One Identity Safeguard for Privileged Sessions 7.5.1

One Identity Safeguard for Privileged Sessions (SPS) integrates data from SPS to use as the basis of user behavior analysis. SPA uses machine learning algorithms to scrutinize behavioral characteristics (using data from SPS), and generates user behavior profiles for each individual privileged user. SPA compares actual user activity to user profiles in real time, with profiles being continually adjusted using machine learning. When SPA detects unusual activity, this is indicated on the user interface of SPS in the form of high scores and visualized insight.

Prerequisites

Make sure that you have session data from network traffic that:

contains real, unique usernames linked to users other than root/administrator or a shared account

To check this, navigate to Sessions , and check whether the Username column contains data. This is important, because session data will be linked to users.

If you do not have unique usernames in your session data, review your authentication settings and consult with the One Identity Professional Services team to learn about your options to tie accounts to users.
has commands extracted (using lightweight or full indexing, or in real-time through content policies)

For instructions on how to configure indexing and include commands in the scope of indexing, see Indexing audit trails in the Administration Guide.

For details on how to configure real-time command extraction using a content policy, see Creating a new content policy in the Administration Guide.
has keystrokes extracted (using lightweight or full indexing, or in real-time through content policies)

The minimum required amount of data for reliable insight is 5 sessions with approximately 200 keystrokes each.

For instructions on how to configure indexing and include typing biometrics in the scope of indexing, see Indexing audit trails in the Administration Guide.

For details on how to configure real-time extraction of keystroke-related data using a content policy, see Creating a new content policy in the Administration Guide.
has pointing device (mouse) biometrics extracted (using lightweight or full indexing, or in real-time through content policies)

For instructions on how to configure indexing and include pointing device biometrics in the scope of indexing, see Indexing audit trails in the Administration Guide.

For details on how to configure real-time extraction of pointing device-related data using a content policy, see Creating a new content policy in the Administration Guide.
has window titles extracted (using lightweight or full indexing, or in real-time through content policies)

For instructions on how to configure indexing and include window titles in the scope of indexing, see Indexing audit trails in the Administration Guide.

For details on how to configure real-time window title extraction using a content policy, see Creating a new content policy in the Administration Guide.

The following describes how to analyze data using One Identity Safeguard for Privileged Analytics.

Limitations

SPS used in combination with SPA currently has the following limitations:

SPA requires at least 12GB RAM to operate. If you are interested in upgrading your appliance, contact our Support Team.
SPA requires a lot of computation, which can put pressure on SPS:
- The keystroke algorithm is much more resource-hungry than the other algorithms, therefore our recommendation is to start analyzing data using the algorithms that require less resources.
- Before you start using SPA, make sure that at least half the capacity of SPS is available.
SPA only analyzes audit trails and SPS metadata, it does not analyze log data.

To start using SPA

Start getting scores.

Scoring for sessions

Scoring happens in real-time, meaning that as soon as new data (even data from an ongoing session) is available, SPA immediately scores it.

TIP: When data is not immediately available to you and you are unable to wait until sufficient amount of data comes in from production traffic, you can resort to manually reindexing historical sessions. For details, see Reindex historical sessions in the Safeguard for Privileged Analytics Configuration Guide.

Scores represent an aggregated amount. Session data is scored by multiple algorithms independent from each other. Scores given by individual algorithms are aggregated to create a single score.

For detailed instructions on how to configure SPA, see Safeguard for Privileged Analytics Configuration Guide.

Scoring for users

The goal of the algorithm is to create a score for the user to represent recent activities. The algorithm does this by averaging recent event scores and weighing the top 3 highest scores and taking in consideration the elapsed time. The user score is calculated hourly and weighs more recent activities with a bias.
Search for sessions with high scores.
1. Go to Sessions .
  
  Sessions are displayed sorted by date. For ongoing sessions, the Search interface is updated in real-time to always show the most up-to-date information.
2. In the Search query field, type analytics.score.aggregated: [80 TO 100], and click Search.
  
  A score between 80 and 100 indicates unusual user behavior.
  
  Figure 277: Searching for sessions with unusual user behavior using a search query
  
  Results that show sessions with high scores are displayed.
  
  Figure 278: Sessions with high scores — table view
  
  Figure 279: Sessions with high scores — card view
Alternatively, search for scripted sessions.

In the Search query field, type analytics.scripted:true, and click Search.

The screen content is first indexed, then processed with the search backend, and finally, the filter expressions are applied. This process is described in detail in the following sections.

Figure 280: The search and filter process

Prerequisites - Indexing phase

First, as a prerequisite of the search process, screen content is indexed. The indexing phase generates a database that the search and filter processes will run on.

The indexer parses the audit trail files, and builds an "inventory" of the privileged user's activity data based on what appeared on their screen.

In the case of a terminal session, screen content corresponds to the activity data that is captured in a terminal window. In the case of graphical protocols, screen content is whatever is visible in the graphical user interface of the applications the user is interacting with. In the latter case, the indexer's Optical Character Recognition (OCR) engine extracts text that appeared on the screen (for example, window titles).

NOTE: If a piece of text is displayed for less than 1 second, it is not extracted.
The indexer returns the information extracted from the parsed audit trail files to One Identity Safeguard for Privileged Sessions (SPS). In the case of a terminal session, the captured text is put in the backend database as one document per one second of screen content. Because of this, the content that you have searched for might only partially appear in the screenshot. In the case of graphical protocols, the captured text is put in the backend database as one document per screenshot.
The queries will be run on this database during the search process.

For details on indexing, see Indexing audit trails.

Search and filter process phases

The search and filter process consists of three major phases:

Query phase
Grouping phase
Filter phase

Query phase

In the query phase, the backend ranks and then limits the number of results.

