Displaying statistics on search results
You can quickly sort and visualize the distribution of the sessions based on their various metadata, for example, username, server address, and so on.
To display statistics on search results
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Click the 
icon.
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Select the type of metadata you want to create statistics on from the Value distribution based on field, for example, select Username to display sessions based on username.
Figure 269: Audit > Sessions — Displaying statistics
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To exclude items from the pie chart, click the 
icon next to the metadata you want to exclude.
For example, if you want to exclude results by a user called testbot, select the icon next to the item.
Figure 270: Audit > Sessions — Excluding items from the pie chart
The pie chart now does not display results for the excluded item. The percentages always add up to 100%.
You can continue to restrict or refine your search results and view statistics as required.
Analyzing data using One Identity Safeguard for Privileged Analytics
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:
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contains real, unique user names 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 user names 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.
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has commands extracted (using lightweight or full indexing, or in real-time through content policies)
For more information 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.
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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 more information 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.
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has pointing device (mouse) biometrics extracted (using lightweight or full indexing, or in real-time through content policies)
For more information on how to configure indexing and include pointing device biometrics in the scope of indexing, see Indexing audit trails in the Administration Guide.
For more information 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.
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has window titles extracted (using lightweight or full indexing, or in real-time through content policies)
For more information on how to configure indexing and include window titles in the scope of indexing, see Indexing audit trails in the Administration Guide.
For more information on how to configure real-time window title extraction using a content policy, see Creating a new content policy in the Administration Guide.
SPA limitations
SPS used in combination with SPA currently has the following limitations:
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SPA requires at least 12GB RAM to operate. If you are interested in upgrading your appliance, contact our Support Team.
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SPA requires a lot of computation, which can increase the load on SPS:
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The keystroke algorithm requires significantly more resources than the other algorithms. As a result One Identity recommends that you start analyzing data using the algorithms that require less resources.
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Before you start using SPA, make sure that at least half the capacity of SPS is available.
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SPA only analyzes audit trails and SPS metadata, it does not analyze log data.
Algorithm limitations
Generating more extracted data from sessions results in better analytics score calculations. However, you must meet certain algorithm-specific limitations. These limitations are necessary to ensure that the appliance stays operational even under high load.
Algorithms with limits:
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window title
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host login
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keystroke
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mouse
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command
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FIS
Window title and host login
SPS cannot build the baseline for these algorithms if the number of users observed by the appliance is over 4096.
Keystroke, mouse and command
These algorithms generate extracted data from every analyzed session.
SPS cannot build the baseline for a user for these algorithms if the collected extraction data amount is over 128MB in the last 90 days. Since these algorithms are very complex, it is not possible to estimate how much activity is needed to reach this threshold.
FIS
SPS cannot build the FIS baseline if there are more than 20000 recorded sessions in the last 90 days for a user.
The following describes how to analyze data using One Identity Safeguard for Privileged Analytics.
To start using SPA
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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 more information, 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.
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Search for sessions with high scores.
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Go to Sessions.
Sessions are displayed sorted by date. For ongoing sessions, the Search interface is updated in real time to always display the most up-to-date information.
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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 271: Searching for sessions with unusual user behavior using a search query
Results that show sessions with high scores are displayed.
Figure 272: Sessions with high scores — table view
Figure 273: Sessions with high scores — card view
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Alternatively, search for scripted sessions.
In the Search query field, type analytics.scripted:true, and click Search.
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View the details of a session.
To view details of a session, click 
.
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View session analytics.
Click the Analytics tab.
The top of the page displays a summary of key insights about the session, such as:
The Anomalies found and Normal behavior sections of the page display detailed analyses provided by each of the configured algorithms. This includes short information on how a particular algorithm works and how to read the visualized insight, as well as scores given by the individual algorithms.
Figure 274: Audit > Sessions — Viewing details on the Analytics tab: Anomalies found
Figure 275: Sessions — Viewing details on the Analytics tab: Normal behavior
The search and filter process
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 276: 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.
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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.
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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.
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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:
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Query phase
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Grouping phase
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Filter phase
Query phase
In the query phase, the backend ranks and then limits the number of results.
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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.
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The grouping phase receives the results.
Grouping phase
The grouping phase groups the results that were passed on from the query phase.
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First, the results with the same trail IDs are grouped together. A trail ID group contains all search hits that are in that trail.
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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).
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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: The search and filter process.
Viewing session details
View the session details of each session for in-depth information on each of the indexed session stored in the connection database. You can use it to gain contextual insight about the indexed session and its events.
You can view session details for data recorded by:
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One Identity Safeguard for Privileged Sessions (SPS). For more information, see Viewing session details for data recorded by SPS in the Administration Guide.
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One Identity Safeguard for Privileged Passwords (SPP). For more information, see Viewing session details for data recorded by SPP in the Administration Guide.
Frequent Item Set (FIS) flow view visuals
Frequent Item Set (FIS) flow view visuals are also available on the Analytics tab. The FIS flow view is similar to the flow view analytics overview, except that the FIS flow view only displays data narrowed down to a single user's previous sessions in the analysis period (which is the previous 90 days by default). For more information, see Visualizing Frequent Item Sets on the FIS flow view.
Session tags
Session tags allow you to get basic information about the session and its contents at a glance.
Scripted session tag: One Identity Safeguard for Privileged Sessions (SPS) currently supports the scripted session tag. SPS uses One Identity Safeguard for Privileged Analytics to detect if sessions are generated using human interaction or automation. If sessions are generated using automation, SPS displays the scripted tag in the search interface as shown below:
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Scripted sessions are shown on the main search screen.
Figure 277: Scripted sessions — cards view
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Scripted sessions are shown on the Overview tab.
Figure 278: Scripted sessions — Overview tab
