IoT Network Attack Detection using Supervised Machine Learning

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dc.contributor.authorKrishnan, Sundar
dc.contributor.authorNeyaz, Ashar
dc.contributor.authorLiu, Qingzhong
dc.date.accessioned2021-12-15T19:03:47Z
dc.date.available2021-12-15T19:03:47Z
dc.date.issued2021
dc.descriptionArticle originally published in International Journal of Artificial Intelligence and Expert Systems
dc.description.abstractThe use of supervised learning algorithms to detect malicious traffic can be valuable in designing intrusion detection systems and ascertaining security risks. The Internet of things (IoT) refers to the billions of physical, electronic devices around the world that are often connected over the Internet. The growth of IoT systems comes at the risk of network attacks such as denial of service (DoS) and spoofing. In this research, we perform various supervised feature selection methods and employ three classifiers on IoT network data. The classifiers predict with high accuracy if the network traffic against the IoT device was malicious or benign. We compare the feature selection methods to arrive at the best that can be used for network intrusion predictionen_US
dc.description.sponsorshipen_US
dc.identifier.citationKumar A, Neyaz A and Liu Q (2021). IoT Network Attack Detection using Supervised Machine Learning. International Journal of Artificial Intelligence and Expert Systems, 10(2): 18-32.en_US
dc.identifier.issn2180-124X
dc.identifier.urihttps://hdl.handle.net/20.500.11875/3245
dc.publisherInternational Journal of Artificial Intelligence and Expert Systemsen_US
dc.relation.ispartofseriesVolume 10: Issue 2
dc.subjectsupervised learningen_US
dc.subjectnetwork attack detectionen_US
dc.subjectIoTen_US
dc.subjectnetwork forensicsen_US
dc.subjectnetwork securityen_US
dc.titleIoT Network Attack Detection using Supervised Machine Learningen_US
dc.typeArticleen_US

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