Hybrid deep learning-based IoT intrusion detection : A comparative study of CNN, GRU, LSTM, and hybrid architectures
*Sonkarlay J.Y. WeamieCorresponding authorskweamie@hnu.edu.cnCollege of Computer Science and Electronic EngineeringYuelu DistrictHunan UniversityChangsha, Hunan, 410082, China0000-0002-9414-7171View full profile → , Vinothkumar Kolluruvkolluru@stevens.eduDepartment of Data Science1 Castle Point TerraceStevens Institute of TechnologyHoboken, NJ, 07030, U.S.A.0009-0006-1713-5720View full profile → , AB Jallah Balyemah AB Jallah Balyemahab.jallah1990@gmail.comCollege of Computer Science and Electronic EngineeringYuelu DistrictHunan UniversityChangsha, Hunan, 410082, China0009-0002-8815-6060View full profile → , Yagnesh Challagundlayagneshnaidu1234@gmail.comDepartment of Engineering EducationHerbert Wertheim College of EngineeringUniversity of FloridaGainesville, Florida, 32611, U.S.A.0009-0005-5221-1517View full profile →
* Corresponding author · click or hover a name for details
- Received:
- 10 Dec 2024
- Published Online:
- 30 Sep 2025
- Article type:
- Research Article
- Language:
- EN
- Article no.:
- JIOS-2027
- Pages:
- 1983–1994
Abstract
Keywords
Subject Classifications
References
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