Hybrid Machine Learning Models for Intrusion Detection in IoT: Leveraging a Real-World IoT Dataset

TL;DR

This paper presents a hybrid machine learning ensemble approach for IoT intrusion detection, utilizing multiple models to improve accuracy and robustness on a real-world dataset, outperforming individual models.

Contribution

It introduces a voting-based hybrid classifier combining RF, XGBoost, KNN, and AdaBoost for IoT intrusion detection, demonstrating superior performance over standalone models.

Findings

Hybrid models outperform standalone algorithms in detection accuracy.

The ensemble approach effectively handles data complexity and scalability.

Results validated on the IoT-23 dataset with improved detection metrics.

Abstract

The rapid growth of the Internet of Things (IoT) has revolutionized industries, enabling unprecedented connectivity and functionality. However, this expansion also increases vulnerabilities, exposing IoT networks to increasingly sophisticated cyberattacks. Intrusion Detection Systems (IDS) are crucial for mitigating these threats, and recent advancements in Machine Learning (ML) offer promising avenues for improvement. This research explores a hybrid approach, combining several standalone ML models such as Random Forest (RF), XGBoost, K-Nearest Neighbors (KNN), and AdaBoost, in a voting-based hybrid classifier for effective IoT intrusion detection. This ensemble method leverages the strengths of individual algorithms to enhance accuracy and address challenges related to data complexity and scalability. Using the widely-cited IoT-23 dataset, a prominent benchmark in IoT cybersecurity research, we evaluate our hybrid classifiers for both binary and multi-class intrusion detection problems, ensuring a fair comparison with existing literature. Results demonstrate that our proposed hybrid models, designed for robustness and scalability, outperform standalone approaches in IoT environments. This work contributes to the development of advanced, intelligent IDS frameworks capable of addressing evolving cyber threats.