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
Link To Document :
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