|
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. |
|
|