Springer Nature OA
open access
CC BY-NC-ND 4.0
Hybrid metaheuristic-optimized deep learning framework for intelligent cyber intrusion detection
computer-sciencedata-mining-and-knowledge-discoverynatural-language-processing-nlppattern-recognition
Cyber intrusions and malicious network activities have emerged as a serious concern due to the rapid growth of digital communication and networking technologies. Detecting cyber intrusions remains a challenging task because of the complex and high-dimensional characteristics of modern network traffi
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
- Features Dimensionality Reduction Approaches for Machine Learning Based Network Intrusion Detection#199680
- nCMD: Benign-Anchored Feature Selection for Imbalanced Network Intrusion Detection#1117462
- An optimized gradient boosting framework for IoT intrusion detection: a comprehensive evaluation on the CICIoT2023 dataset.#258279
- End - to - End Deep Learning for Detecting Web Attacks#87798
- Images and Annotations for "Evaluating AI-Assisted Pollinator Detection in Agricultural Fields"#1110945
Record · ID 824314
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