A feature-augmented hybrid intrusion detection and prevention system for secure IoT networks
*VishalCorresponding authorvishalhim@yahoo.comDepartment of Computer ApplicationsLovely Professional UniversityJalandhar, Punjab, 144411, IndiaView full profile → , Ruchika Bhakharruchika.bhakhar@krmangalam.edu.inDepartment of Computer Science and EngineeringSchool of Engineering & TechnologyK. R. Mangalam UniversityGurugram, Haryana, 122103, IndiaView full profile → , Pallavi Pandeypallavi.pandey@iilm.eduDepartment of Computer Science and EngineeringIILM UniversityGurugram, Haryana, 122003, IndiaView full profile → , Archna Goyalarchana.goyal@niu.edu.inDepartment of Computer Science and EngineeringSchool of Engineering and TechnologyNoida International UniversityGreater Noida, Uttar Pradesh, 203201, IndiaView full profile → , Manju Mandotmanju.mandot@gmail.comDepartment of Computer Science and Information TechnologyJanardan Rai Nagar Rajasthan Vidyapeeth (Deemed-to-be University)Udaipur, Rajasthan, 313001, 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-2335
- Pages:
- 3053–3062
Abstract
The rapid expansion of IoT devices has increased security risks due to limited resources, diverse protocols, and distributed environments. Traditional intrusion detection systems often fail to achieve high accuracy with low false alarms in such conditions. This study presents a Hybrid-IDPS that combines statistical traffic analysis with machine learning–based anomaly detection and feature enhancement. It integrates rule-based detection for known threats with a CNN and gradient boosting model for improved classification. A tiered edge–fog architecture enables real-time processing with low overhead. Experimental results show 98.7% accuracy and significantly reduced false positives, highlighting its efficiency and scalability for modern IoT applications.
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References
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