Spam filtering in social networks using regularized deep neural networks with ensemble learning

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216275%3A25410%2F18%3A39913513" target="_blank" >RIV/00216275:25410/18:39913513 - isvavai.cz</a>

Výsledek na webu

<a href="https://link.springer.com/chapter/10.1007/978-3-319-92007-8_4" target="_blank" >https://link.springer.com/chapter/10.1007/978-3-319-92007-8_4</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-319-92007-8_4" target="_blank" >10.1007/978-3-319-92007-8_4</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Spam filtering in social networks using regularized deep neural networks with ensemble learning

Popis výsledku v původním jazyce

Spam filtering in social networks is increasingly important owing to the rapid growth of social network user base. Sophisticated spam filters must be developed to deal with this complex problem. Traditional machine learning approaches such as neural networks, support vector machine and Naïve Bayes classifiers are not effective enough to process and utilize complex features present in high-dimensional data on social network spam. To overcome this problem, here we propose a novel approach to social network spam filtering. The approach uses ensemble learning techniques with regularized deep neural networks as base learners. We demonstrate that this approach is effective for social network spam filtering on a benchmark dataset in terms of accuracy and area under ROC. In addition, solid performance is achieved in terms of false negative and false positive rates. We also show that the proposed approach outperforms other popular algorithms used in spam filtering, such as decision trees, Naïve Bayes, artificial immune systems, support vector machines, etc.

Název v anglickém jazyce

Spam filtering in social networks using regularized deep neural networks with ensemble learning

Popis výsledku anglicky

Spam filtering in social networks is increasingly important owing to the rapid growth of social network user base. Sophisticated spam filters must be developed to deal with this complex problem. Traditional machine learning approaches such as neural networks, support vector machine and Naïve Bayes classifiers are not effective enough to process and utilize complex features present in high-dimensional data on social network spam. To overcome this problem, here we propose a novel approach to social network spam filtering. The approach uses ensemble learning techniques with regularized deep neural networks as base learners. We demonstrate that this approach is effective for social network spam filtering on a benchmark dataset in terms of accuracy and area under ROC. In addition, solid performance is achieved in terms of false negative and false positive rates. We also show that the proposed approach outperforms other popular algorithms used in spam filtering, such as decision trees, Naïve Bayes, artificial immune systems, support vector machines, etc.

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

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2018

Kód důvěrnosti údajů

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

Název statě ve sborníku

IFIP Advances in Information and Communication Technology. Vol. 519

ISBN

978-3-319-92006-1

ISSN

1868-4238

e-ISSN

neuvedeno

Počet stran výsledku

11

Strana od-do

38-48

Název nakladatele

Springer

Místo vydání

Heidelberg

Místo konání akce

Rhodos

Datum konání akce

25. 5. 2018

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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