• DocumentCode
    259696
  • Title

    An Intelligent Technique for Detecting Malicious Users on Mobile Stores

  • Author

    Terzi, Ramazan ; Yavanoglu, Uraz ; Sinanc, Duygu ; Oguz, Dogac ; Cakir, Semra

  • Author_Institution
    Dept. of Comput. Eng., Gazi Univ., Ankara, Turkey
  • fYear
    2014
  • fDate
    3-6 Dec. 2014
  • Firstpage
    470
  • Lastpage
    477
  • Abstract
    In this study, malicious users who cause to resource exhausting are tried to detect in a telecommunication company network. Non-Legitimate users could cause lack of information availability and need countermeasures to prevent threat or limit permissions on the system. For this purpose, ANN based intelligent system is proposed and compared to SVM which is well known classification technique. According to results, proposed technique has achieved approximately 70% general success rate, 33% false positive rate and 27% false negative rate in controlled environment. Also ANN has high ability to work compare to SVM for our dataset. As a result proposed technique and developed application shows sufficient and acceptable defense mechanism in huge company networks. We discussed about this is initial study and ongoing research which is compared to the current literature. By the way, this study also shows that non security information such as users mobile experiences could be potential usage to prevent resource exhausting also known as DoS related attacks.
  • Keywords
    computer network security; mobile computing; neural nets; support vector machines; ANN; DoS related attacks; SVM; artificial neural network; company networks; false negative rate; false positive rate; general success rate; information availability; intelligent technique; malicious user detection; mobile stores; resource exhaustion; Artificial neural networks; Computer crime; Data mining; Data models; Entropy; Floods; DoS attack; artificial neural network (ANN); mobile store security; resource exhausting;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Machine Learning and Applications (ICMLA), 2014 13th International Conference on
  • Conference_Location
    Detroit, MI
  • Type

    conf

  • DOI
    10.1109/ICMLA.2014.82
  • Filename
    7033161