Springer Nature OA
open access
CC BY-NC-ND 4.0
A novel deep learning and FAOA-based DXCov-FH net for intrusion detection in cloud computing using MapReduce framework
Intrusion Detection in Cloud Computing focuses on analyzing large amount of log data and identifying security threats. Existing approaches struggle with false-positive reduction, scalability, real-time monitoring, and adapting to new attack techniques. To address these issues, a novel method, named
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
- Research trends in deep learning and machine learning for cloud computing security#339532
- HDCI Net for interpretable cross domain intrusion detection in 6G IoT and critical infrastructure networks.#327123
- HDCI Net for interpretable cross domain intrusion detection in 6G IoT and critical infrastructure networks#142737
- Beyond The Cloud: Mastering Security in the Digital Age#77053
- The Role of Learning in Attacking ML-based Network Intrusion Detection#1064890
Record · ID 1082022
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