A Lightweight Hybrid Deep Learning model for Real-Time Intrusion Detection in IoT Networks.
- Title
- A Lightweight Hybrid Deep Learning model for Real-Time Intrusion Detection in IoT Networks.
- Creator
- Swain, Anugraha; Poonia, Ramesh Chandra; Shanbhog, Manjula
- Description
- In IoT networks, cyber threats are hard to identify because of dynamic and heterogenic nature of IoT traffic as it cannot be identified with more traditional intrusion detection systems. This paper discusses the deep learning methods of intrusion detection in terms of CNN+LSTM, CNN+BiLSTM, CNN+GRU, and BiGRU+RF and propose a novel Dense+SimpleRNN architecture. Preprocessing includes label encoding, feature selection, normalization, SMOTE balancing, and reshaping sequences, using the RT-IoT 2022 dataset. The paper demonstrates, CNN + BiLSTM and CNN + GRU achieving similar accuracy but with higher computational cost. On the other hand, the proposed Dense+SimpleRNN has 98.59% accuracy, precision and recall and Fl-score, which are higher than the baselines models. The results point that Dense+SimpleRNN is an efficient and lightweight IDS that is very appropriate in real-time IoT network security. 2025 IEEE.
- Source
- Proceedings of International Conference on Digital Innovations for Sustainable Solutions, ICDISS 2025;
- Date
- 01-01-2025
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Subject
- Deep Learning; Dense-SimpleRNN; Internet of Things; Intrusion Detection System; ThreatAssessment
- Coverage
- Swain A., Department of Computer Science, CHRIST University, Bangalore, India; Poonia R.C., Department of Computer Science, CHRIST University, Bangalore, India; Shanbhog M., Department of Computer Science, CHRIST University, Bangalore, India
- Rights
- Restricted Access; Hardcopy may be available in the library
- Relation
- ISBN: 979-833155641-9;
- Format
- online
- Language
- English
- Type
- Conference paper
Collection
Citation
Swain, Anugraha; Poonia, Ramesh Chandra; Shanbhog, Manjula, “A Lightweight Hybrid Deep Learning model for Real-Time Intrusion Detection in IoT Networks.,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 18, 2026, https://archives.christuniversity.in/items/show/25959.