Title :
A new clonal selection algorithm based on radius regularization of anomaly detectors
Author :
Seresht, Neda Afzali ; Azmi, Reza ; Pishgoo, Boshra
Author_Institution :
Oper. Syst. Security Lab. (OSSL), Alzahra Univ., Tehran, Iran
Abstract :
Nowadays anomaly detection based on Artificial Immune system (AIS) is highly regarded among the research community and Clonal selection algorithm is one of the most interesting fields of it. In this paper we model self-region by a number of spheres with fix radius. We have assumed anomaly detectors as spherical shape with random central point and variable radius out of this region. Detection rate (DR) and false alarm (FA) are influenced by radius of Detectors. Large radiuses would increase both DR and FA and small radiuses would decrease them, so selecting appropriate quantity plays an important role for achieving high accuracy in recognition. The radius of detectors is determined based on self-environment. The radius of self-region spheres has straight effect on detectors radiuses and is an important parameter to achieve high accuracy. As a result of finding imperfect self-region radius we have faced weak detectors that couldn´t recognize anomaly perfectly. In this paper we propose a novel algorithm to achieve suitable accuracy independent of self-region radius using learning automata.
Keywords :
artificial immune systems; learning automata; security of data; AIS; DR; FA; anomaly detectors; artificial immune system; clonal selection algorithm; detection rate; false alarm; learning automata; radius regularization; random central point; self-region spheres; Accuracy; Cloning; Detectors; Immune system; Learning automata; Sociology; Statistics; Artificial immune systems; clonal selection; learning automata; radius regularization;
Conference_Titel :
Artificial Intelligence and Signal Processing (AISP), 2012 16th CSI International Symposium on
Conference_Location :
Shiraz, Fars
Print_ISBN :
978-1-4673-1478-7
DOI :
10.1109/AISP.2012.6313798