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Enriching network security analysis with time travel

Published: 17 August 2008 Publication History

Abstract

In many situations it can be enormously helpful to archive the raw contents of a network traffic stream to disk, to enable later inspection of activity that becomes interesting only in retrospect. We present a Time Machine (TM) for network traffic that provides such a capability. The TM leverages the heavy-tailed nature of network flows to capture nearly all of the likely-interesting traffic while storing only a small fraction of the total volume. An initial proof-of-principle prototype established the forensic value of such an approach, contributing to the investigation of numerous attacks at a site with thousands of users. Based on these experiences, a rearchitected implementation of the system provides flexible, highperformance traffic stream capture, indexing and retrieval, including an interface between the TM and a real-time network intrusion detection system (NIDS). The NIDS controls the TM by dynamically adjusting recording parameters, instructing it to permanently store suspicious activity for offline forensics, and fetching traffic from the past for retrospective analysis. We present a detailed performance evaluation of both stand-alone and joint setups, and report on experiences with running the system live in high-volume environments.

References

[1]
ANDERSON, E., AND ARLITT, M. Full Packet Capture and Offline Analysis on 1 and 10 Gb/s Networks. Tech. Rep. HPL-2006-156, HP Labs, 2006.
[2]
ANTONELLI, C., CO, K., M FIELDS, AND HONEYMAN, P. Cryptographic Wiretapping at 100 Megabits. In SPIE 16th Int. Symp. on Aerospace Defense Sensing, Simulation, and Controls. (2002).
[3]
CHANDRASEKARAN, S., AND FRANKLIN, M. Remembrance of Streams Past: Overload-sensitive Management of Archived Streams. In Proc. Very Large Data Bases (2004).
[4]
ClearSight Networks. http://www.clearsightnet.com.
[5]
CNET NEWS. Another suspected NASA hacker indicted. http://www.news.com/2102-7350_3-6140001.html.
[6]
CoMo. http://como.sourceforge.net.
[7]
COOKE, E., MYRICK, A., RUSEK, D., AND JAHANIAN, F. Resource-aware Multi-format Network Security Data Storage. In Proc. SIGCOMM LSAD workshop (2006).
[8]
CRANOR, C., JOHNSON, T., AND SPATSCHECK, O. Gigascope: A Stream Database for Network Applications. In Proc. SIGMOD (2003).
[9]
DESNOYERS, P., AND SHENOY, P. J. Hyperion: High Volume Stream Archival for Retrospective Querying. In Proc. 2007 USENIX Technical Conf (2007).
[10]
DREGER, H., FELDMANN, A., PAXSON, V., AND SOMMER, R. Operational Experiences with High-Volume Network Intrusion Detection. In Proc. 11th ACM Conf. on Comp. and Comm. Security (2004).
[11]
DUNLAP, G. W., KING, S. T., CINAR, S., BASRAI, M. A., AND CHEN, P. M. ReVirt: Enabling Intrusion Analysis through Virtual-Machine Logging and Replay. In Proc. Symp. on Operating Systems Design and Implementation (2002).
[12]
ENDACE MEASUREMENT SYSTEMS. http://www.endace.com/, 2008.
[13]
GONZALEZ, J. M., PAXSON, V., AND WEAVER, N. Shunting: A Hardware/Software Architecture for Flexible, High-performance Network Intrusion Prevention. In Proc. 14th ACM Conf. on Comp. and Comm. Security (2007).
[14]
Intelica Networks. http://www.intelicanetworks.com.
[15]
KORNEXL, S., PAXSON, V., DREGER, H., FELDMANN, A., AND SOMMER, R. Building a Time Machine for Efficient Recording and Retrieval of High-Volume Network Traffic (Short Paper). In Proc. ACM SIGCOMM IMC (2005).
[16]
MCGRATH, K. P., AND NELSON, J. Monitoring & Forensic Analysis for Wireless Networks. In Proc. Conf. on Internet Surveillance and Protection (2006).
[17]
PARK, K., KIM, G., AND CROVELLA, M. On the Relationship Between File Sizes, Transport Protocols, and Self-similar Network Traffic. In Proc. ICNP 96 (1996).
[18]
PAXSON, V. Bro: A System for Detecting Network Intruders in Real-Time. Comp. Networks 31, 2324 (1999).
[19]
PAXSON, V., AND FLOYD, S. Wide-Area Traffic: The Failure of Poisson Modeling. IEEE/ACM Transactions on Networking 3, 3 (1995).
[20]
PONEC, M., GIURA, P., BRÖNNIMANN, H., AND WEIN, J. Highly Efficient Techniques for Network Forensics. In Proc. 14th ACM Conf. on Comp. and Comm. Security (2007).
[21]
REISS, F., STOCKINGER, K., WU, K., SHOSHANI, A., AND HELLERSTEIN, J. M. Enabling Real-Time Querying of Live and Historical Stream Data. In Proc. Statistical & Scientific Database Management (2007).
[22]
ROESCH, M. Snort Lightweight Intrusion Detection for Networks. In Proc. 13th Systems Administration Conference - LISA 99 (1999), pp. 229238.
[23]
SHANMUGASUNDARAM, K., MEMON, N., SAVANT, A., AND BRÖNNIMANN, H. ForNet: A Distributed Forensics Network. In Proc. Workshop on Math. Methods, Models and Architectures for Comp. Networks Security (2003).
[24]
SOMMER, R. Viable Network Intrusion Detection in High-Performance Environments. PhD thesis, TU München, 2005.
[25]
SOMMER, R., AND PAXSON, V. Exploiting Independent State For Network Intrusion Detection. In Proc. Computer Security Applications Conf. (2005).
[26]
VALLENTIN, M., SOMMER, R., LEE, J., LERES, C., PAXSON, V., AND TIERNEY, B. The NIDS Cluster: Scalable, Stateful Network Intrusion Detection on Commodity Hardware. In Proc. 10th Int. Symp. Recent Advances in Intrusion Detection (RAID) (2007).
[27]
WALLERICH, J., DREGER, H., FELDMANN, A., KRISHNAMURTHY, B., AND WILLINGER, W. A Methodology for Studying Persistency Aspects of Internet Flows. ACM SIGCOMM CCR 35, 2 (Apr 2005), 2336.

