Hybrid-IDPS for IoT networks : A feature-augmented approach to anomaly detection
Sushamasushama.tanwar@jaipur.manipal.eduDepartment of Computer Science & EngineeringManipal University JaipurJaipur, Rajasthan, 303007, IndiaView full profile → , Shuchita Vaidyashuchitavaidya@gmail.comSchool for Online and Digital LearningSymbiosis International (Deemed University)Pune, Maharashtra, 412115, IndiaView full profile → , *Surbhi SharmaCorresponding authorsurbhi.sharma@jaipur.manipal.eduDepartment of Computer Science & EngineeringManipal University JaipurJaipur, Rajasthan, 303007, IndiaView full profile → , Prashant Vatsprashant.vats@jaipur.manipal.eduDepartment of Computer Science & EngineeringManipal University JaipurJaipur, Rajasthan, 303007, IndiaView full profile → , Anudeep Aroraanudeep.arora@siib.ac.inDepartment of ManagementSymbiosis Institute of International BusinessSymbiosis International (Deemed University)Pune, Maharashtra, 412115, IndiaView full profile →
* Corresponding author · click or hover a name for details
- Received:
- 01 Dec 2025
- Published Online:
- 31 Aug 2026
- Article type:
- Research Article
- Language:
- EN
- Article no.:
- JIOS-2235
- Pages:
- 2931–2938
Abstract
The rapid growth of IoT networks has expanded the attack surface, making conventional intrusion detection systems inadequate due to resource constraints and evolving threats. This paper proposes a Hybrid Intrusion Detection and Prevention System (Hybrid-IDPS) that integrates feature-augmented machine learning with lightweight cryptographic assurance for secure and adaptive anomaly detection. The framework employs a two-stage architecture combining discrete mathematical feature engineering with a hybrid shallow–deep learning engine. Elliptic-curve–based authentication and hash-chained alert logging ensure integrity, non-repudiation, and secure coordination. Experimental results on benchmark IoT datasets show improved detection accuracy, reduced false positives, and lower computational overhead. The proposed system offers a scalable and resource-efficient solution for next-generation IoT security.
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References
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