• DocumentCode
    2413206
  • Title

    Backdoor Detection System Using Artificial Neural Network and Genetic Algorithm

  • Author

    Salimi, Elham ; Arastouie, Narges

  • fYear
    2011
  • fDate
    21-23 Oct. 2011
  • Firstpage
    817
  • Lastpage
    820
  • Abstract
    In this paper, we consider the issue of detecting a missing member of malicious codes named backdoors. We developed a novel approach for revealing them based on two clustered, system behavior and network traffic. Backdoors can easily be installed on the victim system aiming its exploit, detecting them requires considerable policies. Using Artificial Intelligence (AI) has revolutionized all security providing systems. Hence, our proposed method acquired a tunable idea using Artificial Neural Network (ANN) for classifying system features and predicting the percentage of backdoor existing probability and Genetic Algorithm (GA) in order to give a deterministic answer to the issue. Using ANN incorporation with the GA guarantees how precise our approach could be.
  • Keywords
    Artificial intelligence; Artificial neural networks; Computers; Genetic algorithms; Grippers; Intrusion detection; Artificial intelligence; Artificial neural network; Backdoor; Genetic algorithm; Intrusion detection; Security;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computational and Information Sciences (ICCIS), 2011 International Conference on
  • Conference_Location
    Chengdu, China
  • Print_ISBN
    978-1-4577-1540-2
  • Type

    conf

  • DOI
    10.1109/ICCIS.2011.103
  • Filename
    6086325