WhatsThat? On the Usage of Hierarchical Clustering for Unsupervised Detection & Interpretation of Network Attacks
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F20%3A00342465" target="_blank" >RIV/68407700:21230/20:00342465 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1109/EuroSPW51379.2020.00084" target="_blank" >https://doi.org/10.1109/EuroSPW51379.2020.00084</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/EuroSPW51379.2020.00084" target="_blank" >10.1109/EuroSPW51379.2020.00084</a>
Alternative languages
Result language
angličtina
Original language name
WhatsThat? On the Usage of Hierarchical Clustering for Unsupervised Detection & Interpretation of Network Attacks
Original language description
The automatic detection and interpretation of network attacks through machine learning is a well-known problem, for which no general solution is available. Supervised learning and anomaly detection approaches require prior knowledge about the system under analysis, either in terms of normal operation profiles or on the specific attacks to detect. As a consequence, both approaches have clear limitations when it comes to detecting, and in particular interpreting, previously unseen attacks and anomalies. In this paper we present WhatsThat, a novel approach to unsupervised network anomaly detection, which can both detect and interpret anomalous behaviors in a completely black-box manner, without relying on any ground-truth on the system under analysis. WhatsThat relies on hierarchical clustering techniques to discover and characterize anomalous patterns present in nested or hierarchically structured multidimensional data, which is common in network traffic e.g., due to multi-layer protocols. The solution uses unsupervised cluster validity metrics to automatically explore the data structure, and builds on automatic identification of relevant features to provide meaningful descriptions of the detected patterns. We showcase WhatsThat in the detection and interpretation of network attacks hidden in real, large-scale network traffic collected at a transit Internet backbone network. While WhatsThat is mainly tailored for unsupervised anomaly detection and interpretation, it can also be applied to the unsupervised analysis of any kind of nested or hierarchically structured multi-dimensional data, showing the potential of hierarchical clustering for general unsupervised data analytics.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
20202 - Communication engineering and systems
Result continuities
Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2020
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
2020 IEEE European Symposium on Security and Privacy Workshops (EuroS&PW)
ISBN
978-1-7281-8597-2
ISSN
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e-ISSN
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Number of pages
10
Pages from-to
574-583
Publisher name
IEEE
Place of publication
Piscataway (New Jersey)
Event location
online
Event date
Sep 7, 2020
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000630275400073