-pedestrian communication could significantly improve pedestrian safety at signalized intersections. However, it is unlikely that pedestrians will typically be carrying a low latency comm...

Lessons Learned from the Real-world Deployment of a Connected Vehicle Testbed

Abstract: The connected vehicle (CV) system promises unprecedented safety, mobility, environmental, economic and social benefits, which can be unlocked using the enormous amount of data shared between vehicles and infrastructure (e.g., traffic signals, centers). Real world CV deployments including pilot deployments help solve technical issues and observe potential benefits, both of which support the broader adoption of the CV system. This study focused on the Clemson University Connected Vehicle Testbed (CUCVT) with the goal of sharing the lessons learned from the CUCVT deployment. The motivation of this study was to enhance early CV deployments with the objective of depicting the lessons learned from the CUCVT testbed, which includes unique features to support multiple CV applications running simultaneously. The lessons learned in the CUCVT testbed are described at three different levels: i) the development of system architecture and prototyping in a controlled environment, ii) the deployment of the CUCVT testbed, and iii) the validation of the CV application experiments in the CUCVT. Our field experiments with a CV application validated the functionalities needed for running multiple diverse CV applications simultaneously under heterogeneous wireless networking, and realtime and non real time data analytics requirements. The unique deployment experiences, related to heterogeneous wireless networks, real time data aggregation, a distribution using broker system and data archiving with big data management tools, gained from the CUCVT testbed, could be used to advance CV research and guide public and private agencies for the deployment of CVs in the real world.

Cybersecurity Attacks in Vehicle-to-Infrastructure Applications and Their Prevention:

Abstract: A connected vehicle (CV) environment is comprised of diverse computing infrastructure, data communication and dissemination, and data collection systems that are vulnerable to the same cyberattacks...

IEEE 802.11bd & 5G NR V2X: Evolution of Radio Access Technologies for V2X Communications

Abstract: With the rising interest in autonomous vehicles, developing radio access technologies (RATs) that enable reliable and low-latency vehicular communications has become of paramount importance. Dedicated short-range communications (DSRCs) and cellular V2X (C-V2X) are two present-day technologies that are capable of supporting day-1 vehicular applications. However, these RATs fall short of supporting communication requirements of many advanced vehicular applications, which are believed to be critical in enabling fully autonomous vehicles. Both the DSRC and C-V2X are undergoing extensive enhancements in order to support advanced vehicular applications that are characterized by high reliability, low latency, and high throughput requirements. These RAT evolutions-the IEEE 802.11bd for the DSRC and NR V2X for C-V2X-can supplement today's vehicular sensors in enabling autonomous driving. In this paper, we survey the latest developments in the standardization of 802.11bd and NR V2X. We begin with a brief description of the two present-day vehicular RATs. In doing so, we highlight their inability to guarantee the quality of service requirements of many advanced vehicular applications. We then look at the two RAT evolutions, i.e., the IEEE 802.11bd and NR V2X, outline their objectives, describe their salient features, and provide an in-depth description of key mechanisms that enable these features. While both, the IEEE 802.11bd and NR V2X, are in their initial stages of development, we shed light on their preliminary performance projections and compare and contrast the two evolutionary RATs with their respective predecessors.

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.

LSTM-Based Intrusion Detection System for In-Vehicle Can Bus Communications

Abstract: The modern automobile is a complex piece of technology that uses the Controller Area Network (CAN) bus system as a central system for managing the communication between the electronic control units (ECUs). Despite its central importance, the CAN bus system does not support authentication and authorization mechanisms, i.e., CAN messages are broadcast without basic security features. As a result, it is easy for attackers to launch attacks at the CAN bus network system. Attackers can compromise the CAN bus system in several ways including Denial of Service (DoS), Fuzzing and Spoofing attacks. It is imperative to devise methodologies to protect modern cars against the aforementioned attacks. In this paper, we propose a Long Short-Term Memory (LSTM)-based Intrusion Detection System (IDS) to detect and mitigate the CAN bus network attacks. We generate our own dataset by first extracting attack-free data from our experimental car and by injecting attacks into the latter and collecting the dataset. We use the dataset for testing and training our model. With our selected hyper-parameter values, our results demonstrate that our classifier is efficient in detecting the CAN bus network attacks, we achieved an overall detection accuracy of 99.995%. We also compare the proposed LSTM method with the Survival Analysis for automobile IDS dataset which is developed by the Hacking and Countermeasure Research Lab, Korea. Our proposed LSTM model achieves a higher detection rate than the Survival Analysis method.