A Hybrid Approach for IoT Security: Combining Ensemble Learning with Fuzzy Logic

Area of Science:

• Cybersecurity
• Artificial Intelligence
• Internet of Things

Background:

• The proliferation of Internet of Things (IoT) devices presents significant security challenges due to their diverse and resource-constrained nature.
• Traditional security methods are inadequate for effectively combating the growing threat of malware targeting IoT ecosystems.
• Malware poses a substantial risk to the integrity and functionality of interconnected IoT systems.

Purpose of the Study:

• To propose a novel hybrid security framework for Internet of Things (IoT) systems.
• To enhance the accuracy and interpretability of malware detection in resource-limited IoT environments.
• To develop a robust solution addressing the cybersecurity vulnerabilities inherent in diverse IoT ecosystems.

Main Methods:

• Integration of ensemble learning techniques to combine multiple classifiers for improved detection accuracy.
• Application of fuzzy logic for a flexible and human-intuitive assessment of IoT system security status.
• Development of a hybrid framework leveraging ensemble learning's predictive power and fuzzy logic's interpretability.

Main Results:

• The proposed framework achieves high-accuracy malware detection rates for IoT devices.
• The integrated fuzzy system provides a more flexible and human-oriented evaluation of security status.
• Experimental validation demonstrates the effectiveness of the hybrid approach in diverse IoT scenarios.

Conclusions:

• The novel hybrid approach offers a significant advancement in securing Internet of Things (IoT) devices against malware.
• The combination of ensemble learning and fuzzy logic provides a powerful and interpretable solution for IoT cybersecurity.
• This study presents a practical and applicable method for enhancing the security of various IoT ecosystems.