Identification of Device Dependencies Using Link Prediction
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F24%3A00135430" target="_blank" >RIV/00216224:14330/24:00135430 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1109/NOMS59830.2024.10575713" target="_blank" >http://dx.doi.org/10.1109/NOMS59830.2024.10575713</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/NOMS59830.2024.10575713" target="_blank" >10.1109/NOMS59830.2024.10575713</a>
Alternative languages
Result language
angličtina
Original language name
Identification of Device Dependencies Using Link Prediction
Original language description
Devices in computer networks cannot work without essential network services provided by a limited count of devices. Identification of device dependencies determines whether a pair of IP addresses is a dependency, i.e., the host with the first IP address is dependent on the second one. These dependencies cannot be identified manually in large and dynamically changing networks. Nevertheless, they are important due to possible unexpected failures, performance issues, and cascading effects. We address the identification of dependencies using a new approach based on graph-based machine learning. The approach belongs to link prediction based on a latent representation of the computer network’s communication graph. It samples random walks over IP addresses that fulfill time conditions imposed on network dependencies. The constrained random walks are used by a neural network to construct IP address embedding, which is a space that contains IP addresses that often appear close together in the same communication chain (i.e., random walk). Dependency embedding is constructed by combining values for IP addresses from their embedding and used for training the resulting dependency classifier. We evaluated the approach using IP flow datasets from a controlled environment and university campus network that contain evidence about dependencies. Evaluation concerning the correctness and relationship to other approaches shows that the approach achieves acceptable performance. It can simultaneously consider all types of dependencies and is applicable for batch processing in operational conditions.
Czech name
—
Czech description
—
Classification
Type
D - Article in proceedings
CEP classification
—
OECD FORD branch
10200 - Computer and information sciences
Result continuities
Project
<a href="/en/project/EH22_010%2F0003229" target="_blank" >EH22_010/0003229: MSCAfellow5_MUNI</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach
Others
Publication year
2024
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 2024 IEEE/IFIP NETWORK OPERATIONS AND MANAGEMENT SYMPOSIUM, NOMS 2024
ISBN
9798350327946
ISSN
1542-1201
e-ISSN
2374-9709
Number of pages
10
Pages from-to
1-10
Publisher name
IEEE Xplore Digital Library
Place of publication
Seoul, South Korea
Event location
Seoul, South Korea
Event date
Jan 1, 2024
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
001270140300175