Continuous and Adaptive Learning over Big Streaming Data for Network Security

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F19%3A00338618" target="_blank" >RIV/68407700:21230/19:00338618 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1109/CloudNet47604.2019.9064134" target="_blank" >http://dx.doi.org/10.1109/CloudNet47604.2019.9064134</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/CloudNet47604.2019.9064134" target="_blank" >10.1109/CloudNet47604.2019.9064134</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Continuous and Adaptive Learning over Big Streaming Data for Network Security

Popis výsledku v původním jazyce

Continuous and adaptive learning is an effective learning approach when dealing with highly dynamic and changing scenarios, where concept drift often happens. In a continuous, stream or adaptive learning setup, new measurements arrive continuously and there are no boundaries for learning, meaning that the learning model has to decide how and when to (re)learn from these new data constantly. We address the problem of adaptive and continual learning for network security, building dynamic models to detect network attacks in real network traffic. The combination of fast and big network measurements data with the re-training paradigm of adaptive learning imposes complex challenges in terms of data processing speed, which we tackle by relying on big data platforms for parallel stream processing. We build and benchmark different adaptive learning models on top of a novel big data analytics platform for network traffic monitoring and analysis tasks, and show that high speed-up computations (as high as x 6) can be achieved by parallelizing off-the-shelf stream learning approaches.

Název v anglickém jazyce

Continuous and Adaptive Learning over Big Streaming Data for Network Security

Popis výsledku anglicky

Continuous and adaptive learning is an effective learning approach when dealing with highly dynamic and changing scenarios, where concept drift often happens. In a continuous, stream or adaptive learning setup, new measurements arrive continuously and there are no boundaries for learning, meaning that the learning model has to decide how and when to (re)learn from these new data constantly. We address the problem of adaptive and continual learning for network security, building dynamic models to detect network attacks in real network traffic. The combination of fast and big network measurements data with the re-training paradigm of adaptive learning imposes complex challenges in terms of data processing speed, which we tackle by relying on big data platforms for parallel stream processing. We build and benchmark different adaptive learning models on top of a novel big data analytics platform for network traffic monitoring and analysis tasks, and show that high speed-up computations (as high as x 6) can be achieved by parallelizing off-the-shelf stream learning approaches.

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

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2019

Kód důvěrnosti údajů

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

Název statě ve sborníku

IEEE International Conference on Cloud Networking

ISBN

978-1-7281-4832-8

ISSN

—

e-ISSN

—

Počet stran výsledku

4

Strana od-do

—

Název nakladatele

IEEE

Místo vydání

Santa Monica

Místo konání akce

Coimbra

Datum konání akce

4. 11. 2019

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000574777100031