Smart City Gnosys

Smart city article details

Title	Design Of Intrusion Detection Systems On The Internet Of Things Infrastructure Using Machine Learning Algorithms
ID_Doc	18874
Authors	Banadaki Y.; Brook J.; Sharifi S.
Year	2021
Published	Proceedings of SPIE - The International Society for Optical Engineering, 11594
DOI	http://dx.doi.org/10.1117/12.2584499
Abstract	Network intrusion detection systems (NIDS) for Internet-of-Things (IoT) infrastructure are among the most critical tools to ensure the protection and security of networks against malicious cyberattacks. This paper employs four machine learning algorithms and evaluates their performance in NIDS considering the accuracy, precision, recall, and F-score. The comparative analysis conducted using the CICIDS2017 dataset reveals that the Boosted machine learning techniques perform better than the other algorithms reaching the predicted accuracy of above 99% in detecting cyberattacks. Such ML-based attack detectors also have the largest weighted metrics of F1-score, precision, and recall. The results assist the network engineers in choosing the most effective machine learning-based NIDS to ensure network security for today's growing IoT network traffic. © 2021 SPIE.
Author Keywords	Internet-of-Things; Machine Learning Algorithms; Malicious Cyberattacks; Network Intrusion Detection Systems; Network Traffic

Similar Articles

Id	Similarity	Authors	Title	Published
24715	0.907	Ali M.; Pervez S.; Hosseini S.E.; Siddhu M.K.	Evaluation And Detection Of Cyberattack In Iot-Based Smart City Networks Using Machine Learning On The Unsw-Nb15 Dataset	International Journal of Online and Biomedical Engineering, 21, 2 (2025)
47716	0.888	Wang M.; Yang N.; Weng N.	Securing A Smart Home With A Transformer-Based Iot Intrusion Detection System	Electronics (Switzerland), 12, 9 (2023)
33346	0.888	Berhili M.; Chaieb O.; Benabdellah M.	Intrusion Detection Systems In Iot Based On Machine Learning: A State Of The Art	Procedia Computer Science, 251 (2024)
5365	0.887	Natarajan B.; Bose S.; Maheswaran N.; Logeswari G.; Anitha T.	A Survey: An Effective Utilization Of Machine Learning Algorithms In Iot Based Intrusion Detection System	12th IEEE International Conference on Advanced Computing, ICoAC 2023 (2023)
36080	0.886	Ahanger T.A.; Ullah I.; Algamdi S.A.; Tariq U.	Machine Learning-Inspired Intrusion Detection System For Iot: Security Issues And Future Challenges	Computers and Electrical Engineering, 123 (2025)
59592	0.886	Khatkar M.; Kumar K.; Kumar B.	Unfolding The Network Dataset To Understand The Contribution Of Features For Detecting Malicious Activities Using Ai/Ml	Materials Today: Proceedings, 59 (2022)
47766	0.885	Plazas Olaya M.K.; Vergara Tejada J.A.; Aedo Cobo J.E.	Securing Microservices-Based Iot Networks: Real-Time Anomaly Detection Using Machine Learning	Journal of Computer Networks and Communications, 2024 (2024)
23835	0.884	Arabiat A.; Altayeb M.	Enhancing Internet Of Things Security: Evaluating Machine Learning Classifiers For Attack Prediction	International Journal of Electrical and Computer Engineering, 14, 5 (2024)
32898	0.883	Kaur B.; Dadkhah S.; Shoeleh F.; Neto E.C.P.; Xiong P.; Iqbal S.; Lamontagne P.; Ray S.; Ghorbani A.A.	Internet Of Things (Iot) Security Dataset Evolution: Challenges And Future Directions	Internet of Things (Netherlands), 22 (2023)
37199	0.881	Al-Ambusaidi M.; Yinjun Z.; Muhammad Y.; Yahya A.	Ml-Ids: An Efficient Ml-Enabled Intrusion Detection System For Securing Iot Networks And Applications	Soft Computing, 28, 2 (2024)