• DocumentCode
    259627
  • Title

    Q-Learning: From Computer Network Security to Software Security

  • Author

    Randrianasolo, Arisoa S. ; Pyeatt, Larry D.

  • Author_Institution
    Sch. of Comput. & Inf., Lipscomb Univ., Nashville, TN, USA
  • fYear
    2014
  • fDate
    3-6 Dec. 2014
  • Firstpage
    257
  • Lastpage
    262
  • Abstract
    Reinforcement learning techniques become more popular in computer network security. The same reinforcement learning techniques developed for network security can be applied to software security as well. This research summarizes a work in progress 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 is expected to be seen in the future work.
  • Keywords
    computer network security; data analysis; learning (artificial intelligence); Q-learning; Q-values; computer network security; data analysis; malicious actions; reinforcement learning techniques; security diagnostic; software security; temporary repair mechanism; Connectors; Detectors; Learning (artificial intelligence); Maintenance engineering; Security; Software systems; Machine Learning; Q-Learning; Repair; Security; Software Architecture;
  • 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.2014.47
  • Filename
    7033124