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Toward the accurate identification of network applications

Published: 31 March 2005 Publication History

Abstract

Well-known port numbers can no longer be used to reliably identify network applications. There is a variety of new Internet applications that either do not use well-known port numbers or use other protocols, such as HTTP, as wrappers in order to go through firewalls without being blocked. One consequence of this is that a simple inspection of the port numbers used by flows may lead to the inaccurate classification of network traffic. In this work, we look at these inaccuracies in detail. Using a full payload packet trace collected from an Internet site we attempt to identify the types of errors that may result from port-based classification and quantify them for the specific trace under study. To address this question we devise a classification methodology that relies on the full packet payload. We describe the building blocks of this methodology and elaborate on the complications that arise in that context. A classification technique approaching 100% accuracy proves to be a labor-intensive process that needs to test flow-characteristics against multiple classification criteria in order to gain sufficient confidence in the nature of the causal application. Nevertheless, the benefits gained from a content-based classification approach are evident. We are capable of accurately classifying what would be otherwise classified as unknown as well as identifying traffic flows that could otherwise be classified incorrectly. Our work opens up multiple research issues that we intend to address in future work.

References

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Moore, D., Keys, K., Koga, R., Lagache, E., kc Claffy: CoralReef software suite as a tool for system and network administrators. In: Proceedings of the LISA 2001 15th Systems Administration Conference. (2001).
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Connie Logg and Les Cottrell: Characterization of the Traffic between SLAC and the Internet (2003) http://www.slac.stanford.edu/comp/net/slac-netflow/html/SLACnetflow.html.
[3]
Fraleigh, C., Moon, S., Lyles, B., Cotton, C., Khan, M., Moll, D., Rockell, R., Seely, T., Diot, C.: Packet-level traffic measurements from the sprint IP backbone. IEEE Network (2003) 6-16.
[4]
Choi, T., Kim, C., Yoon, S., Park, J., Lee, B., Kim, H., Chung, H., Jeong, T.: Content-aware Internet Application Traffic Measurement and Analysis. In: IEEE/IFIP Network Operations & Management Symposium (NOMS) 2004. (2004).
[5]
Moore, A., Hall, J., Kreibich, C., Harris, E., Pratt, I.: Architecture of a Network Monitor. In: Passive & Active Measurement Workshop 2003 (PAM2003). (2003).
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Roesch, M.: Snort - Lightweight Intrusion Detection for Networks. In: USENIX 13th Systems Administration Conference -- LISA '99, Seattle, WA (1999).
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Orebaugh, A., Morris, G., Warnicke, E., Ramirez, G.: Ethereal Packet Sniffing. Syngress Publishing, Rockland, MA (2004).
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Moore, A.: Discrete content-based classification -- a data set. Technical Report, Intel Research, Cambridge (2005).

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  • (2023)Replication: Contrastive Learning and Data Augmentation in Traffic Classification Using a Flowpic Input RepresentationProceedings of the 2023 ACM on Internet Measurement Conference10.1145/3618257.3624820(36-51)Online publication date: 24-Oct-2023
  • (2023)ChainDetector: Identifying Anonymous Blockchain TrafficProceedings of the 2023 International Conference on Frontiers of Artificial Intelligence and Machine Learning10.1145/3616901.3616932(136-139)Online publication date: 14-Apr-2023
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    Information & Contributors

    Information

    Published In

    cover image Guide Proceedings
    PAM'05: Proceedings of the 6th international conference on Passive and Active Network Measurement
    March 2005
    373 pages
    ISBN:3540255206

    Sponsors

    • INTEL: Intel Corporation
    • Endace Measurement Systems: Endace Measurement Systems

    Publisher

    Springer-Verlag

    Berlin, Heidelberg

    Publication History

    Published: 31 March 2005

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    • (2024)OptiClass: An Optimized Classifier for Application Layer Protocols Using Bit Level SignaturesACM Transactions on Privacy and Security10.1145/363377727:1(1-23)Online publication date: 10-Jan-2024
    • (2023)Replication: Contrastive Learning and Data Augmentation in Traffic Classification Using a Flowpic Input RepresentationProceedings of the 2023 ACM on Internet Measurement Conference10.1145/3618257.3624820(36-51)Online publication date: 24-Oct-2023
    • (2023)ChainDetector: Identifying Anonymous Blockchain TrafficProceedings of the 2023 International Conference on Frontiers of Artificial Intelligence and Machine Learning10.1145/3616901.3616932(136-139)Online publication date: 14-Apr-2023
    • (2023)Decoding the Kodi EcosystemACM Transactions on the Web10.1145/356370017:1(1-36)Online publication date: 1-Feb-2023
    • (2023)SHAPE: A Simultaneous Header and Payload Encoding Model for Encrypted Traffic ClassificationIEEE Transactions on Network and Service Management10.1109/TNSM.2022.321375820:2(1993-2012)Online publication date: 1-Jun-2023
    • (2023)A Network Traffic Anomaly Detection Method Based on Shapelet and KNNArtificial Intelligence Security and Privacy10.1007/978-981-99-9785-5_5(53-64)Online publication date: 3-Dec-2023
    • (2022)Reinforcement Learning-Based Service-Oriented Dynamic Multipath Routing in SDNWireless Communications & Mobile Computing10.1155/2022/13309932022Online publication date: 1-Jan-2022
    • (2022)AppClassNetACM SIGCOMM Computer Communication Review10.1145/3561954.356195852:3(19-27)Online publication date: 6-Sep-2022
    • (2022)Practical and configurable network traffic classification using probabilistic machine learningCluster Computing10.1007/s10586-021-03393-225:4(2839-2853)Online publication date: 1-Aug-2022
    • (2021)Auto-Recon: An Automated Network Reconnaissance System Based on Knowledge GraphAlgorithms and Architectures for Parallel Processing10.1007/978-3-030-95391-1_7(101-115)Online publication date: 3-Dec-2021
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