C1000-162 IBM Security QRadar SIEM V7.5 Analysis Questions and Answers

 Understanding Offense Management: In QRadar, offenses are generated based on predefined rules and correlations. When legitimate traffic is identified as an offense, it's essential to adjust configurations to prevent future false positives.

 Managing Legitimate LDAP Traffic:

Building Blocks: QRadar uses building blocks to group similar types of data. The BB

Definition: LDAP Servers building block is used to identify and manage LDAP servers within the network.

Excluding Legitimate Traffic: By adding the IP address of the legitimate LDAP server to this building block, QRadar will recognize the traffic as expected and exclude it from generating offenses.

 Implementation Steps:

Navigate to the QRadar console.

Go to the Admin tab and select Building Blocks.

Find and edit the BB

Definition: LDAP Servers building block.

Add the IP address of the LDAP server to this building block.

 Reference Confirmation: According to IBM QRadar documentation, adding the IP address of the LDAP server to the appropriate building block (BB

Definition: LDAP Servers) is the recommended method to handle such scenarios.

References:

http://www.ibm.com/support/knowledgecenter/en/SS42VS_7.2.7/com.ibm.qradar.doc/shc_qradar_comps.html

Here's why "Am I Affected" is the most suitable answer among the given options:

Am I Affected (AIA): The "Am I Affected" feature on the IBM X-Force Exchange is designed specifically to help you determine if your systems have observed Indicators of Compromise (IOCs) related to a specific threat or campaign.

COVID-19 IOCs: If you have a set of IOCs (e.g., IP addresses, domain names, file hashes) associated with COVID-19-themed attacks, you can use the AIA feature to query QRadar and see if any were detected within your network.

Reasons Why Other Options Are Less Ideal:

TAXII Automatic Updates: This focuses on automatically pulling threat intelligence feeds into QRadar, not retrospective searches for past IOC presence.

STIX Bundle: A STIX bundle is a structured way to represent threat intelligence.expand_more It wouldn't directly tell you if those indicators have been seen in your QRadar data.

Threat Intelligence ATP: This likely refers to a broader threat intelligence platform, not a specific X-Force Exchange feature for checking QRadar data.

When a security analyst needs to filter events based on the time they occurred on the endpoints, the most relevant parameter to use is "Log Source Time." This parameter reflects the original timestamp of an event as recorded by the log source, providing the actual time when the event took place on the endpoint, regardless of when the event was received or processed by QRadar. This is crucial for accurate temporal analysis of events, ensuring that the timing of activities is correctly aligned with the actual occurrence on the devices or systems generating the logs.

In Rule Response for Offense Naming, QRadar provides options to either contribute to or set/replace the name of the associated offenses. These options allow for dynamic naming of offenses based on event name information, facilitating easier identification and categorization of offenses​​.

A YARA rule is used for malware identification and classification, based on textual or binary patterns. The example provided suggests a rule that flags occurrences of a specific string (str1) at a precise location within a file. The "offset" keyword in YARA rules specifies the exact byte position where the pattern (in this case, 'str1') should appear. Thus, the correct interpretation of the YARA rule example is that it flags instances where 'str1' appears 25 bytes into the file, indicating a very specific pattern match used for identifying potentially malicious files or activities that conform to this pattern.

IOCs and Reference Sets: Reference sets are specifically designed to store lists of Indicators of Compromise (IOCs) like IP addresses, domain names, file hashes, etc.

Correlation and Matching: QRadar can match events and flows against data in reference sets, triggering rules or alerts when suspicious activity is detected.

AQL Focus: AQL is QRadar's search language primarily used for analyzing:

Log Activity: The core area to search events received from various log sources.

Offenses: Offenses are generated based on rule triggering, and you can search them to investigate patterns.

Context Menu: Right-clicking on an IP address in the Offense Summary window offers quick investigation actions.

IP-Related Tools: WHOIS and DNS Lookups are essential tools for:

WHOIS: Retrieving IP registration information (owner, contact details, etc.). DNS: Resolving domain names associated with the IP.

Vulnerability Scans and Offenses: VA scanners frequently trigger alerts as their activity can resemble malicious behavior.

Host Definitions: This QRadar building block group helps define known hosts, including their attributes and roles on the network.

Adding to Definitions: Including the VA scanner's IP in the host definitions allows QRadar to recognize it and properly categorize its activity.

Identify a value from the event payload that will be used as the basis for this threat detection. You must first determine the specific piece of information within the log payload that signals the malware outbreak activity you want to detect.

Create a custom property to extract the value from the logs. QRadar needs a custom property to isolate this specific value from the raw log data in a structured way.

Ensure the custom property is optimized and enabled. Optimize the custom property's extraction method for accuracy and efficiency. Ensure it's enabled, so QRadar actively parses this data element.

Create and Configure a rule to create an offense that uses the custom property as the offense index field. Now that the custom property is ready, create a rule that references this property. Designate the custom property as the rule's offense index field to ensure offenses are correctly grouped based on the extracted malware indicator.

