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.
翻译:现代科学进步往往有助于引入和完善从未见过的技术。 这也许是人类维持和监测的任务,因此,我们社会依靠机器学习来协助完成这项任务。随着新技术带来新的方法,从而出现了规避现有网络安全措施的新方法。本研究审视了目前业界用于恶意软件和入侵探测的三种不同的“物”网络安全算法互联网的有效性:随机森林(Rand Forest)、支持-视频机器(SVM)和K-Nearest Nieghbor(KNN),每个算法都经过了Aposmat IoT-23数据集的培训和测试,该数据集最早于2018年和2019年发布,最早于2020年发布。RF、SVM和KNN达到了92.96%、86.23%和91.48%的峰值,分别是入侵探测和92.27%、83.52%和89.80%的恶意软件检测。发现所有三种算法都能够有效地用于2021年IoT网络安全现状。