Machine learning - IoT 23

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Primary 2021_Spring_MISSM_Bains-et-al.pdf (1.9 MB)

DOI

https://doi.org/10.7939/r3-r5xn-yz96

Date

2021-06-01

Author(s)

Bains, Jayant Singh
Goyal, Rahul
Kopanati, Hemanth Varma
Savaram, Bhargav Krishna

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Abstract

Description

IoT devices have become the future of many industries like household, security, etc. but these devices have a requirement that is that they should be connected to a network. This makes them vulnerable to outside attacks. If security is built into each device, it would cost more and would not reliable but if machine learning is used in the IDS and IPS to protect the system. This paper shows how machine learning can be used to detect malicious traffic in IoT datasets. The accuracy recorded ranges from 98.9%-100% depending on the dataset.

Item Type

http://purl.org/coar/resource_type/c_1843

Title

Machine learning - IoT 23

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License

http://creativecommons.org/licenses/by-nc/4.0/

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Subject/Keywords

Internet of Things
IoT
intrusion detection systems
intrusion prevention system
decision tree
logistic regression
Artificial Neural Network

Language

en

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Master of Information Systems Security Management (MISSM) and Master of Information Systems Assurance Management (MISAM) Project Reports (Concordia University of Edmonton)