TY - GEN
T1 - Enhancing the Performance of Mobile Traffic Identification with Communication Patterns
AU - Mongkolluksamee, Sophon
AU - Visoottiviseth, Vasaka
AU - Fukuda, Kensuke
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/9/21
Y1 - 2015/9/21
N2 - Traffic classification is important especially for managing and monitoring networks which contain a wide variety of traffic, such as in the mobile network. Using only packet based feature in traditional classification is not enough for classifying mobile application traffic because of the complexity of mobile traffic. Therefore, this study proposes the technique that combines the packet size distribution and communication patterns extracted via graph let for identifying mobile application. The technique is robust to the complexity of mobile traffic and has no privacy concerns. Validation results over five popular mobile applications (Facebook, Line, Skype, You Tube, and Web) demonstrate that our combined method achieves high performance (0.95) of F-measure even using only randomly sampled 50 packets during 3-minute time interval. Moreover, the combination of these features distinguishes various applications with similar characteristics such as Facebook and Web.
AB - Traffic classification is important especially for managing and monitoring networks which contain a wide variety of traffic, such as in the mobile network. Using only packet based feature in traditional classification is not enough for classifying mobile application traffic because of the complexity of mobile traffic. Therefore, this study proposes the technique that combines the packet size distribution and communication patterns extracted via graph let for identifying mobile application. The technique is robust to the complexity of mobile traffic and has no privacy concerns. Validation results over five popular mobile applications (Facebook, Line, Skype, You Tube, and Web) demonstrate that our combined method achieves high performance (0.95) of F-measure even using only randomly sampled 50 packets during 3-minute time interval. Moreover, the combination of these features distinguishes various applications with similar characteristics such as Facebook and Web.
KW - Communication patterns
KW - Graphlet
KW - Mobile application identification
KW - Random forest
KW - Traffic classification
UR - https://www.scopus.com/pages/publications/84962163764
U2 - 10.1109/COMPSAC.2015.50
DO - 10.1109/COMPSAC.2015.50
M3 - Conference contribution
AN - SCOPUS:84962163764
T3 - Proceedings - International Computer Software and Applications Conference
SP - 336
EP - 345
BT - Proceedings - 2015 IEEE 39th Annual Computer Software and Applications Conference, COMPSAC 2015
A2 - Ahamed, Sheikh Iqbal
A2 - Chang, Carl K.
A2 - Chu, William
A2 - Crnkovic, Ivica
A2 - Hsiung, Pao-Ann
A2 - Huang, Gang
A2 - Yang, Jingwei
PB - IEEE Computer Society
T2 - 39th Annual IEEE Computer Software and Applications Conference, COMPSAC 2015
Y2 - 1 July 2015 through 5 July 2015
ER -