Adaptive and Reinforcement Learning Approaches for Online Network Monitoring and Analysis

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F21%3A00346218" target="_blank" >RIV/68407700:21230/21:00346218 - isvavai.cz</a>

Result on the web

<a href="https://doi.org/10.1109/TNSM.2020.3037486" target="_blank" >https://doi.org/10.1109/TNSM.2020.3037486</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/TNSM.2020.3037486" target="_blank" >10.1109/TNSM.2020.3037486</a>

Result language

angličtina

Original language name

Adaptive and Reinforcement Learning Approaches for Online Network Monitoring and Analysis

Original language description

Network-monitoring data commonly arrives in the form of fast and changing data streams. Continuous and dynamic learning is an effective learning strategy when dealing with such data, where concept drifts constantly occur. We propose different stream-based, adaptive learning approaches to analyze network-traffic streams on the fly. We address two major challenges associated to stream-based machine learning and online network monitoring: (i) how to dynamically learn from and adapt to non-stationary data changing over time, and (ii) how to deal with the limited availability of labeled data to continuous lytune a supervised-learning model. We introduce ADAM & RAL,two stream-based machine-learning techniques to tackle these challenges. ADAM relies on adaptive memory strategies to dynamically tune stream-based learning models to changes in the input data distribution. RAL combines reinforcement learning with stream-based active-learning to reduce the amount of labeled data needed for continual learning, dynamically deciding on the most informative samples to learn from. We apply ADAM & RAL to the real-time detection of network attacks in Internet network traffic, and show that it is possible to continuously achieve high detection accuracy even under the occurrence of concept drifts,limiting the amount of labeled data needed for learning.

Czech name

—

Czech description

—

Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

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

Project

—

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2021

Confidentiality

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

Name of the periodical

IEEE Transactions on Network and Service Management

ISSN

1932-4537

e-ISSN

1932-4537

Volume of the periodical

18

Issue of the periodical within the volume

2

Country of publishing house

US - UNITED STATES

Number of pages

18

Pages from-to

1832-1849

UT code for WoS article

000660636700051

EID of the result in the Scopus database

2-s2.0-85096365931