Publication detail

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

HOMOLIAK, I. MALINKA, K. HANÁČEK, P.

Original Title

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

Type

journal article in Web of Science

Language

English

Original Abstract

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

Keywords

   - Dataset,    - network intrusion detection,    - adversarial classification,    - evasions,    - ASNM features,    - buffer overflow,    - non-payload-based obfuscations,    - tunneling obfuscations

Authors

HOMOLIAK, I.; MALINKA, K.; HANÁČEK, P.

Released

11. 6. 2020

ISBN

2169-3536

Periodical

IEEE Access

Year of study

8

Number

6

State

United States of America

Pages from

112427

Pages to

112453

Pages count

27

URL

https://ieeexplore.ieee.org/document/9115004

BibTex

@article{BUT162288,
  author="Ivan {Homoliak} and Kamil {Malinka} and Petr {Hanáček}",
  title="ASNM Datasets: A Collection of Network Attacks for Testing of Adversarial Classifiers and Intrusion Detectors",
  journal="IEEE Access",
  year="2020",
  volume="8",
  number="6",
  pages="112427--112453",
  doi="10.1109/ACCESS.2020.3001768",
  issn="2169-3536",
  url="https://ieeexplore.ieee.org/document/9115004"
}