• DocumentCode
    229366
  • Title

    A theoretical Q-learning temporary security repair

  • Author

    Randrianasolo, Arisoa S. ; Pyeatt, Larry D.

  • Author_Institution
    Sch. of Comput. & Inf., Lipscomb Univ., Nashville, TN, USA
  • fYear
    2014
  • fDate
    9-12 Dec. 2014
  • Firstpage
    1
  • Lastpage
    7
  • Abstract
    This research summarizes the first attempt to incorporate Q-learning algorithm in software security. The Q-learning method is embedded as part of the software itself to provide a security mechanism that has ability to learn by itself to develop a temporary repair mechanism. The results of the experiment express that given the right parameters and the right setting the Q-learning approach rapidly learns to block all malicious actions. Data analysis on the Q-values produced by the software can provide security diagnostic as well. A larger scale experiment with extended parameter testing is expected to be seen in the future work.
  • Keywords
    data analysis; learning (artificial intelligence); security of data; software architecture; data analysis; extended parameter testing; security diagnostic; software security; temporary repair mechanism; theoretical Q-learning temporary security repair; Abstracts; Connectors; Detectors; Maintenance engineering; Security; Software; Software architecture; Machine Learning; Q-Learning; Repair; Security; Software Architecture;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computational Intelligence in Cyber Security (CICS), 2014 IEEE Symposium on
  • Conference_Location
    Orlando, FL
  • Type

    conf

  • DOI
    10.1109/CICYBS.2014.7013370
  • Filename
    7013370