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Creators/Authors contains: "Banadaki, Yaser M."

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  1. ; ; (, Design of Intrusion Detection Systems on the Internet of Things Infrastructure using Machine Learning Algorithms)
    Meyendorf, Norbert G.; Farhangdoust, Saman (Ed.)
    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. 
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