A screenshot of a computer Description automatically generated

When you right-click on an IP address within an event in the QRadar Log Activity tab, you get a context-sensitive menu with these primary options:

Filter on: This is the main way to focus your view. It adds the selected IP address as a filter, showing you only events associated with that IP.

False Positive: Marking an event as a false positive helps QRadar's analytical engine learn and potentially reduce similar alerts in the future.

More Options: This expands the menu to show further actions you might take on the event such as:

Adding the IP to a reference set

Running an AQL query

Executing a custom action

Searching in other areas of QRadar using the IP address.

Quick Filter: Provides a quick, inline way to add additional filtering logic based on other fields of the event.

References:

IBM QRadar Log Activity Tab Overview: This section of the QRadar documentation describes the actions available in the Log Activity tab: https://www.ibm.com/docs/SSKMKU/com.ibm.qradar.doc/c_qradar_log_activ_tab_over

When an analyst wants to combine multiple extraction and calculation-based properties into a single property, such as URLs, virus names, and secondary user names, an AQL-based property should be used. AQL (Ariel Query Language)-based properties allow for the aggregation of diverse data types into a unified custom property, facilitating more flexible and comprehensive data analysis within QRadar​​.

Report Data Source: To generate a report focused on offense data, you must explicitly select "Offense" as the data source. This tells QRadar to structure the report around offense information.

Edit Search: The "Edit Search" interface often provides the ability to configure report generation.

Here's why MaxMind updates are essential:

IP to Location Mapping: QRadar relies on a GeoIP database to translate IP addresses into geographical locations (countries, regions, cities, etc.).

MaxMind: A widely used provider of GeoIP databases. QRadar integrates with MaxMind to obtain this data.

Fresh Updates: GeoIP mapping can change over time. Regular updates ensure the accuracy of location-based rules.

Why Other Options Are Less Relevant

X-Force Exchange: Provides threat intelligence feeds, primarily focused on IOCs, not geographic mappings.

X-Force Exchange ATP Updates: Likely refers to threat intelligence updates but not specifically for geolocation data.

Watson: IBM's AI platform. While potentially related to analytics, it's not the primary mechanism for geolocation in QRadar.

The example provided refers to a "Reference table," which is a type of reference data collection in QRadar that can store complex structured data. A reference table allows for multiple keys and values, supporting the storage of data like Usernames, Source IPs with a specific data type (e.g., cidr for IP addresses), and Source Ports as values​​.

The information displayed when pressing “Ctrl + Space” in the search field in the Log Activity tab in QRadar is not explicitly mentioned in the search results. However, in general, this shortcut is often used in various software and platforms to display a list of available commands, functions, or properties. In the context of QRadar, it’s likely that pressing “Ctrl + Space” in the search field would display a list of available AQL (Ariel Query Language) databases, functions, and fields (properties).

In QRadar, to limit the time period for which an AQL (Ariel Query Language) query is evaluated, the functions and clauses that can be used include START, STOP, LAST, NOW, and PARSEDATETIME. Specifically, the LAST function is used to define a relative time range for the query, such as "LAST 2 DAYS"​​.

Understanding Time Series Charts in QRadar: Time series charts in IBM QRadar are used to plot data points over time. The axes of these charts are crucial as they define how data is represented and interpreted.

Types of Axis:

Linear Axis: A linear axis displays data points equally spaced along the axis. This is useful for evenly distributed data and straightforward trends.

Logarithmic (Log) Axis: A log axis represents data on a logarithmic scale. This is useful for data that spans several orders of magnitude and for visualizing exponential trends.

Selection of Axis Types: When creating time series charts in QRadar, users can choose from various axis types to best represent their data. The linear and log axes are commonly used due to their effectiveness in displaying a wide range of data types and trends.

Reference Confirmation: According to IBM QRadar documentation, the linear and logarithmic axes are supported for time series charts, making them the correct choices.

References:

IBM QRadar documentation on charting options and axis types confirms the availability of linear and logarithmic axes.

Widget chart types - IBM Documentation

Indexing Principle: QRadar creates indexes on default properties to quickly locate data matching your queries.

Lookup vs. Scan: Instead of scanning all raw data, QRadar utilizes the index like a 'phonebook' for targeted lookups.

Optimization: Searching using indexed properties dramatically decreases the amount of data QRadar needs to process.

Understanding Offense Creation in QRadar: QRadar SIEM generates offenses based on the correlation of various types of information to detect potential security threats and incidents.

Analyzed Information for Offense Creation:

Incoming Events and Flows: QRadar collects and analyzes incoming log events and network flows to identify suspicious activities.

Asset Information: Information about the assets within the organization, including their roles and vulnerabilities, is crucial for accurate threat detection.

Known Vulnerabilities: QRadar uses data about known vulnerabilities to correlate events and determine if a potential threat is exploiting these vulnerabilities.

