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Intrusion Detection Dataset (BCCC-CIC-IDS2017)

Using NLFlowLyzer, we successfully generated the “BCCC-CIC-IDS2017” dataset by extracting key flows from raw network traffic data of CIC-IDS2017, resulting in CSV files integrating essential network and transport layer features. This new dataset offers a structured approach for analyzing intrusion detection, combining diverse traffic types into multiple sub-categories. The “BCCC-CIC-IDS2017” dataset enriches the depth and variety needed to rigorously evaluate our proposed profiling model, advancing research in network security and enhancing the development of intrusion detection systems.

The full research paper outlining the details of the dataset and its underlying principles:

"NTLFlowLyzer: Toward Generating an Intrusion Detection Dataset and Intruders Behavior Profiling through Network Layer Traffic Analysis and Pattern Extraction", MohammadMoein Shafi, Arash Habibi Lashkari, Arousha Haghighian Roudsari, Computer & Security, Computers & Security, 104160, ISSN 0167-4048 (2024)"

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