Scalable machine learning-based intrusion detection system for IoT-enabled smart cities

• Main Authors: Rahman, Md. Arafatur, Asyhari, A. Taufiq, Leong, L. S., Satrya, G. B., Tao, M. Hai, Mohamad Fadli, Zolkipli
• Format: Article
• Language: English; English
• Published: Elsevier Ltd 2020
• Subjects: QA76 Computer software; TA Engineering (General). Civil engineering (General); TK Electrical engineering. Electronics Nuclear engineering
• Online Access: http://umpir.ump.edu.my/id/eprint/28942/1/Scalable%20machine%20learning-based%20intrusion%20detection%20system%20for%20IoT_FULL.pdf; http://umpir.ump.edu.my/id/eprint/28942/2/Scalable%20machine%20learning-based%20intrusion%20detection%20system%20for%20IoT.pdf; http://umpir.ump.edu.my/id/eprint/28942/; https://doi.org/10.1016/j.scs.2020.102324; https://doi.org/10.1016/j.scs.2020.102324

Given a scale expansion of Internet of Things for sustainable resource management in smart cities, proper design of an intrusion detection system (IDS) is critical to safeguard the future network infrastructure from intruders. With the growth of connected things, the most-widely used centralized (cl...