All

What are you looking for?

All
Projects
Results
Organizations

Quick search

  • Projects supported by TA ČR
  • Excellent projects
  • Projects with the highest public support
  • Current projects

Smart search

  • That is how I find a specific +word
  • That is how I leave the -word out of the results
  • “That is how I can find the whole phrase”

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

The result's identifiers

  • Result code in 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>

  • Result on the web

    <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>

Alternative languages

  • Result language

    angličtina

  • Original language name

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

  • Original language description

    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.

  • Czech name

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

  • OECD FORD branch

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

Result continuities

  • Project

  • Continuities

    S - Specificky vyzkum na vysokych skolach

Others

  • Publication year

    2018

  • Confidentiality

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

Data specific for result type

  • Article name in the collection

    IFIP Advances in Information and Communication Technology. Vol. 519

  • ISBN

    978-3-319-92006-1

  • ISSN

    1868-4238

  • e-ISSN

    neuvedeno

  • Number of pages

    11

  • Pages from-to

    38-48

  • Publisher name

    Springer

  • Place of publication

    Heidelberg

  • Event location

    Rhodos

  • Event date

    May 25, 2018

  • Type of event by nationality

    WRD - Celosvětová akce

  • UT code for WoS article