| 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. | |