Stream4Flow: Real-time IP Flow Host Monitoring using Apache Spark

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14610%2F18%3A00106894" target="_blank" >RIV/00216224:14610/18:00106894 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1109/NOMS.2018.8406132" target="_blank" >http://dx.doi.org/10.1109/NOMS.2018.8406132</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/NOMS.2018.8406132" target="_blank" >10.1109/NOMS.2018.8406132</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Stream4Flow: Real-time IP Flow Host Monitoring using Apache Spark

Popis výsledku v původním jazyce

In this paper, we present Stream4Flow, a framework for cyber situational awareness based on Apache Spark Streaming. We demonstrate utilization of Stream4Flow for real-time IP flow host monitoring in a large campus network. Contemporary IP flow analysis systems are not designed for the continuous host monitoring. Gaining the detailed overview of each host is not straightforward with these systems due to connection-based paradigm and performance challenges. We show that distributed stream processing is a natural solution for detailed IP flow host monitoring. Moreover, we describe a real-time host monitoring workflow in data streams in detail and present advantages of flow-based host monitoring in Apache Spark including real-time host profiling, dynamic level of detail and granularity.

Název v anglickém jazyce

Stream4Flow: Real-time IP Flow Host Monitoring using Apache Spark

Popis výsledku anglicky

In this paper, we present Stream4Flow, a framework for cyber situational awareness based on Apache Spark Streaming. We demonstrate utilization of Stream4Flow for real-time IP flow host monitoring in a large campus network. Contemporary IP flow analysis systems are not designed for the continuous host monitoring. Gaining the detailed overview of each host is not straightforward with these systems due to connection-based paradigm and performance challenges. We show that distributed stream processing is a natural solution for detailed IP flow host monitoring. Moreover, we describe a real-time host monitoring workflow in data streams in detail and present advantages of flow-based host monitoring in Apache Spark including real-time host profiling, dynamic level of detail and granularity.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

<a href="/cs/project/VI20162019014" target="_blank" >VI20162019014: Simulace, detekce a potlačení kybernetických hrozeb ohrožujících kritickou infrastrukturu</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2018

Kód důvěrnosti údajů

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

Název statě ve sborníku

NOMS 2018 - 2018 IEEE/IFIP Network Operations and Management Symposium

ISBN

9781538634165

ISSN

—

e-ISSN

—

Počet stran výsledku

2

Strana od-do

1-2

Název nakladatele

IEEE

Místo vydání

Taipei, Taiwan

Místo konání akce

Taipei, Taiwan

Datum konání akce

1. 1. 2018

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

—