ArticleScientific reports2024
A novel intrusion detection framework for optimizing IoT security.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Sequential submodular feature-sample selection for lightweight and update-efficient IoT intrusion detection.Scientific reports · 2026Article
- Intrusion Detection in the Internet of Things: A Comprehensive Review of Techniques, Architectures, Datasets, and Emerging Trends.Sensors (Basel, Switzerland) · 2026Review
- Improving IoT security through an explainable hybrid CNN-transformer model and federated learning.Scientific reports · 2026Article
- Blockchain-driven trust management and AI computing for sensor networks optimization.Scientific reports · 2026Article
- A dataset-centric review of IoT and IIoT intrusion detection: realism, evaluation biases, and future research directions.Frontiers in big data · 2026Review
- An enhanced multi-model ensemble learning architecture for robust network intrusion detection.Frontiers in artificial intelligence · 2026Article
- A privacy preserving intrusion detection framework for IIoT in 6G networks using homomorphic encryption and graph neural networks.Scientific reports · 2025Article
- Design of an iterative method for adaptive federated intrusion detection for energy-constrained edge-centric 6G IoT cyber-physical systems.Scientific reports · 2025Article
- A scalable cloud-integrated AI platform for real-time optimization of EV charging and resilient microgrid energy management.Scientific reports · 2025Article
- A novel encrypted traffic detection model based on detachable convolutional GCN-LSTM.Scientific reports · 2025Article
- A comprehensive review of post-harvest agricultural product deterioration signature volatile organic compounds.Food chemistry: X · 2025Review
- KronNet a lightweight Kronecker enhanced feed forward neural network for efficient IoT intrusion detection.Scientific reports · 2025Article
- Advanced cloud intrusion detection framework using graph based features transformers and contrastive learning.Scientific reports · 2025Article
- A lightweight coal mine pedestrian detector for video surveillance systems with multi-level feature fusion and channel pruning.Scientific reports · 2025Article
- Hybrid recurrent with spiking neural network model for enhanced anomaly prediction in IoT networks security.Frontiers in artificial intelligence · 2025Article
- A hybrid approach using support vector machine rule-based system: detecting cyber threats in internet of things.Scientific reports · 2024Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The emerging expanding scope of the Internet of Things (IoT) necessitates robust intrusion detection systems (IDS) to mitigate security risks effectively. However, existing approaches often struggle with adaptability to emerging threats and fail to account for IoT-specific complexities. To address these challenges, this study proposes a novel approach by hybridizing convolutional neural network (CNN) and gated recurrent unit (GRU) architectures tailored for IoT intrusion detection. This hybrid model excels in capturing intricate features and learning relational aspects crucial in IoT security. Moreover, we integrate the feature-weighted synthetic minority oversampling technique (FW-SMOTE) to handle imbalanced datasets, which commonly afflict intrusion detection tasks. Validation using the IoTID20 dataset, designed to emulate IoT environments, yields exceptional results with 99.60% accuracy in attack detection, surpassing existing benchmarks. Additionally, evaluation on the network domain dataset, UNSW-NB15, demonstrates robust performance with 99.16% accuracy, highlighting the model's applicability across diverse datasets. This innovative approach not only addresses current limitations in IoT intrusion detection but also establishes new benchmarks in terms of accuracy and adaptability. The findings underscore its potential as a versatile and effective solution for safeguarding IoT ecosystems against evolving security threats.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.