Create r4.2/readme.txt

r4.2/readme.txt

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+Release 4, Dataset 2 Notes
+
+Major Changes
+* Content is integrated with the graph structure.
+* A user's topics of interest can drift over time.
+* Email now includes CC/BCC.
+* Email table now includes user ID and PC.
+* Users can have one or more non-work email addresses.
+* A latent job satisfaction variable was added. It might make sense for us to specify exactly how this affects observable variables, so let us know if that information is desired.
+* An additional red team scenario was added. (All previous red team scnearios also occur in the dataset.)
+* This is a "dense needles" dataset. There is an unrealistically high amount of red team data interspersed.
+
+
+license.txt
+* ExactData license information
+
+
+logon.csv
+* Fields: id, date, user, pc, activity (Logon/Logoff)
+* Weekends and statutory holidays (but not personal vacations) are included as days when fewer people work.
+* No user may log onto a machine where another user is already logged on, unless the first user has locked the screen.
+* Logoff requires preceding logon
+* A small number of daily logons are intentionally not recorded to simulate dirty data.
+* Some logons occur after-hours
+  - After-hours logins and after-hours thumb drive usage are intended to be significant.
+* Logons precede other PC activity
+* Screen unlocks are recorded as logons. Screen locks are not recorded.
+* Any particular user’s average habits persist day-to-day
+  - Start time (+ a small amount of variance)
+  - Length of work day (+ a small amount of variance)
+  - After-hours work: some users will logon after-hours, most will not
+* Some employees leave the organization:  no new logon activity from the default start time on the day of termination
+* 1k users, each with an assigned PC
+* 100 shared machines used by some of the users in addition to their assigned PC. These are shared in the sense of a computer lab, not in the sense of a Unix server or Windows Terminal Server.
+* Systems administrators with global access privileges are identified by job role "ITAdmin".
+* Some users log into another user's dedicated machine from time to time.
+
+
+device.csv
+* Fields: id, date, user, pc, activity (connect/disconnect)
+* Some users use a thumb drive
+* Some connect events may be missing disconnect events, because users can power down machine before removing drive
+* Users are assigned a normal/average number of thumb drive uses per day. Deviations from a user's normal usage can be considered significant.
+
+
+http.csv
+* Fields: id, date, user, pc, url, content
+* Has modular/community structure, but is not correlated with social/email graph.
+* Domain names have been expanded to full URLs with paths.
+* Words in the URL are usually related to the topic of the web page.
+* Content consists of a space-separated list of content keywords.
+* Each web page can contain multiple topics.
+* WARNING: Most of the domain names are randomly generated, so some may point to malicious websites. Please exercise caution if visiting any of them.
+
+
+email.csv
+* Fields: id, date, user, pc, to, cc, bcc, from, size, attachment_count, content
+* Driven by underlying friendship and organizational graphs.
+* Role (from LDAP) drives the amount of email a user sends per day.
+* The vast majority of edges (sender/recipient pairs) are exist because the two users are friends.
+* A small number of edges are introduced as noise. A small percentage of the time, a user will email someone randomly.
+* Emails can have multiple recipients
+* Emails can have a mix of employees and non-employees in dist list
+* Non employees use a non-DTAA email addresses; employees use a DTAA email address
+* Terminated employees remain in the population, and thus are eligible to be contacted as non-employees
+* A friendship graph edge is not implied between the multiple recipients of an email.
+* Unlike the previous release, we do not believe the observed email graph follows graph power laws
+  because the power-law-conforming friendship graph is overwhelmed by the organizational graph.
+* Email size and attachment count are not correlated with each other.
+* Email size refers to the number of bytes in the message, not including attachments.
+* Content consists of a space-separated list of content keywords.
+* "Content" does not specifically refer to the subject or body. We have not made that distinction.
+* Each message can contain multiple topics.
+* Message topics are chosen based on both sender and recipient topic affinities.
+
+
+file.csv
+Fields: id, date, user, pc, filename, content
+* Each entry represents a file copy to a removable media device.
+* Content consists of a hexadecimal encoded file header followed by a space-separated list of content keywords
+* Each file can contain multiple topics.
+* File header correlates with filename extension.
+* The file header is the same for all MS Office file types.
+* Each user has a normal number of file copies per day. Deviation from normal can be considered a significant indicator.
+
+
+psychometric.csv
+* Fields: employee_name, user_id, O, C, E, A, N
+* Big 5 psychometric score
+* See http://en.wikipedia.org/wiki/Big_Five_personality_traits for the definitions of O, C, E, A, N ("Big 5").
+* Extroversion score drives the number of connections a user has in the friendship graph.
+* Conscientiousness score drives late work arrivals.
+* This information would be latent in a real deployment, but is offered here in case it is helpful.
+* A latent job satisfaction variable drives some behaviors.
+
+Malicious actors
+* This data contains two instances of insider threats.
+* Data dimensions that are fair game for anomaly detection (not all are used in red team scenarios)
+  - In general, radical changes in behavior
+  - Unusual logon times (for that user)
+  - Unusual logins to another user's dedicated machine (for users that don't do this normally)
+  - Device usage for users who aren't normally device users, or increased device usage for those that are.
+  - Radical increases in the amount of device usage by a user
+  - Employee termination (as an indicator, but not anomaly detection per se)
+  - Number of emails sent / day
+  - Change in web browsing habits (visits to unusual websites are interesting, but also common)
+  - Radical change in social graph behavior (unexpected email recipients, perhaps)
+  - Topics of web sites visited, emails, and files copied.
+* We can reveal as much as you would like about the red team scenarios.
+* This is a "dense needles" dataset. There is an unrealistically high amount of red team data interspersed.
+
+Errata:
+* Field Ids are unique within a csv file (logon.csv, device.csv) but may not be globally unique.