Title :
Machine self-learning applications in security systems
Author :
Jotsov, Vladimir S.
Author_Institution :
State Univ. for Libr. Studies & Inf. Technol. (SULSIT), Sofia, Bulgaria
Abstract :
A set of conflict resolution methods is investigated with the purpose to construct knowledge refinement and self-learning tools applicable in a wide range of security systems (SS). The introduction of ontologies simplifies the detection process and lowers the complexity of the machine learning procedures. Different conflict resolution ways lead to particular autonomous/intelligent applications in different types of SS. The proposed self-learning methods are combinable with other web/data mining, anomaly detection, statistical methods, and show new ways in the development of collective evolutionary systems.
Keywords :
Internet; data mining; evolutionary computation; ontologies (artificial intelligence); security of data; statistical analysis; unsupervised learning; Web mining; anomaly detection; collective evolutionary systems; conflict resolution methods; data mining; detection process; knowledge refinement; machine learning procedures complexity; machine self-learning applications; ontology; security systems; self-learning methods; self-learning tools; statistical methods; Machine learning; Multiagent systems; Ontologies; Security; Software; Training; Agent; Conflict Resolution; Evolution; Intelligent System; Model; Ontology; Security System;
Conference_Titel :
Intelligent Data Acquisition and Advanced Computing Systems (IDAACS), 2011 IEEE 6th International Conference on
Conference_Location :
Prague
Print_ISBN :
978-1-4577-1426-9
DOI :
10.1109/IDAACS.2011.6072866
