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In this paper, we present three datasets that have been built from networktraffic traces using ASNM features, designed in our previous work. The firstdataset was built using a state-of-the-art dataset called CDX 2009, while theremaining two datasets were collected by us in 2015 and 2018, respectively.These two datasets contain several adversarial obfuscation techniques that wereapplied onto malicious as well as legitimate traffic samples during theexecution of particular TCP network connections. Adversarial obfuscationtechniques were used for evading machine learning-based network intrusiondetection classifiers. Further, we showed that the performance of suchclassifiers can be improved when partially augmenting their training data bysamples obtained from obfuscation techniques. In detail, we utilized tunnelingobfuscation in HTTP(S) protocol and non-payload-based obfuscations modifyingvarious properties of network traffic by, e.g., TCP segmentation,re-transmissions, corrupting and reordering of packets, etc. To the best of ourknowledge, this is the first collection of network traffic metadata thatcontains adversarial techniques and is intended for non-payload-based networkintrusion detection and adversarial classification. Provided datasets enabletesting of the evasion resistance of arbitrary classifier that is using ASNMfeatures.

ASNM Datasets: A Collection of Network Attacks for Testing of Adversarial Classifiers and Intrusion Detectors