TY - GEN
T1 - A method for analyzing software faults based on mining outliers' feature attribute sets
AU - Ren, Jiadong
AU - Hu, Changzhen
AU - Wang, Kunsheng
AU - Wu, Di
PY - 2009
Y1 - 2009
N2 - Faults analysis is a hot topic in the field software security. In this paper, the concepts of the improved Euclidian distance and the feature attribute set are defined. A novel algorithm MOFASIED for mining outliers' feature attribute set based on improved Euclidian distance is presented. The high dimensional space is divided into some subspaces. The outlier set is obtained by using the definition of the improved Euclidian distance in each subspace. Moreover, the corresponding feature attribute sets of the outliers are gained. The outliers are formalized by the attribute sets. According to the idea of the anomaly-based intrusion detection research, a software faults analysis method SFAMOFAS based on mining outliers' feature attribute set is proposed. The outliers' feature attributes can be mined to guide the software faults feature. Experimental results show that MOFASIED is better than the distance-based outlier mining algorithm in performance test and time cost.
AB - Faults analysis is a hot topic in the field software security. In this paper, the concepts of the improved Euclidian distance and the feature attribute set are defined. A novel algorithm MOFASIED for mining outliers' feature attribute set based on improved Euclidian distance is presented. The high dimensional space is divided into some subspaces. The outlier set is obtained by using the definition of the improved Euclidian distance in each subspace. Moreover, the corresponding feature attribute sets of the outliers are gained. The outliers are formalized by the attribute sets. According to the idea of the anomaly-based intrusion detection research, a software faults analysis method SFAMOFAS based on mining outliers' feature attribute set is proposed. The outliers' feature attributes can be mined to guide the software faults feature. Experimental results show that MOFASIED is better than the distance-based outlier mining algorithm in performance test and time cost.
UR - http://www.scopus.com/inward/record.url?scp=77949594923&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-04875-3_43
DO - 10.1007/978-3-642-04875-3_43
M3 - Conference contribution
AN - SCOPUS:77949594923
SN - 9783642048746
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 409
EP - 417
BT - Active Media Technology - 5th International Conference, AMT 2009, Proceedings
T2 - 5th International Conference on Active Media Technology, AMT 2009
Y2 - 22 October 2009 through 24 October 2009
ER -