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Time Machine (Old information)

The time machine is a joint project of the Technische Universität Berlin, the Technische Universität München, and the ICSI (University of California Berkeley). It is open-source and published under the BSD license.


There are times when it would be extraordinarily convenient to record the entire contents of a high-volume network traffic stream, in order to later "travel back in time" and inspect activity that has only become interesting in retrospect. Two examples are security forensics—determining just how an attacker compromised a given machine—and network trouble-shooting, such as inspecting the precursors to a fault after the fault.

To perform this task efficiently, the packets are first stored in a ring buffer in the memory (RAM), later the packets are copied to (hard) disk. This allows the time machine to smoothen capture bandwidth peaks in memory and store huge amounts of traffic on disk, covering several days of network traffic. The time machine is designed to work in Gbps environments.

Since it is not feasible to capture the complete load of a fully utilized Gbps link to disk, the time machine utilizes a mechanism called "connection cutoff" to reduce the the amount of data to process. This "connection cutoff" only records the first X bytes of every monitored connection (identified via the 5-tupel of source and destination IP and Port and the transport protocol). Indeed this approach it does not impair the analysis capabilities (unless the cutoff is set to low) because most of the "interessting" data is located in the first few packets of a connection. The effiency of this approach comes from leveraging the heavy-tailed nature of network traffic: because the bulk of the traffic in high-volume streams comes from just a few connections.

To take full advantage of this recording it is import to be able to quickly locate certain packets. For example one might be interested in all packets of a specific connection or all packets from one IP address. This is achieved by indexing stored packets. The indexes to create can be specified, for example one could create indexes for the connection 5-tupel, for IP address pairs, for IP addresses, etc. One can than issue a queries for a specific index to the time machine and the time machine will lookup the query in its index and will return all stored packets matching the query.

To further streamline the analysis capabilities we have coupled the TimeMachine with the Bro network intrusion detection system (IDS) (www.bro-ids.org). Thus the IDS can directly interact with the TimeMachine and request historic traffic to represent it to a security analyst or to do retrospective analysis.



Please note, that the current release of the time machine is in an early development stage. Bug reports and comments on the functionality and handling of the time machine and its documentation are appreciated. Please do not hesitate to send an email with your question or comment to tmlists.net.t-labs.tu-berlin.de. Developer release: Download tm-20090206-0.tar.gz Most notable changes since 20080206:

  • Bugfixes in broccoli communication code
  • GCC 4.3 compatability

Most notable changes since 20061220:

  • Many bugfixes and performance improvements.

    • New connection table code (with less locking)
    • New index hash tables
    • Disk write performance

  • Coupling with IDS through broccoli.
  • Support for dynamic classes: The TM can be instructed to (temporarily) assign a particular host to a different storage class (e.g., if an IDS detected suspicious behavior from that host)
  • Better logging facilities.
  • Subscription support for all indexes.

Most notable changes since 20061111:

  • Huge increase in performance due to

    • Changes in internal data structures
    • Index generation and aggregation
    • using ptmalloc on FreeBSD
    • Thread scheduling

  • Documentation Updates
  • Support for running tm in the background as a daemon

(Be sure to subscribe to tm-announce.)

Previous releases:

If you are experiencing packet losses, you might perhaps want to take a look at our recommendations for best packet capturing systems.

Users Mailinglist

For up-to-date Informations on the Time Machine project, new versions, and improvments please be sure to subscribe to tm-announce mailinglist subscription page



  • Gregor Maier (TU Berlin / DT Laboratories)
  • Stefan Kornexl (TU München)


All of us can be reached via the time machine list: .


Enriching Network Security Analysis with Time Travel
Citation key MSDFPS-ENSATT-08
Author Maier, Gregor and Sommer, Robin and Dreger, Holger and Feldmann, Anja and Paxson, Vern and Schneider, Fabian
Title of Book SIGCOMM '08: Proceedings of the 2008 conference on Applications, technologies, architectures, and protocols for computer communications
Pages 183–194
Year 2008
ISBN 978-1-60558-175-0
ISSN 0146-4833
DOI http://dx.doi.org/10.1145/1402946.1402980
Location Seattle, WA, USA
Address New York, NY, USA
Month August
Note Please find the slides of the talk held during SIGCOMM'08 at http://www.net.t-labs.tu-berlin.de/papers/MSDFPS-ENSATT-08-slides.pdf.
Publisher ACM Press
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, high-performance 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.
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