The result of one query is the top 3000 documents, ordered by the default ranking system of the backend.

This means that if there are more than 3000 results, those of the lowest rank will not be passed to the next phase at all.

The ranking system cannot be modified, so there is no way to "upvote" those results of lower ranks.

If you want to ensure that all important results are passed to the grouping phase, use a smaller time range that you run the query on. If there are fewer than 3000 results, it is certain that the events you are interested in will be included in the grouping phase.
The grouping phase receives the results.

Grouping phase

The grouping phase groups the results that were passed on from the query phase.

First, the results with the same trail IDs are grouped together. A trail ID group contains all search hits that are in that trail.
The trail ID groups are then further grouped by seach expression and time range. This group is essentially the time range during which the expression is displayed on the screen (for example, if the text root is displayed from 00:00:12 to 00:01:45, this will be one group).
This grouped result is displayed in the search screen as one row.

Filter phase

The filter phase applies filter expressions to these grouped results.

NOTE: If there were screen content search results that were excluded during the query phase, the filter expressions will not be applied to them.

Example: Filtering for search results that were excluded in the query phase

For example, if you want to filter for Telnet connections where the text root was displayed, the following can happen:

You search for the Screen content: root. There are 3100 search results that consist of 3050 SSH connections and 50 Telnet connections. In this example, Telnet connections received the lowest ranks for some reason. 100 results that have received the lowest rank are excluded, and in this example it means all Telnet connections.

If you filter for protocol Telnet now, you will not see any results.

To remedy this situation, try searching in a smaller time range to make sure that there are less than 3000 search results. If you are unsure about the time range, you might want to attempt fine-tuning the backend search manually. For details, see: Fine-tuning the backend search manually.

Fine-tuning the backend search manually

You can fine-tune your search manually with the command line utility lucenectl. To do this, log on to the core shell. For details, see Accessing the One Identity Safeguard for Privileged Sessions (SPS) console.

Specify more exact time ranges (use Unix timestamps).

For example, to limit the time range to Thursday, June 30, 2016 11:39:51 AM - Thursday, November 3, 2016 2:44:46 PM, enter the following command:
```
indexerctl search --from-to 1467286791 1478184286 --text remote --limit 3000 --aggregate-by-trail --normalize-rank
```
NOTE: For converting timestamps to Unix timestamp, use https://www.epochconverter.com/.
Increase the query limit of 3000 to a limit of your choice.

For example, to increase the query limit of 3000 to 4500, enter the following command:
```
lucenectl search --from-to 1467286791 1478184286 --text <your-screen-content-search-expression> --limit 4500 --aggregate-by-trail --normalize-rank
```
```
lucenectl search --from-to 1467286791 1478184286 --text remote --limit 4500 --aggregate-by-trail --normalize-rank
```
NOTE: If you do not receive more results with a larger query limit, it means that you have found all results with your search expression.

However, the downside of using lucenectl to fine-tune your search is that after the cli search, you have to manually extract the trails that you find interesting with the help of the metadb.

The following example shows the output of a lucenectl search:

{
 "hits": [
   {
     "hits_count": 1,
     "channel_id": 1,
     "trail_id": "58",
     "rank": 0.4068610216585047
   },
   {
     "hits_count": 7,
     "channel_id": 761,
     "trail_id": "12",
     "rank": 1.0
   },
   {
     "hits_count": 2,
     "channel_id": 1,
     "trail_id": "139",
     "rank": 0.5923645275802537
   }
 ]
}

rank: the larger the number, the higher the rank
hits_count: the number of times the screen content search expression is displayed in the audit trail
trail_id: the ID of the trail
channel_id: the ID of the channel

The most relevant audit trail will probably be the one with the highest rank.

If you have determined which audit trail you are interested in, enter the following command. The value of _connection_channel_id will be the value of the trail_id from the lucenectl output that you have determined as most relevant.

psql -U scb scb -c "select audit from channels where _connection_channel_id = 12;"

The output of this command will be:

/<audittrailpath>/audit-scb_rdp-1467274538-0.zat:2
/<audittrailpath>/audit-scb_rdp-1467274538-0.zat:1

From this output, the audit trail file name path is as follows: /<audittrailpath>/audit-scb_rdp-1467274538-0.zat

NOTE: If you cannot find the file at the path, check whether it has been archived and search for the file in the archive path. Use the following command:

psql -U scb scb -c "select audit, _archive_path  from channels where _connection_channel_id = 12;"

The output of this command will be:

                    audit                                | _archive_path
---------------------------------------------------------+---------------
/<audittrailpath>/audit-scb_rdp-1467274538-0.zat:2 | /<myarchive/path>/
/<audittrailpath>/audit-scb_rdp-1467274538-0.zat:1 | /<myarchive/path>/
                                                         |

If you still cannot find the audit trail, contact our Support Team.

One Identity Safeguard for Privileged Sessions 7.5.1 - Administration Guide

Audit trail downloads information on the Search interface

Prerequisites

Displayed fields

Displaying statistics on search results

Analyzing data using One Identity Safeguard for Privileged Analytics

Prerequisites

Limitations

The search and filter process

Prerequisites - Indexing phase

Search and filter process phases

Query phase

Grouping phase

Filter phase

Example: Filtering for search results that were excluded in the query phase

Fine-tuning the backend search manually

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One Identity Safeguard for Privileged Sessions 7.5.1 - Administration Guide

Audit trail downloads information on the Search interface

Prerequisites

Displayed fields

Displaying statistics on search results

Analyzing data using One Identity Safeguard for Privileged Analytics

Prerequisites

Limitations

The search and filter process

Prerequisites - Indexing phase

Search and filter process phases

Query phase

Grouping phase

Filter phase

Example: Filtering for search results that were excluded in the query phase

Fine-tuning the backend search manually