arXiv (All)
open access
Explainability-Aware Evaluation of Transfer Learning Models for IoT DDoS Detection Under Resource Constraints
Distributed denial of service (DDoS) attacks threaten the availability of Internet of Things (IoT) infrastructures, particularly under resource-constrained deployment conditions. Although transfer learning models have shown promising detection accuracy, their reliability, computational feasibility,
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
- Anomaly Detection IDS for Detecting DoS Attacks in IoT Networks Based on Machine Learning Algorithms#133993
- Beyond Detection Accuracy: Measuring Explanation Cost, Stability, and Utility for Resource-Aware IoT Intrusion Detection#444578
- Binary Anomaly Detection in Streaming IoT Traffic under Concept Drift#974252
- Enhancing intrusion detection in IoT networks using machine learning-based feature selection and ensemble models#134011
- A stacking ensemble of deep learning models for IoT intrusion detection#200935
Record · ID 1490454
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