Asset Details
Do you wish to reserve the book?
Comparative Analysis of Machine Learning Algorithms for Sustainable Attack Detection in Intelligent Transportation Systems Using Long-Range Sensor Network Technology
Hey, we have placed the reservation for you!
By the way, why not check out events that you can attend while you pick your title.
Oops! Something went wrong.
Looks like we were not able to place the reservation. Kindly try again later.
Are you sure you want to remove the book from the shelf?
Comparative Analysis of Machine Learning Algorithms for Sustainable Attack Detection in Intelligent Transportation Systems Using Long-Range Sensor Network Technology
Do you wish to request the book?
Comparative Analysis of Machine Learning Algorithms for Sustainable Attack Detection in Intelligent Transportation Systems Using Long-Range Sensor Network Technology
Please be aware that the book you have requested cannot be checked out. If you would like to checkout this book, you can reserve another copy
We have requested the book for you!
Your request is successful and it will be processed during the Library working hours. Please check the status of your request in My Requests.
Oops! Something went wrong.
Looks like we were not able to place your request. Kindly try again later.
Journal Article
Comparative Analysis of Machine Learning Algorithms for Sustainable Attack Detection in Intelligent Transportation Systems Using Long-Range Sensor Network Technology
2025
Overview
Intelligent transportation systems (ITS) play a crucial role in building sustainable and resilient urban mobility by improving traffic efficiency, reducing energy consumption, and lowering emissions. The integration of IoT technologies, particularly long-range low-power networks such as LoRaWAN, enables energy-efficient communication between vehicles and road infrastructure, supporting the sustainability goals of smart cities. However, the widespread deployment of IoT devices also introduces significant cybersecurity risks that may compromise the safety, reliability, and long-term sustainability of transportation systems. To address this challenge, we propose a method for generating synthetic network data that simulates normal traffic and DDoS attacks by randomly selecting distribution parameters for features like packets per second and unique device addresses, enabling evaluation of machine learning algorithms (e.g., Gradient Boosting, Random Forest, SVM, XGBoost) using F1-score and AUC metrics in a controlled environment. By enhancing cybersecurity and resilience in ITS, our research contributes to the development of safer, more energy-efficient, and sustainable transportation infrastructures.
Publisher
MDPI AG
Subject
