• DocumentCode
    260222
  • Title

    A cognitive approach for botnet detection using Artificial Immune System in the cloud

  • Author

    Kebande, Victor R. ; Venter, H.S.

  • Author_Institution
    Dept. of Comput. Sci., Univ. of Pretoria, Tshwane, South Africa
  • fYear
    2014
  • fDate
    April 29 2014-May 1 2014
  • Firstpage
    52
  • Lastpage
    57
  • Abstract
    The advent of cloud computing has given a provision for both good and malicious opportunities. Virtualization itself as a component of Cloud computing, has provided users with an immediate way of accessing limitless resource infrastructures. Botnets have evolved to be the most dangerous group of remote-operated zombie computers given the open cloud environment. They happen to be the dark side of computing due to the ability to run illegal activities through remote installations, attacks and propagations through exploiting vulnerabilities. The problem that this paper addresses is that botnet technology is advancing each day and detection in the cloud is becoming hard. In this paper, therefore, the authors´ presents an approach for detecting an infection of a robot network in the cloud environment. The authors proposed a detection mechanism using Artificial Immune System (AIS). The results show that this research is significant.
  • Keywords
    artificial immune systems; cloud computing; invasive software; virtualisation; AIS; artificial immune system; botnet detection; cloud computing; cognitive approach; directed graph network; resource infrastructure access; virtualization; Cloud computing; Computers; Detectors; Immune system; Monitoring; Pattern matching; Artificial immune system; Botnet; Cloud; Detection; Negative selection;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Cyber Security, Cyber Warfare and Digital Forensic (CyberSec), 2014 Third International Conference on
  • Conference_Location
    Beirut
  • Print_ISBN
    978-1-4799-3905-3
  • Type

    conf

  • DOI
    10.1109/CyberSec.2014.6913971
  • Filename
    6913971