ArticleSensors (Basel, Switzerland)2022
Transfer-Learning-Based Intrusion Detection Framework in IoT Networks.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Cross-Domain Deep Transfer Learning Framework for Intrusion Detection in Data-Constrained and Resource-Limited IoT Environments.Sensors (Basel, Switzerland) · 2026Article
- Application of representation learning in detecting botnet attacks.Scientific reports · 2026Article
- Cross-Protocol Domain Gap in Internet of Things Intrusion and Anomaly Detection: An Empirical Internet Protocol-to-Bluetooth Low Energy Study of Domain-Adversarial Training.Sensors (Basel, Switzerland) · 2026Article
- Federated transfer learning for rare attack class detection in network intrusion detection systems.Scientific reports · 2025Article
- Signature-based intrusion detection using machine learning and deep learning approaches empowered with fuzzy clustering.Scientific reports · 2025Article
- A Scalable Approach to Internet of Things and Industrial Internet of Things Security: Evaluating Adaptive Self-Adjusting Memory K-Nearest Neighbor for Zero-Day Attack Detection.Sensors (Basel, Switzerland) · 2025Article
- A lightweight model design approach for few-shot malicious traffic classification.Scientific reports · 2024Article
- Logistic Regression Ensemble Classifier for Intrusion Detection System in Internet of Things.Sensors (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Cyberattacks in the Internet of Things (IoT) are growing exponentially, especially zero-day attacks mostly driven by security weaknesses on IoT networks. Traditional intrusion detection systems (IDSs) adopted machine learning (ML), especially deep Learning (DL), to improve the detection of cyberattacks. DL-based IDSs require balanced datasets with large amounts of labeled data; however, there is a lack of such large collections in IoT networks. This paper proposes an efficient intrusion detection framework based on transfer learning (TL), knowledge transfer, and model refinement, for the effective detection of zero-day attacks. The framework is tailored to 5G IoT scenarios with unbalanced and scarce labeled datasets. The TL model is based on convolutional neural networks (CNNs). The framework was evaluated to detect a wide range of zero-day attacks. To this end, three specialized datasets were created. Experimental results show that the proposed TL-based framework achieves high accuracy and low false prediction rate (
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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.