TY - GEN
T1 - Generalization regions in hamming negative selection
AU - Stibor, Thomas
AU - Timmis, Jonathan
AU - Eckert, Claudia
PY - 2006
Y1 - 2006
N2 - Negative selection is an immune-inspired algorithm which is typically applied to anomaly detection problems. We present an empirical investigation of the generalization capability of the Hamming negative selection, when combined with the r-chunk affinity metric. Our investigations reveal that when using the r-chunk metric, the length r is a crucial parameter and is inextricably linked to the input data being analyzed. Moreover, we propose that input data with different characteristics, i.e. different positional biases, can result in an incorrect generalization effect.
AB - Negative selection is an immune-inspired algorithm which is typically applied to anomaly detection problems. We present an empirical investigation of the generalization capability of the Hamming negative selection, when combined with the r-chunk affinity metric. Our investigations reveal that when using the r-chunk metric, the length r is a crucial parameter and is inextricably linked to the input data being analyzed. Moreover, we propose that input data with different characteristics, i.e. different positional biases, can result in an incorrect generalization effect.
KW - negative selection
KW - intrusion detection
KW - positional bias
KW - semantic representation
KW - generalization region
UR - http://www.scopus.com/inward/record.url?scp=33750986457&partnerID=8YFLogxK
U2 - 10.1007/3-540-33521-8_49
DO - 10.1007/3-540-33521-8_49
M3 - Conference Proceeding (Non-Journal item)
AN - SCOPUS:33750986457
SN - 9783540335207
T3 - Advances in Soft Computing
SP - 447
EP - 456
BT - Intelligent Information Processing and Web Mining
A2 - Klopotek, Mieczyslaw
A2 - Trojanowski, Krzysztof
A2 - Wierzchon, Slawomir
PB - Springer Nature
ER -