mlsanityData to 14 Oct 2026

A Modern Analysis of Aging Machine Learning Based IoT Cybersecurity Methods

arXiv 2110.07832 ↗First posted 15 Oct 2021cs.CR cs.LGcited by 10

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Abstract (arXiv)

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.

Key facts. Cited by 10 papers in the map (data through 14 Oct 2026). Builds on 4 of its 24 references that are in the map. First posted on arXiv 15 Oct 2021.

Builds on

4 of 24 references.

References · 4

  1. 2018IoT Security Techniques Based on Machine Learningcited by 22
  2. 2018Machine Learning DDoS Detection for Consumer Internet of Things Devicescited by 21
  3. 2017Detection of Unauthorized IoT Devices Using Machine Learning Techniquescited by 8
  4. 2019Fast Authentication and Progressive Authorization in Large-Scale IoT: How to Leverage AI for Security Enhancement?cited by 3

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Cited by year

Citing papers per year: 2022: 8; 2023: 2.

10 papers.

  1. 2022Hold On and Swipe: A Touch-Movement Based Continuous Authentication Schema based on Machine Learningcited by 12
  2. 2022Human Activity Recognition models using Limited Consumer Device Sensors and Machine Learningcited by 8
  3. 2022A Close Look into Human Activity Recognition Models using Deep Learningcited by 8
  4. 2022Evaluation of a User Authentication Schema Using Behavioral Biometrics and Machine Learningcited by 6
  5. 2022Using Deep Learning to Detecting Deepfakescited by 6
  6. 2022A Robust Framework for Deep Learning Approaches to Facial Emotion Recognition and Evaluationcited by 3
  7. 2023Deepfake Detection Analyzing Hybrid Dataset Utilizing CNN and SVMcited by 3
  8. 2023Your Identity is Your Behavior -- Continuous User Authentication based on Machine Learning and Touch Dynamicscited by 1
  9. 2022Exploration of Machine Learning Classification Models Used for Behavioral Biometrics Authenticationcited by 0
  10. 2022Mitigating Presentation Attack using DCGAN and Deep CNNcited by 0