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    Published In

    cover image ACM SIGCOMM Computer Communication Review
    ACM SIGCOMM Computer Communication Review  Volume 38, Issue 4
    October 2008
    436 pages
    ISSN:0146-4833
    DOI:10.1145/1402946
    Issue’s Table of Contents
    • cover image ACM Conferences
      SIGCOMM '08: Proceedings of the ACM SIGCOMM 2008 conference on Data communication
      August 2008
      452 pages
      ISBN:9781605581750
      DOI:10.1145/1402958
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 17 August 2008
    Published in SIGCOMM-CCR Volume 38, Issue 4

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    Author Tags

    1. forensics
    2. intrusion detection
    3. packet capture

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    • (2023)Accelerating IDS Using TLS Pre-Filter in FPGA2023 IEEE Symposium on Computers and Communications (ISCC)10.1109/ISCC58397.2023.10218049(436-442)Online publication date: 9-Jul-2023
    • (2020)An Unsupervised Deep Learning Model for Early Network Traffic Anomaly DetectionIEEE Access10.1109/ACCESS.2020.29730238(30387-30399)Online publication date: 2020
    • (2020)A review on machine learning–based approaches for Internet traffic classificationAnnals of Telecommunications10.1007/s12243-020-00770-775:11-12(673-710)Online publication date: 22-Jun-2020
    • (2017)Acceptance Test for Fault Detection in Component-based Cloud Computing and SystemsFuture Generation Computer Systems10.1016/j.future.2016.06.03070(74-93)Online publication date: May-2017
    • (2015)Selective Capping of Packet Payloads for Network Analysis and ManagementTraffic Monitoring and Analysis10.1007/978-3-319-17172-2_1(3-16)Online publication date: 17-Apr-2015
    • (2014)Construction and Analysis of Website Intrusion Forensics ModelApplied Mechanics and Materials10.4028/www.scientific.net/AMM.687-691.2748687-691(2748-2751)Online publication date: Nov-2014
    • (2014)A roadmap towards improving managed security services from a privacy perspectiveEthics and Information Technology10.1007/s10676-014-9348-316:3(227-240)Online publication date: 1-Sep-2014
    • (2014)Multi-granular, multi-purpose and multi-Gb/s monitoring on off-the-shelf systemsNetworks10.1002/nem.186124:4(221-234)Online publication date: 1-Jul-2014
    • (2013)Horizon extenderProceedings of the 8th ACM SIGSAC symposium on Information, computer and communications security10.1145/2484313.2484378(499-504)Online publication date: 8-May-2013
    • (2012)Extended Time Machine Design using Reconfigurable Computing for Efficient Recording and Retrieval of Gigabit Network TrafficComputer Engineering10.4018/978-1-61350-456-7.ch313(699-709)Online publication date: 2012
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