Biblio-US17: A labeled real URL dataset for anomaly-based intrusion detection systems development
Author:
Affiliation:
1. Dpt. Signal Theory, Telematics and Communications - CITIC, University of Granada, Spain
2. Dpt. of Telematics Engineering, University of Seville, Spain
Funder
MICIN/AEI/10.13039/501100011033
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/3655693.3661319
Reference7 articles.
1. A Survey on Network Security Monitoring Systems
2. Outside the Closed World: On Using Machine Learning for Network Intrusion Detection
3. Toward Credible Evaluation of Anomaly-Based Intrusion-Detection Methods
4. [4] J.E. Díaz-Verdejo R. Estepa Alonso A. Estepa Alonso G. Madinabeitia. 2023. A Critical Review of the Techniques used for Anomaly Detection of HTTP-based Attacks: Taxonomy Limitations and Open Challenges. Computers & Security 124(2023) 102997.
5. A survey of network-based intrusion detection data sets
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