Hybrid Deep Learning and Rule-Based Models for Real-Time Intrusion Detection in IoT Networks: Extending IDS to Edge AI
Year: 2025
Type: article
Abstract: The rapid expansion of Internet of Things (IoT) networks has introduced significant security vulnerabilities, necessitating intelligent Intrusion Detection Systems (IDS) capable of operating under constrained edge environments. This paper presents a hybrid framework combining deep learning and rule-based models for real-time intrusion detection in IoT ecosystems. The proposed Edge-IDS integrates a CNN-LSTM-based deep model for behavioral pattern extraction with Snort-inspired rule-based decision fusion for anomaly validation. Evaluation across BoT-IoT, TON-IoT, and CICIDS2019 datasets demonstrates an average detection accuracy of 98.6% and latency reduction of 31% compared to centralized IDS architectures. The framework’s edge-deployable nature and adaptability to dynamic IoT environments make it suitable for future 6G and industrial automation networks.
Authors: Rajat Paswan, Atishay Prem, Nitin Jain
Institution: University of Arizona
Language: English
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W4416142833
FWCI:
Cites:
Cited by:
Related to: