Abstract
The Industrial Internet of Things (IIoT) stands as a revolutionary force, intertwining physical devices, sensors, and industrial systems to usher in advanced automation and data-driven decision-making across various sectors. However, this increased connectivity has exposed these systems to a growing array of cyber threats. Safeguarding IIoT environments becomes crucial to maintain the integrity, availability, and confidentiality of critical industrial processes. In response, this research explores the optimization of neural network parameters using Genetic Algorithms (GA). The application of GA has led to achieve a remarkable accuracy of 95% across various attack types. The results demonstrate a high performance in identifying complex attack patterns, contributing to the resilience of IIoT systems against emerging cyber threats.
Library of Congress Subject Headings
Internet of things--Industrial applications; Internet of things--Security measures; Neural networks (Computer science)
Publication Date
2024
Document Type
Thesis
Student Type
Graduate
Degree Name
Cybersecurity (MS)
Department, Program, or Center
Cybersecurity, Department of
Advisor
Kevser Ovaz Akpinar
Recommended Citation
Alblooshi, Hamad, "Optimizing Neural Networks for IIoT Attack Detection" (2024). Thesis. Rochester Institute of Technology. Accessed from
https://repository.rit.edu/theses/11923
Campus
RIT Dubai
Plan Codes
COMPSEC-MS
