TY - GEN
T1 - P2P traffic classification for residential network
AU - Thay, Channary
AU - Visoottiviseth, Vasaka
AU - Mongkolluksamee, Sophon
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2016/2/8
Y1 - 2016/2/8
N2 - Excessive bandwidth consuming by peer-to-peer (P2P) applications is one of serious problems in residential networks such as in dorms, apartments and even Small and Medium-sized Enterprises (SMEs) networks which have a limited bandwidth. P2P file sharing and P2P streaming applications usually are the cause of this problem. To share the bandwidth fairly among users, the traffic of these applications needs to be classified and filtered out. However, traditional port-based and payload-based classification will fail when the applications use dynamic ports, port disguise and payload encryption. In this paper, we present the classification technique that based on characteristics of number of peer connection and number of traffic in both incoming and outgoing direction for 5-minute duration to classify the P2P traffic. We make use of decision tree J48 to model and classify the traffic. Experimental results over three well-known P2P applications (BitTorrent, Skype and SopCast) confirm that this technique can detect the existence of P2P traffic from the background traffic with 100% accuracy and can classify three types of P2P applications with 90% accuracy.
AB - Excessive bandwidth consuming by peer-to-peer (P2P) applications is one of serious problems in residential networks such as in dorms, apartments and even Small and Medium-sized Enterprises (SMEs) networks which have a limited bandwidth. P2P file sharing and P2P streaming applications usually are the cause of this problem. To share the bandwidth fairly among users, the traffic of these applications needs to be classified and filtered out. However, traditional port-based and payload-based classification will fail when the applications use dynamic ports, port disguise and payload encryption. In this paper, we present the classification technique that based on characteristics of number of peer connection and number of traffic in both incoming and outgoing direction for 5-minute duration to classify the P2P traffic. We make use of decision tree J48 to model and classify the traffic. Experimental results over three well-known P2P applications (BitTorrent, Skype and SopCast) confirm that this technique can detect the existence of P2P traffic from the background traffic with 100% accuracy and can classify three types of P2P applications with 90% accuracy.
KW - application identification
KW - decision-tree j48
KW - peer-to-peer application
KW - traffic classification
UR - https://www.scopus.com/pages/publications/84964370971
U2 - 10.1109/ICSEC.2015.7401433
DO - 10.1109/ICSEC.2015.7401433
M3 - Conference contribution
AN - SCOPUS:84964370971
T3 - ICSEC 2015 - 19th International Computer Science and Engineering Conference: Hybrid Cloud Computing: A New Approach for Big Data Era
BT - ICSEC 2015 - 19th International Computer Science and Engineering Conference
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 19th International Computer Science and Engineering Conference, ICSEC 2015
Y2 - 23 November 2015 through 26 November 2015
ER -