• DocumentCode
    270162
  • Title

    3-Tier hybrid approach for SMS filtering

  • Author

    Kiliç, Esma ; Arslan, Sumeyra Nur ; Guvensan, M.A.

  • Author_Institution
    Bilgisayar Muhendisligi Bolumu, Yildiz Teknik Univ., Istanbul, Turkey
  • fYear
    2014
  • fDate
    23-25 April 2014
  • Firstpage
    1950
  • Lastpage
    1953
  • Abstract
    Short Message Service is one of the most using services in mobile phones. In daily life, several spam messages, which could disturb mobile phone users, are received frequently. Unwanted messages could be delivered for advertisement, announcement of promotional events and/or only for disturbing people. In this study, 3-tier hybrid message filtering architecture has been introduced to protect the mobile users from spam messages. In the first two steps, incoming messages are classified based on the white /black lists and are identified as “legitimate SMS” and “spam SMS”. If the number of incoming message is not in those lists, the message is examined according to their meaning and morphological features. The success rate of k-Nearest Neighbor and Naive Bayes algorithms is about 96%. The proposed architecture has been implemented on Android platform.
  • Keywords
    Bayes methods; electronic messaging; information filtering; smart phones; unsolicited e-mail; 3-tier hybrid message filtering architecture; Android platform; Naive Bayes algorithms; SMS filtering; black lists; k-Nearest Neighbor algorithms; legitimate SMS; mobile phone users; morphological features; short message service; spam SMS; spam messages; unwanted messages; white lists; Androids; Conferences; Filtering; Humanoid robots; Signal processing; Smart phones; Unsolicited electronic mail; Detection of Spam SMS; Hybrid Approach; Machine Learning; SMS Filtering; Smartphones;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Signal Processing and Communications Applications Conference (SIU), 2014 22nd
  • Conference_Location
    Trabzon
  • Type

    conf

  • DOI
    10.1109/SIU.2014.6830638
  • Filename
    6830638