Statistical analysis of honeypot data and building of Kyoto 2006+ dataset for NIDS evaluation

By Jungsuk Song, H. Takakura, Y. Okabe, Masashi Eto, D. Inoue, K. Nakao. International Workshop on Building Analysis Datasets and Gathering Experience Returns for Security, 2011

329 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications

View Paper: https://www.semanticscholar.org/paper/b7ebe844f02fe60c489bc7e199164bd3a68d1a73

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Citations in the corpus

Deep Learning Approach for Intelligent Intrusion Detection System (2019, methodology): “Moreover, the dataset doesn’t contain false positives that help to minimize the number of alerts to the network admin [58].”
Building an Intrusion Detection System Using a Filter-Based Feature Selection Algorithm (2016, methodology): “A dimensionality reduction method proposed in [25] was to AMBUSAIDI ETAL.”
Toward Generating a New Intrusion Detection Dataset and Intrusion Traffic Characterization (2018, background): “The normal traffic here has been simulated repeatedly during the attacks and producing only DNS and mail traffic data, which is not reflected in real world normal network traffic, so there are no false positives, which are important for minimizing the number of alerts (Song et al., 2011) (M.”
Adversarial Machine Learning for Network Intrusion Detection Systems: A Comprehensive Survey (2023, methodology): “used datasets for general networks include NSL-KDD [56], Kyoto 2006+ [57], ISCXIDS2012 [58], CTU-13 [59],”