  Modern scientific advancements often contribute to the introduction and
refinement of never-before-seen technologies. This can be quite the task for
humans to maintain and monitor and as a result, our society has become reliant
on machine learning to assist in this task. With new technology comes new
methods and thus new ways to circumvent existing cyber security measures. This
study examines the effectiveness of three distinct Internet of Things cyber
security algorithms currently used in industry today for malware and intrusion
detection: Random Forest (RF), Support-Vector Machine (SVM), and K-Nearest
Neighbor (KNN). Each algorithm was trained and tested on the Aposemat IoT-23
dataset which was published in January 2020 with the earliest of captures from
2018 and latest from 2019. The RF, SVM, and KNN reached peak accuracies of
92.96%, 86.23%, and 91.48%, respectively, in intrusion detection and 92.27%,
83.52%, and 89.80% in malware detection. It was found all three algorithms are
capable of being effectively utilized for the current landscape of IoT cyber
security in 2021.
