Rule-Based Cyber Security Model with Adaptive Interval Type-2 Fuzzy Neural Networks in Multi-Sensor Data Monitoring
| International Journal of Electronics and Communication Engineering |
| © 2025 by SSRG - IJECE Journal |
| Volume 12 Issue 10 |
| Year of Publication : 2025 |
| Authors : Radhika Rajoju, P. Swetha |
10.14445/23488549/IJECE-V12I10P112
How to Cite?
Radhika Rajoju, P. Swetha, "Rule-Based Cyber Security Model with Adaptive Interval Type-2 Fuzzy Neural Networks in Multi-Sensor Data Monitoring," SSRG International Journal of Electronics and Communication Engineering, vol. 12, no. 10, pp. 129-146, 2025. Crossref, https://doi.org/10.14445/23488549/IJECE-V12I10P112
Abstract:
In the context of multi-sensor data monitoring systems, especially in critical domains like Healthcare, cybersecurity plays a pivotal role in ensuring the integrity, confidentiality, and availability of the data being collected, transmitted, and analyzed. These systems often gather sensitive physiological and behavioral information from multiple sensors—such as ECG, EEG, temperature, blood pressure, and movement sensors-making them prime targets for cyberattacks. Unauthorized access or tampering with this data can lead to serious consequences, including incorrect diagnoses, compromised patient safety, and data privacy breaches. In this paper, a Rule-Based Adaptive Type-2 Fuzzy Neural Network (RbAFNN) and an HMM are used to manage data from multiple sensors in healthcare monitoring. With interval Type-2 fuzzy logic and the adaptive neural network, the approach can properly work with uncertain and imprecise data and quickly self-adjust to new changes in patients. With HMM, sensor data are handled properly over time, so fault detection and health classification improve. Experiments with several healthcare-related datasets find that the RbAFNN-HMM model delivers high accuracy, a high sensitivity level, and a low number of false positives in the tasks of health monitoring and cyber threat detection with efficient performance in real-time. Experimental analysis stated that the accuracy of detecting a phone call with the RbAFNN-HMM model is more than 96%, it has a 97% sensitivity, and its false positives are no more than 3%. The system is very accurate in detecting threats, as its threat detection is around 92% for different attack types, and it usually mitigates threats with a success rate above 89%. These results prove that the framework helps deliver correct, prompt, and secure health care, thereby making it a dependable solution for changing and uncertain situations in hospitals. The framework’s solid cybersecurity features ensure that important data is safe and better protected against DoS, spoofing, and data injection. The advanced solution offered by the system is reliable, smart, and ensures security in unstable and uncertain situations.
Keywords:
Healthcare Monitoring, Cybersecurity, Multi-Sensor, Type-2 Fuzzy, Hidden Markov Model (HMM), Neural Network, Rule-Based Model.
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