TY - GEN
T1 - Tracking User Application Activity by using Machine Learning Techniques on Network Traffic
AU - Fathi-Kazerooni, Sina
AU - Kaymak, Yagiz
AU - Rojas-Cessa, Roberto
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/3/18
Y1 - 2019/3/18
N2 - A network eavesdropper may invade the privacy of an online user by collecting the passing traffic and classifying the applications that generated the network traffic. This collection may be used to build fingerprints of the user's Internet usage. In this paper, we investigate the feasibility of performing such breach on encrypted network traffic generated by actual users. We adopt the random forest algorithm to classify the applications in use by users of a campus network. Our classification system identifies and quantifies different statistical features of user's network traffic to classify applications rather than looking into packet contents. In addition, application classification is performed without employing a port mapping at the transport layer. Our results show that applications can be identified with an average precision and recall of up to 99%.
AB - A network eavesdropper may invade the privacy of an online user by collecting the passing traffic and classifying the applications that generated the network traffic. This collection may be used to build fingerprints of the user's Internet usage. In this paper, we investigate the feasibility of performing such breach on encrypted network traffic generated by actual users. We adopt the random forest algorithm to classify the applications in use by users of a campus network. Our classification system identifies and quantifies different statistical features of user's network traffic to classify applications rather than looking into packet contents. In addition, application classification is performed without employing a port mapping at the transport layer. Our results show that applications can be identified with an average precision and recall of up to 99%.
KW - Internet Traffic Classification
KW - Machine Learning
KW - Online Activity Tracking
KW - Random Forest
UR - http://www.scopus.com/inward/record.url?scp=85063874906&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85063874906&partnerID=8YFLogxK
U2 - 10.1109/ICAIIC.2019.8669040
DO - 10.1109/ICAIIC.2019.8669040
M3 - Conference contribution
AN - SCOPUS:85063874906
T3 - 1st International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2019
SP - 405
EP - 410
BT - 1st International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 1st International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2019
Y2 - 11 February 2019 through 13 February 2019
ER -