Identification of Device Dependencies Using Link Prediction

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Identification of Device Dependencies Using Link Prediction

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Identification of Device Dependencies Using Link Prediction

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10200 - Computer and information sciences

Projekt

<a href="/cs/project/EH22_010%2F0003229" target="_blank" >EH22_010/0003229: MSCAfellow5_MUNI</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2024

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

PROCEEDINGS OF 2024 IEEE/IFIP NETWORK OPERATIONS AND MANAGEMENT SYMPOSIUM, NOMS 2024

ISBN

9798350327946

ISSN

1542-1201

e-ISSN

2374-9709

Počet stran výsledku

10

Strana od-do

1-10

Název nakladatele

IEEE Xplore Digital Library

Místo vydání

Seoul, South Korea

Místo konání akce

Seoul, South Korea

Datum konání akce

1. 1. 2024

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

001270140300175