Springer Nature OA
open access
CC BY-NC-ND 4.0
Optimizing cyberattack frequency forecasting by advanced machine learning adaptation with tumbling windows
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 for Achieving Cybersecurity in Smart Grids Using IEC 61850 GOOSE Messages#671809
- Adversarial Machine Learning: Difficulties in Applying Machine Learning to Existing Cybersecurity Systems#626256
- Adversarial Machine Learning: Difficulties in Applying Machine Learning to Existing Cybersecurity Systems#199298
- A New Hybrid Machine Learning for Cybersecurity Threat Detection Based on Adaptive Boosting#431446
- A comprehensive review of machine learning applications in cybersecurity: identifying gaps and advocating for cybersecurity auditing#225375
Record · ID 331719
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