Optimizing Resource Allocation and Energy Efficiency in Federated Fog Computing for IoT

TL;DR

This paper presents a multi-layered machine learning framework that significantly improves ARP spoofing detection accuracy and reduces false positives in IoT networks, enhancing security and reliability.

Contribution

It introduces a novel multi-layer ensemble classifier system tailored for IoT security, addressing dataset imbalance and enabling dynamic retraining for robust ARP spoofing detection.

Findings

Detection accuracy up to 97.5%

False positive rate below 2%

Faster detection times compared to existing methods

Abstract

Address Resolution Protocol (ARP) spoofing attacks severely threaten Internet of Things (IoT) networks by allowing attackers to intercept, modify, or block communications. Traditional detection methods are insufficient due to high false positives and poor adaptability. This research proposes a multi-layered machine learning-based framework for intelligently detecting ARP spoofing in IoT networks. Our approach utilizes an ensemble of classifiers organized into multiple layers, each layer optimizing detection accuracy and reducing false alarms. Experimental evaluations demonstrate significant improvements in detection accuracy (up to 97.5\%), reduced false positive rates (less than 2\%), and faster detection time compared to existing methods. Our key contributions include introducing multi-layer ensemble classifiers specifically tuned for IoT networks, systematically addressing dataset imbalance problems, introducing a dynamic feedback mechanism for classifier retraining, and validating practical applicability through extensive simulations. This research enhances security management in IoT deployments, providing robust defenses against ARP spoofing attacks and improving reliability and trust in IoT environments.