Design of intrusion detection systems on the internet of things infrastructure using machine learning algorithms

Yaser Banadaki; Jalen Brook; Safura Sharifi

doi:10.1117/12.2584499

27 April 2021 Design of intrusion detection systems on the internet of things infrastructure using machine learning algorithms

Yaser Banadaki, Jalen Brook, Safura Sharifi

Author Affiliations +

Proceedings Volume 11594, NDE 4.0 and Smart Structures for Industry, Smart Cities, Communication, and Energy; 115940J (2021) https://doi.org/10.1117/12.2584499
Event: SPIE Smart Structures + Nondestructive Evaluation, 2021, Online Only

Conference Poster

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.

Conference Presentation

Citation Download Citation

Yaser Banadaki, Jalen Brook, and Safura Sharifi "Design of intrusion detection systems on the internet of things infrastructure using machine learning algorithms", Proc. SPIE 11594, NDE 4.0 and Smart Structures for Industry, Smart Cities, Communication, and Energy, 115940J (27 April 2021); https://doi.org/10.1117/12.2584499

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