ASNM datasets include records consisting of many features, that express various properties and characteristics of TCP communications. These features are called Advanced Security Network Metrics (ASNM) and were designed with the intention to discern legitimate and malicious connections (especially intrusions).

Dataset Files

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Documentation: 
[1] Ivan Homoliak, Petr Hanacek, "ASNM Datasets: A Collection of Network Traffic Data for Testing of Adversarial Classifiers and Network Intrusion Detectors", IEEE Dataport, 2019. [Online]. Available: http://dx.doi.org/10.21227/081t-dj23. Accessed: Apr. 13, 2024.
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doi = {10.21227/081t-dj23},
url = {http://dx.doi.org/10.21227/081t-dj23},
author = {Ivan Homoliak; Petr Hanacek },
publisher = {IEEE Dataport},
title = {ASNM Datasets: A Collection of Network Traffic Data for Testing of Adversarial Classifiers and Network Intrusion Detectors},
year = {2019} }
TY - DATA
T1 - ASNM Datasets: A Collection of Network Traffic Data for Testing of Adversarial Classifiers and Network Intrusion Detectors
AU - Ivan Homoliak; Petr Hanacek
PY - 2019
PB - IEEE Dataport
UR - 10.21227/081t-dj23
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Ivan Homoliak, Petr Hanacek. (2019). ASNM Datasets: A Collection of Network Traffic Data for Testing of Adversarial Classifiers and Network Intrusion Detectors. IEEE Dataport. http://dx.doi.org/10.21227/081t-dj23
Ivan Homoliak, Petr Hanacek, 2019. ASNM Datasets: A Collection of Network Traffic Data for Testing of Adversarial Classifiers and Network Intrusion Detectors. Available at: http://dx.doi.org/10.21227/081t-dj23.
Ivan Homoliak, Petr Hanacek. (2019). "ASNM Datasets: A Collection of Network Traffic Data for Testing of Adversarial Classifiers and Network Intrusion Detectors." Web.
1. Ivan Homoliak, Petr Hanacek. ASNM Datasets: A Collection of Network Traffic Data for Testing of Adversarial Classifiers and Network Intrusion Detectors [Internet]. IEEE Dataport; 2019. Available from : http://dx.doi.org/10.21227/081t-dj23
Ivan Homoliak, Petr Hanacek. "ASNM Datasets: A Collection of Network Traffic Data for Testing of Adversarial Classifiers and Network Intrusion Detectors." doi: 10.21227/081t-dj23