• DocumentCode
    1696137
  • Title

    Framework for cloud intrusion detection system service

  • Author

    Aljurayban, Nouf Saleh ; Emam, Ahmed

  • Author_Institution
    Inf. Syst. Dept., King Saud Univ., Riyadh, Saudi Arabia
  • fYear
    2015
  • Firstpage
    1
  • Lastpage
    5
  • Abstract
    In this Internet era, the use of cloud computing is causing a massive volume of online financial transactions, and the exchange of personal and sensitive information over the internet. Attackers use many different types of malware in searches motivated by curiosity or financial gain. In this paper, we propose an efficient framework called the Layered Intrusion Detection Framework (LIDF) that can be applied on the different layers of cloud computing in order to identify the presence of normal traffic among the monitored cloud traffic. The proposed framework uses data mining, especially an Artificial Neural Network, which makes it accurate, fast, and scalable. At the same time, the LIDF can reduce the rate of the analyzed traffic and achieve better performance by increasing the throughput without affecting its main goal.
  • Keywords
    cloud computing; data mining; invasive software; neural nets; telecommunication traffic; ANN; Internet era; LIDF; artificial neural network; cloud computing; cloud intrusion detection system service; cloud traffic; data mining; layered intrusion detection framework; malware; normal traffic; online financial transactions; Artificial neural networks; Cloud computing; Computational modeling; Intrusion detection; Monitoring; Training; Artificial Neural Network; Data Mining; Intrusion Detection; cloud computing;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Web Applications and Networking (WSWAN), 2015 2nd World Symposium on
  • Conference_Location
    Sousse
  • Print_ISBN
    978-1-4799-8171-7
  • Type

    conf

  • DOI
    10.1109/WSWAN.2015.7210298
  • Filename
    7210298