A New Ensemble-Based Intrusion Detection System for Internet of Things

TLDR

The rapid expansion of IoT networks has increased cyber threats, prompting the development of intrusion detection systems that rely on machine learning to safeguard data integrity. This study proposes an ensemble-based intrusion detection model to enhance detection performance. The model combines logistic regression, naive Bayes, and decision tree classifiers in a voting scheme, evaluated against state‑of‑the‑art techniques on the CICIDS2017 dataset. The ensemble achieves higher accuracy than existing models in both binary and multi‑class classification scenarios.

Abstract

Abstract The domain of Internet of Things (IoT) has witnessed immense adaptability over the last few years by drastically transforming human lives to automate their ordinary daily tasks. This is achieved by interconnecting heterogeneous physical devices with different functionalities. Consequently, the rate of cyber threats has also been raised with the expansion of IoT networks which puts data integrity and stability on stake. In order to secure data from misuse and unusual attempts, several intrusion detection systems (IDSs) have been proposed to detect the malicious activities on the basis of predefined attack patterns. The rapid increase in such kind of attacks requires improvements in the existing IDS. Machine learning has become the key solution to improve intrusion detection systems. In this study, an ensemble-based intrusion detection model has been proposed. In the proposed model, logistic regression, naive Bayes, and decision tree have been deployed with voting classifier after analyzing model’s performance with some prominent existing state-of-the-art techniques. Moreover, the effectiveness of the proposed model has been analyzed using CICIDS2017 dataset. The results illustrate significant improvement in terms of accuracy as compared to existing models in terms of both binary and multi-class classification scenarios.