Deep generative models to extend active directory graphs with honeypot users
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F21%3A00351092" target="_blank" >RIV/68407700:21230/21:00351092 - isvavai.cz</a>
Result on the web
<a href="https://www.scitepress.org/Link.aspx?doi=10.5220/0010556601400147" target="_blank" >https://www.scitepress.org/Link.aspx?doi=10.5220/0010556601400147</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.5220/0010556601400147" target="_blank" >10.5220/0010556601400147</a>
Alternative languages
Result language
angličtina
Original language name
Deep generative models to extend active directory graphs with honeypot users
Original language description
Active Directory (AD) is a crucial element of large organizations, given its central role in managing access to resources. Since AD is used by all users in the organization, it is hard to detect attackers. We propose to generate and place fake users (honeyusers) in AD structures to help detect attacks. However, not any honeyuser will attract attackers. Our method generates honeyusers with a Variational Autoencoder that enriches the AD structure with well-positioned honeyusers. It first learns the embeddings of the original nodes and edges in the AD, then it uses a modified Bidirectional DAG-RNN to encode the parameters of the probability distribution of the latent space of node representations. Finally, it samples nodes from this distribution and uses an MLP to decide where the nodes are connected. The model was evaluated by the similarity of the generated AD with the original, by the positions of the new nodes, by the similarity with GraphRNN and finally by making real intruders attack the generated AD structure to see if they select the honeyusers. Results show that our machine learning model is good enough to generate well-placed honeyusers for existing AD structures so that intruders are lured into them.
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
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Proceedings of the 2nd International Conference on Deep Learning Theory and Applications - DeLTA
ISBN
978-989-758-526-5
ISSN
2184-9277
e-ISSN
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Number of pages
8
Pages from-to
140-147
Publisher name
SciTePress - Science and Technology Publications
Place of publication
Porto
Event location
Online streaming
Event date
Jul 7, 2021
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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