DigitalCommons@Kennesaw State University
metadata only
Advancing Cybersecurity Practice: Explainable Machine Learning for Network Intrusion Detection
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 for Intelligent Data Analysis and Automation in Cybersecurity: Current and Future Prospects#1108078
- Overview on Intrusion Detection Systems Design Exploiting Machine Learning for Networking Cybersecurity#671637
- Adaptive class-aware feature selection for high-dimensional and imbalanced multi-class network intrusion detection.#351636
- Detecting adversarial evasion in deep learning intrusion detection systems using explainable AI#372662
- Adversarial attacks on machine learning cybersecurity defences in Industrial Control Systems#1091973
Record · ID 311317
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