Springer Nature OA
open access
CC BY-NC-ND 4.0
Confidence-driven lightweight adaptive online deep learning for IoT intrusion detection
The Internet of Things (IoT) paradigm has become a technology used in homes, industry, agriculture, energy, and healthcare. Because of this technology’s widespread adoption and the weak security architecture of most IoT devices, the ecosystem has become an attractive target for cyberattacks. One mit
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- A Comparative Study of Anomaly Detection Techniques for IoT Security Using Adaptive Machine Learning for IoT Threats#133959
- A Shark Inspired Ensemble Deep Learning Stacks for Ensuring the Security in Internet of Things (IoT)-Based Smart City Infrastructure#20529
- IoT Network Cybersecurity Assessment With the Associated Random Neural Network#672094
- Multi-Layer Intrusion Detection Framework for IoT Systems Using Ensemble Machine Learning#81330
- Ensemble learning based anomaly detection for IoT cybersecurity via Bayesian hyperparameters sensitivity analysis#671990
Record · ID 1169166
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive