Social dynamics of FLOSS team communication across channels

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Presentation for IFIP 2.13 Open Source Software 2008 in Milan, reporting findings of dynamic social network analysis of communications in FLOSS teams in a variety of communication venues, including trackers, developer communication venues and user communication venues.

Technologie Économie & finance

Social dynamics of FLOSS team communication across channels Andrea Wiggins, James Howison & Kevin Crowston Syracuse University School of Information Studies 9 September 2008 ~ IFIP 2.13 - OSS 2008

Introduction ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

FLOSS Networks ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Methodological Issues ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Measures and Time ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Sliding Windows and Smoothing 90 days 30 days 30 days 90 day window, sliding forward by 30 days at a time

Intensity and Variation Across Venues ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Venue Volumes Over Time

Data ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Intensity-Based Smoothing ,[object Object],[object Object],[object Object],[object Object],[object Object]

Applying the Smoothing Function

Effect of Edge Weighting on Smoothing ,[object Object],[object Object],[object Object],[object Object]

Scientific Workflows for Dynamic SNA ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Findings: Variations in Dynamics ,[object Object],[object Object]

Findings: Gaim ,[object Object],[object Object],[object Object],[object Object]

Findings: Fire ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Tracker Housekeeping Behavior ,[object Object],[object Object],[object Object],[object Object],Cleaning up before shutting down

Observations on Dynamics ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Conclusion ,[object Object],[object Object],[object Object],[object Object],[object Object]

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The investigation of any event or incident often involves the evaluation of physical evidence. Occasionally, a comparison is conducted between an evidentiary sample of unknown origin and that of an appropriate known sample. In a Denial of Service (DoS) attack, items of evidentiary value may cross the spectrum from anecdotes to useful information in firewall logs or complete packet captures. Because of the spoofed or reflective nature of DoS attacks, relevant information leading to the direct identification of the perpetrator is rarely available. In many instances, this underscores the significance of the investigator's ability to accurately identify the tool utilized by the suspect. For a DoS attack scenario, this would likely involve a commercially available stresser or criminal bot infrastructure. In this paper, we propose the concept of a DoS exemplar and determine if the comparison of evidentiary samples to an appropriate known sample of DoS attributes could add value in the investigative process. We also provide a simple tool to compare two DoS flows.

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6. Sliding Windows and Smoothing 90 days 30 days 30 days 90 day window, sliding forward by 30 days at a time

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8. Venue Volumes Over Time

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11. Applying the Smoothing Function

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