arXiv (All)
open access
BARS: Benign-Anchored Ranking and Selection for False Alarm Reduction in Network Intrusion Detection
False alarms remain a major barrier to deploying network intrusion detection systems (NIDS). In high-volume environments, even a sub-1% false positive rate can generate tens of thousands of daily alerts. Filter-based feature selection is attractive because it operates upstream of the classifier and
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 Phishing detection model based on dynamic-hybrid feature selection#957119
- A resource efficient IoT intrusion detection model using hybrid feature selection for edge computing.#437241
- A resource efficient IoT intrusion detection model using hybrid feature selection for edge computing#431827
- Hybrid feature selection for IoMT based intrusion detection system for integrating mutual information filtering with deep learning based accelerated metaheuristic optimization.#227814
- LiteShield: Hybrid Feature Selection-Driven Lightweight Intrusion Detection for Resource-Constrained IoT Networks#157326
Record · ID 1117513
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