Hybrid Deep Learning and Rule-Based Models for Real-Time Intrusion Detection in IoT Networks: Extending IDS to Edge AI

• Author(s): Rajat Paswan; Atishay Prem; Nitin Jain
• Paper ID: 1711965
• Page: 2085-2090
• Published Date: 01-11-2025
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 9 Issue 3 September-2025

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.

Citations

IRE Journals:
Rajat Paswan, Atishay Prem, Nitin Jain "Hybrid Deep Learning and Rule-Based Models for Real-Time Intrusion Detection in IoT Networks: Extending IDS to Edge AI" Iconic Research And Engineering Journals Volume 9 Issue 3 2025 Page 2085-2090 https://doi.org/10.64388/IREV9I4-1711286

IEEE:
Rajat Paswan, Atishay Prem, Nitin Jain "Hybrid Deep Learning and Rule-Based Models for Real-Time Intrusion Detection in IoT Networks: Extending IDS to Edge AI" Iconic Research And Engineering Journals, 9(3) https://doi.org/10.64388/IREV9I4-1711286