2023
doi: 10.12720/jait.14.4.811-820
Malicious Agricultural IoT Traffic Detection and Classification: A Comparative Study of ML Classifiers
Omar Bin Samin1,2,*
,
Nasir Ahmed Abdulkhader Algeelani2 ,
Ammar Bathich2 ,
Abdul Qadus1 ,
Adnan Amin1
- 1Center of Excellence in Information Technology, Institute of Management Sciences, Peshawar, Pakistan
- 2Faculty of Computer & Information Technology, Al-Madinah International University, Kuala Lumpur, Malaysia
- Manuscript receivedMarch 24, 2023
- revisedApril 18, 2023
- acceptedMay 10, 2023
- publishedAugust 17, 2023
Abstract
The number of internet-connected devices is rising, resulting in a global network of connected devices, referred to as Internet of Things (IoT). The technologically advanced agriculture industry employs IoT to monitor their environment and automate required functionality. IoT devices generate enormous amount of confidential and critical data, hence, securing the information is of significant importance. This research proposes integrating computationally intensive Machine Learning (ML) classifiers with resource-constrained IoT devices in order to safeguard the obtained data. This study analyses Naïve Bayes and Decision Tree for a cutting-edge Edge-IIoTset cybersecurity dataset encompassing 15 classes of IoT traffic derived from Soil Moisture, Temperature, Humidity, Water Level, and Water pH Sensors to enhance IoT data security. The experimental results of both the ML classifiers on given subsets of Edge-IIoTset presented Decision Tree as superior option, achieving accuracy of 72% and 73% for ML and DNN Edge-IIoTset respectively as compared to Naïve Bayes with accuracy of 47% and 45% respectively.
Keywords
- internet of things
- anomaly detection
- malicious activity classification
- naïve bayes
- decision tree
How to Cite
Omar Bin Samin, Nasir Ahmed Abdulkhader Algeelani, Ammar Bathich, Abdul Qadus, and Adnan Amin, "Malicious Agricultural IoT Traffic Detection and Classification: A Comparative Study of ML Classifiers," Journal of Advances in Information Technology, vol. 14, no. 4, pp. 811-820, 2023. https://doi.org/10.12720/jait.14.4.811-820
Copyright & License
Copyright © 2023 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
