TY - GEN
T1 - Unexpected means of protocol inference
AU - Ma, Justin
AU - Levchenko, Kirill
AU - Kreibich, Christian
AU - Savage, Stefan
AU - Voelker, Geoffrey M.
PY - 2006
Y1 - 2006
N2 - Network managers are inevitably called upon to associate network traffic with particular applications. Indeed, this operation is critical for a wide range of management functions ranging from debugging and security to analytics and policy support. Traditionally, managers have relied on application adherence to a well established global port mapping: Web traffic on port 80, mail traffic on port 25 and so on. However, a range of factors - including firewall port blocking, tunneling, dynamic port allocation, and a bloom of new distributed applications - has weakened the value of this approach. We analyze three alternative mechanisms using statistical and structural content models for automatically identifying traffic that uses the same application-layer protocol, relying solely on flow content. In this manner, known applications may be identified regardless of port number, while traffic from one unknown application will be identified as distinct from another. We evaluate each mechanism's classification performance using real-world traffic traces from multiple sites.
AB - Network managers are inevitably called upon to associate network traffic with particular applications. Indeed, this operation is critical for a wide range of management functions ranging from debugging and security to analytics and policy support. Traditionally, managers have relied on application adherence to a well established global port mapping: Web traffic on port 80, mail traffic on port 25 and so on. However, a range of factors - including firewall port blocking, tunneling, dynamic port allocation, and a bloom of new distributed applications - has weakened the value of this approach. We analyze three alternative mechanisms using statistical and structural content models for automatically identifying traffic that uses the same application-layer protocol, relying solely on flow content. In this manner, known applications may be identified regardless of port number, while traffic from one unknown application will be identified as distinct from another. We evaluate each mechanism's classification performance using real-world traffic traces from multiple sites.
KW - Application signatures
KW - Network data mining
KW - Protocol analysis
KW - Relative entropy
KW - Sequence analysis
KW - Statistical content modeling
KW - Traffic classification
UR - https://www.scopus.com/pages/publications/34249790654
UR - https://www.scopus.com/inward/citedby.url?scp=34249790654&partnerID=8YFLogxK
U2 - 10.1145/1177080.1177123
DO - 10.1145/1177080.1177123
M3 - Conference contribution
AN - SCOPUS:34249790654
SN - 1595935614
SN - 9781595935618
T3 - Proceedings of the ACM SIGCOMM Internet Measurement Conference, IMC
SP - 313
EP - 326
BT - Proceedings of the 2006 ACM SIGCOMM Internet Measurement Conference, IMC 2006
T2 - 6th ACM SIGCOMM on Internet Measurement Conference, IMC 2006
Y2 - 25 October 2006 through 27 October 2006
ER -