TY - GEN
T1 - An embedding approach to anomaly detection
AU - Hu, Renjun
AU - Aggarwal, Charu C.
AU - Ma, Shuai
AU - Huai, Jinpeng
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2016/6/22
Y1 - 2016/6/22
N2 - Network anomaly detection has become very popular in recent years because of the importance of discovering key regions of structural inconsistency in the network. In addition to application-specific information carried by anomalies, the presence of such structural inconsistency is often an impediment to the effective application of data mining algorithms such as community detection and classification. In this paper, we study the problem of detecting structurally inconsistent nodes that connect to a number of diverse influential communities in large social networks. We show that the use of a network embedding approach, together with a novel dimension reduction technique, is an effective tool to discover such structural inconsistencies. We also experimentally show that the detection of such anomalous nodes has significant applications: one is the specific use of detected anomalies, and the other is the improvement of the effectiveness of community detection.
AB - Network anomaly detection has become very popular in recent years because of the importance of discovering key regions of structural inconsistency in the network. In addition to application-specific information carried by anomalies, the presence of such structural inconsistency is often an impediment to the effective application of data mining algorithms such as community detection and classification. In this paper, we study the problem of detecting structurally inconsistent nodes that connect to a number of diverse influential communities in large social networks. We show that the use of a network embedding approach, together with a novel dimension reduction technique, is an effective tool to discover such structural inconsistencies. We also experimentally show that the detection of such anomalous nodes has significant applications: one is the specific use of detected anomalies, and the other is the improvement of the effectiveness of community detection.
UR - https://www.scopus.com/pages/publications/84980318041
U2 - 10.1109/ICDE.2016.7498256
DO - 10.1109/ICDE.2016.7498256
M3 - 会议稿件
AN - SCOPUS:84980318041
T3 - 2016 IEEE 32nd International Conference on Data Engineering, ICDE 2016
SP - 385
EP - 396
BT - 2016 IEEE 32nd International Conference on Data Engineering, ICDE 2016
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 32nd IEEE International Conference on Data Engineering, ICDE 2016
Y2 - 16 May 2016 through 20 May 2016
ER -