The correlation study for parameters in four tuples

For analysing the characteristics of current internet connections, this paper proposed several analytical schemes to study the correlation of parameters inside four tuples. We extracted the real trace in packet level from campus network border routers. No sampling method is added to maintain the full information. First, different time bins are employed for exploring the single parameter variations comprehensively. Then, the potential association of parameters inside four tuples is investigated two-by-two. Thirdly, three tuples' properties are shown and compared. The significance of source port is also discussed. Following that, the duration and size for connections, i.e., four tuples, are studied. Some interesting observations are presented and explained. The influence of three and four tuples fluctuation is checked based on a new statistical scheme we proposed. Finally, the service type classification of connections and packets are demonstrated, respectively. The distribution properties of different services are also declared. The conclusions summarise several unexpected phenomenon and the future work is pointed out at the end.

[1]  Divesh Srivastava,et al.  Finding Hierarchical Heavy Hitters in Data Streams , 2003, VLDB.

[2]  Benoit Claise,et al.  Internet Engineering Task Force (ietf) Flow Aggregation for the Ip Flow Information Export (ipfix) Protocol , 2022 .

[3]  Nick McKeown,et al.  Algorithms for packet classification , 2001, IEEE Netw..

[4]  Alex X. Liu,et al.  High-Speed Flow Nature Identification , 2009, 2009 29th IEEE International Conference on Distributed Computing Systems.

[5]  Benoit Claise,et al.  Operation of the IP Flow Information Export (IPFIX) Protocol on IPFIX Mediators , 2014, RFC.

[6]  Stan Matwin,et al.  Network traffic classification using AdaBoost Dynamic , 2013, 2013 IEEE International Conference on Communications Workshops (ICC).

[7]  Baohua Yang,et al.  Practical Multituple Packet Classification Using Dynamic Discrete Bit Selection , 2014, IEEE Transactions on Computers.

[8]  Simon Tjoa,et al.  Evidence and Cloud Computing: The Virtual Machine Introspection Approach , 2013, J. Wirel. Mob. Networks Ubiquitous Comput. Dependable Appl..

[9]  Wei Ding,et al.  Comparative Research on Internet Flows Characteristics , 2012, 2012 Third International Conference on Networking and Distributed Computing.

[10]  Judith Kelner,et al.  A Survey on Internet Traffic Identification , 2009, IEEE Communications Surveys & Tutorials.

[11]  Scott Shenker,et al.  On the characteristics and origins of internet flow rates , 2002, SIGCOMM.

[12]  Du Min,et al.  Online Internet traffic identification algorithm based on multistage classifier , 2013, China Communications.

[13]  Benoit Claise,et al.  Cisco Systems NetFlow Services Export Version 9 , 2004, RFC.

[14]  Salvatore Pontarelli,et al.  Traffic-Aware Design of a High-Speed FPGA Network Intrusion Detection System , 2013, IEEE Transactions on Computers.

[15]  Sung-Ho Yoon,et al.  Behavior signature for big data traffic identification , 2014, 2014 International Conference on Big Data and Smart Computing (BIGCOMP).

[16]  Kun-Chan Lan,et al.  A measurement study of correlations of Internet flow characteristics , 2006, Comput. Networks.

[17]  Karl Andersson,et al.  Rethinking IP Mobility Management , 2012, J. Wirel. Mob. Networks Ubiquitous Comput. Dependable Appl..

[18]  Yong Chen,et al.  Binary-tree-based high speed packet classification system on FPGA , 2013, The International Conference on Information Networking 2013 (ICOIN).

[19]  Salvatore D'Antonio,et al.  Flow Selection Techniques , 2013, RFC.

[20]  Sung-Ho Yoon,et al.  An efficient method to maintain the header signatures for internet traffic identification , 2013, 2013 15th Asia-Pacific Network Operations and Management Symposium (APNOMS).

[21]  Nevil Brownlee,et al.  Streams, Flows and Torrents , 2001 .

[22]  Anja Feldmann,et al.  Leveraging Zipf's law for traffic offloading , 2012, CCRV.

[23]  Viktor K. Prasanna,et al.  Scalable Packet Classification on FPGA , 2012, IEEE Transactions on Very Large Scale Integration (VLSI) Systems.

[24]  Jeffrey Xu Yu,et al.  Duplicate-Insensitive Order Statistics Computation over Data Streams , 2010, IEEE Transactions on Knowledge and Data Engineering.

[25]  Fabrice Guillemin,et al.  A heuristic method of finding heavy hitter prefix pairs in IP traffic , 2009, IEEE Communications Letters.

[26]  Anja Feldmann,et al.  Capturing the Variability of Internet Flows Across Time , 2006, Proceedings IEEE INFOCOM 2006. 25TH IEEE International Conference on Computer Communications.

[27]  Lieguang Zeng,et al.  GrainFlow: Enable data plane innovation at per-bit level , 2013, 2013 IEEE International Conference on Communications (ICC).

[28]  Divyakant Agrawal,et al.  Fast Algorithms for Heavy Distinct Hitters using Associative Memories , 2007, 27th International Conference on Distributed Computing Systems (ICDCS '07).

[29]  Eduardo Rocha,et al.  A Survey of Payload-Based Traffic Classification Approaches , 2014, IEEE Communications Surveys & Tutorials.

[30]  Kang Li,et al.  Architectures for packet classification caching , 2003, The 11th IEEE International Conference on Networks, 2003. ICON2003..

[31]  Antonio Pescapè,et al.  Issues and future directions in traffic classification , 2012, IEEE Network.

[32]  Anees Shaikh,et al.  Load-sensitive routing of long-lived IP flows , 1999, SIGCOMM '99.

[33]  Li Fan,et al.  Web caching and Zipf-like distributions: evidence and implications , 1999, IEEE INFOCOM '99. Conference on Computer Communications. Proceedings. Eighteenth Annual Joint Conference of the IEEE Computer and Communications Societies. The Future is Now (Cat. No.99CH36320).

[34]  Xiaojun Wang,et al.  Ultra-High Throughput Low-Power Packet Classification , 2014, IEEE Transactions on Very Large Scale Integration (VLSI) Systems.

[35]  kc claffy,et al.  Understanding Internet traffic streams: dragonflies and tortoises , 2002, IEEE Commun. Mag..

[36]  Lada A. Adamic,et al.  Zipf's law and the Internet , 2002, Glottometrics.