TY - GEN
T1 - Service usage analysis in mobile messaging apps
T2 - 16th IEEE International Conference on Data Mining, ICDM 2016
AU - Fu, Yanjie
AU - Liu, Junming
AU - Li, Xiaolin
AU - Lu, Xinjiang
AU - Ming, Jingci
AU - Guan, Chu
AU - Xiong, Hui
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2016/7/2
Y1 - 2016/7/2
N2 - The service usage analysis, aiming at identifying customers' messaging behaviors based on encrypted App traffic flows, has become a challenging and emergent task for service providers. Prior literature usually starts from segmenting a traffic sequence into single-usage subsequences, and then classify the subsequences into different usage types. However, they could suffer from inaccurate traffic segmentations and mixed-usage subsequences. To address this challenge, we exploit a multi-label multi-view learning strategy and develop an enhanced framework for in-App usage analytics. Specifically, we first devise an enhanced traffic segmentation method to reduce mixed-usage subsequences. Besides, we develop a multi-label multi-view logistic classification method, which comprises two alignments. The first alignment is to make use of the classification consistency between packet-length view and time-delay view of traffic subsequences and improve classification accuracy. The second alignment is to combine the classification of single-usage subsequence and the post-classification of mixed-usage subsequences into a unified multi-label logistic classification problem. Finally, we present extensive experiments with real-world datasets to demonstrate the effectiveness of our approach.
AB - The service usage analysis, aiming at identifying customers' messaging behaviors based on encrypted App traffic flows, has become a challenging and emergent task for service providers. Prior literature usually starts from segmenting a traffic sequence into single-usage subsequences, and then classify the subsequences into different usage types. However, they could suffer from inaccurate traffic segmentations and mixed-usage subsequences. To address this challenge, we exploit a multi-label multi-view learning strategy and develop an enhanced framework for in-App usage analytics. Specifically, we first devise an enhanced traffic segmentation method to reduce mixed-usage subsequences. Besides, we develop a multi-label multi-view logistic classification method, which comprises two alignments. The first alignment is to make use of the classification consistency between packet-length view and time-delay view of traffic subsequences and improve classification accuracy. The second alignment is to combine the classification of single-usage subsequence and the post-classification of mixed-usage subsequences into a unified multi-label logistic classification problem. Finally, we present extensive experiments with real-world datasets to demonstrate the effectiveness of our approach.
UR - http://www.scopus.com/inward/record.url?scp=85014531887&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85014531887&partnerID=8YFLogxK
U2 - 10.1109/ICDM.2016.0106
DO - 10.1109/ICDM.2016.0106
M3 - Conference contribution
AN - SCOPUS:85014531887
T3 - Proceedings - IEEE International Conference on Data Mining, ICDM
SP - 877
EP - 882
BT - Proceedings - 16th IEEE International Conference on Data Mining, ICDM 2016
A2 - Bonchi, Francesco
A2 - Domingo-Ferrer, Josep
A2 - Baeza-Yates, Ricardo
A2 - Zhou, Zhi-Hua
A2 - Wu, Xindong
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 12 December 2016 through 15 December 2016
ER -