Atdhe B, Marika Apostolova and Artan Luma
Adv. Artif. Intell. Mach. Learn., 4 (3):2408-2415
Atdhe B : University of South East European (SEEU), Tetovo, N. Macedonia.
Marika Apostolova : University of South East European (SEEU), Tetovo, N. Macedonia.
Artan Luma : University of South East European (SEEU), Tetovo, N. Macedonia.
Article History: Received on: 15-May-24, Accepted on: 12-Jul-24, Published on: 19-Jul-24
Corresponding Author: Atdhe B
Email: ab29762@seeu.edu.mk
Citation: Atdhe Buja, et al. A Model Proposal of Cybersecurity for the IIoT: Enhancing IIoT Cybersecurity through Machine Learning and Deep Learning Techniques. Advances in Artificial Intelligence and Machine Learning. 2024;4(3):140.
The Industrial Internet of Things
(IIoT) acceleration has caused automation and data exchange enhancements within
industrial surroundings. Yet, this evolution has presented security issues due
to the raised exposure of critical infrastructure to cyber threats. This study
focuses on designing a thorough model for identifying and mitigating
vulnerabilities within IIoT networks utilizing Machine Learning (ML) and Deep
Learning (DL) techniques. A replicated IIoT network infrastructure was set to
communicate and exchange data for simulation. Use of Python script to execute
network scanning and data collection, distinct possible vulnerabilities. Then,
ML-DL analysis is handled by employing techniques of gradient boosting,
logistic regression, decision trees, random forest, multilayer perceptron, and convolutional
neural network. Throughout, gradient boosting has proven to higher performance
accuracy rate in recognizing the most impactful vulnerabilities. As well as a
model integral part of a Cost-Benefit Analysis (CBA) provides security
recommendations to mitigate identified vulnerabilities. According to the CBA
model vulnerabilities are prioritized based on the severity, related costs, and
potential benefits of mitigation. The proposed Cybersecurity model in addition
to high accuracy in vulnerability detection also provides a standardized
approach for categorizing Cybersecurity countermeasures according to
cost-effectiveness. This study emphasizes the need for a consolidated
Cybersecurity model for the IIoT and shows the capability of ML techniques to advance
Cybersecurity posture. Future work considered testing the model in a real
operation environment of IIoT, refining the model, and enrichment with more
knowledge base actionable mitigations.