OpenAlex
metadata only
CC BY-NC-ND 4.0
“Why Should I Trust Your IDS?”: An Explainable Deep Learning Framework for Intrusion Detection Systems in Internet of Things Networks
artificial-intelligenceartificial-neural-networkcomputer-sciencecomputer-securityinternet-of-thingsset-abstract-data-type
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
- Machine Learning-Based Intrusion Detection Framework for Detecting Security Attacks in Internet of Things#91748
- Anomaly Detection IDS for Detecting DoS Attacks in IoT Networks Based on Machine Learning Algorithms#133993
- Explainable artificial intelligence for intrusion detection in IoT networks: A deep learning based approach#200902
- ARFU-IDS: Robust Federated Unlearning for Transformer-Based Intrusion Detection in IoT and Sensor Networks.#1091521
- Explainable Hybrid Feature Selection for Intrusion Detection in Internet of Medical Things Environments#423877
Record · ID 199729
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