DocumentCode :
3115269
Title :
Towards a Classifying Artificial Immune System for Web Server Attacks
Author :
Danforth, Melissa
Author_Institution :
Dept. of Comput. Sci., California State Univ., Bakersfield, Bakersfield, CA, USA
fYear :
2009
fDate :
13-15 Dec. 2009
Firstpage :
523
Lastpage :
527
Abstract :
Classic artificial immune systems for security provide only a simple binary classification of "attack" versus "normal". This work explores expanding an artificial immune system for Web server requests into a classifying system that can categorize the attack as one of several common attack categories. Classification can provide a system administrator with an indication of the severity of the attack and can help direct attack mitigation. This work shows promise at the task of classifying Web server attacks, but still requires some fine-tuning to get the best performance.
Keywords :
Internet; artificial immune systems; pattern classification; security of data; Web server attacks classification; artificial immune system; attack severity; direct attack mitigation; system administrator; Application software; Artificial immune systems; Computer science; Encoding; Fingerprint recognition; Immune system; Intrusion detection; Machine learning; Road transportation; Web server; artificial immune systems;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Machine Learning and Applications, 2009. ICMLA '09. International Conference on
Conference_Location :
Miami Beach, FL
Print_ISBN :
978-0-7695-3926-3
Type :
conf
DOI :
10.1109/ICMLA.2009.38
Filename :
5381434
Link To Document :
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