From CIC-IDS2017 to LYCOS-IDS2017: A corrected dataset for better performance
Arnaud Rosay, Florent Carlier, Eloïse Cheval, Pascal Leroux
Abstract
As connected objects become the standard for quality of life, network intrusion detection is getting more critical than ever. Over the past decades, various datasets have been developed to address this security challenge. Analysis of earlier datasets, such as KDD-Cup99 and NSL-KDD, highlighted some of the issues, leading the way for newer datasets that have corrected the identified problems. CIC-IDS2017, one of the newest network intrusion detection datasets, has become a popular choice. Its advantage is the availability of raw data in PCAP files as well as flow-based features in CSV files.
Topics & Concepts
Computer scienceIntrusion detection systemData miningRaw dataProgramming languageNetwork Security and Intrusion DetectionAdvanced Malware Detection TechniquesInternet Traffic Analysis and Secure E-voting