Intelligent Cybersecurity for IoT: A Hybrid QRIME-SDPN Approach for Network Attack Detection on CIC-IoT-2023

Abstract

The need for intelligent intrusion detection systems (IDS) has arisen due to the fact that IoT networks are becoming more vulnerable to complex cyber threats due to their fast growth. Problems with high-dimensional data, redundant characteristics, and changing assault patterns are common causes of performance constraints in traditional intrusion detection systems. This paper presents a hybrid intrusion detection system that syndicates a Stacked Deep Polynomial Network (SDPN) classifier with QRIME-based feature selection to efficiently and accurately detect network attacks on CIC-IoT-2023 dataset. The goal is to tackle these problems. In order to improve computational efficiency, the Quantum-Inspired Redundant and Irrelevant feature Minimization and Extraction (QRIME) method is used to decrease the dimensionality of features. This method ensures that only the most significant characteristics are used for classification. By using deep polynomial transformations, the SDPN classifier is able to improve its generalization capabilities and catch intricate attack patterns. Together, they enable the model to reduce false positives while efficiently detecting varied IoT-based assaults. The suggested QRIMESDPN model beats both conventional and deep learning models in terms of recall, accuracy, precision, <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{F 1}$</tex>-score, besides false alarm rate (FAR) when tested on the CIC-IoT-2023 dataset. The model's successful handling of high-dimensional network traffic data, with a balance among computational efficiency and detection capability, is demonstrated by the findings. The overall result of this study is an innovative and scalable method for intrusion detection in the Internet of Things (IoT) that combines deep polynomial-based classification with feature selection influenced by quantum mechanics. To further improve IDS performance in dynamic IoT environments, future work can centre on real-time deployment, adversarial resilience, and transfer learning. In light of the critical requirement for flexible intrusion detection systems to counter evolving cyber threats, this study makes a substantial contribution to Internet of Things security.

Topics & Concepts