NCBI PubMed Central
open access
Enhancing anomaly-based zero-day attack detection framework using CNN-driven feature extraction and OC-SVM.
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 Cross-Dataset based Zero-Day Intrusion Detection System by Integrating Siamese Network and Reinforcement Learning#1062018
- PRA-TAM: prototype-regularised residual affinity maximisation for unsupervised graph anomaly detection#425890
- UNAD+: An Explainable Hybrid Framework for Unknown Network Attack Detection#216704
- Anomaly Detection IDS for Detecting DoS Attacks in IoT Networks Based on Machine Learning Algorithms#133993
- An Adversarial Zero-Shot Learning Approach for Anomaly Detection in Multivariate IoT Traffic Data#639631
Record · ID 631150
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