Relevance of the Selected Information: The combination of incoming events, flows, asset information, and known vulnerabilities provides a comprehensive view that helps QRadar accurately identify and correlate potential security incidents, resulting in the creation of offenses.

Reference Confirmation: According to IBM QRadar documentation, the correct combination of analyzed information for creating offenses includes incoming events and flows, asset information, and known vulnerabilities.

References:

IBM QRadar documentation on offense creation and analysis confirms the use of incoming events, flows, asset information, and known vulnerabilities.

Understanding Scatter Charts in QRadar Pulse: QRadar Pulse is a visualization application used to create and view different types of charts for better data analysis and interpretation.

Types of Y-Axis:

Linear Axis: This type of axis displays data points at equal intervals. It's suitable for evenly distributed data and shows trends in a straightforward manner.

Logarithmic (Log) Axis: This axis type displays data on a logarithmic scale, which is useful for data that covers several orders of magnitude or for data that grows exponentially.

Selection for Scatter Charts: When creating scatter charts in QRadar Pulse, the application allows users to choose between linear and logarithmic (log) Y-axis types to best represent their data.

Reference Confirmation: According to IBM QRadar documentation, both linear and logarithmic Y-axis types are supported for scatter charts in the Pulse app, making them the correct answers.

References:

IBM QRadar documentation on Pulse app charting options confirms the availability of linear and logarithmic Y-axis types.

In QRadar, when performing a search in the My Offenses or All Offenses tabs, valid values for the Offense Type field include "Any" and "Source IP". "Any" searches all offense sources, while "Source IP" allows for searching offenses with a specific source IP address​​.

To search offense data on the By Networks page, an analyst can use the options "Events/Flows" to filter based on the types of data points, and "Network" to specify the network they want to search for. This allows for a focused search on specific networks and types of data​​​​.

App Communication: QRadar apps often communicate with the core QRadar system using APIs or other internal communication channels.

Authorization: Authorized service tokens provide a secure mechanism for apps to authenticate these actions, ensuring proper access and data flow.

Dashboard Data Refresh: Most widgets on QRadar dashboards typically refresh the displayed data every minute by default.

Customization: In some cases, you might be able to configure this refresh interval depending on the widget type.

In QRadar, "unknown events" refer to data that is collected and parsed by the system but cannot be accurately mapped or categorized to a specific log source due to lack of sufficient information or matching criteria. On the other hand, "stored events" imply that the data has been retained in the system but may not be fully understood or parsed by QRadar, possibly due to it not conforming to expected formats or lacking recognizable patterns. This distinction highlights the challenges in data categorization and analysis within a SIEM system, where not all collected data can be immediately attributed to known sources or fully analyzed due to various constraints .

The default setting for the System Notification dashboard is to display 10 notifications, providing a manageable overview of system alerts and issues. Users can adjust this setting to view fewer or more notifications based on their preferences​​.

In IBM Security QRadar SIEM, building blocks are utilized to categorize assets and server types into CIDR/IP ranges to either exclude or include entire asset categories in rule tests. The most suitable type of building block for this purpose is the "Host definition". This type of building block allows administrators to define groups of IP addresses, often in CIDR notation, to represent different parts of the network, such as specific servers, subnets, or entire network segments. By doing so, rules can be crafted to apply only to traffic involving these defined hosts, thereby including or excluding specific asset categories from rule tests based on their network location or role within the organization​​.

Offense Summary Window: The Offense Summary window provides detailed information about a specific offense.

Display Menu: Within this window, the "Display" menu offers options to customize what information is shown.

Rules Option: Selecting "Display > Rules" will reveal a list of rules that contributed to the chained offense sequence.

References

IBM QRadar Documentation - Offense Summary: [invalid URL removed]

IBM QRadar Documentation: Offense Chaining https://www.ibm.com/docs/en/qsip/7.4?topic=management-offense-chaining

When searching for all events related to "Login Failure," a security analyst should use the Event Name parameter to filter the events. This allows the analyst to specifically target events with descriptions such as "Database Login Failure," which indicates that a database login attempt failed​​​​.

QRadar will mark an Event offense as dormant if no new events or flows occur within 30 minutes. However, if QRadar did not process any events within 4 hours, this also triggers the offense to become dormant. Once dormant, the offense remains in this state for 5 days unless new events or flows are added​​​​.

The magnitude rating of an offense in QRadar is calculated based on relevance, severity, and credibility. Relevance determines the impact on the network, credibility indicates the integrity of the offense, and severity represents the level of threat. QRadar uses complex algorithms to calculate and periodically re-evaluate the offense magnitude rating​​.

To identify events that were missed by the Custom Rule Engine (CRE) in IBM Security QRadar SIEM, an analyst would primarily look for "Log Only Events sent to a Data Store" and "High Level Category Unknown Events." Log Only Events are those that are stored directly without being processed by the CRE, indicating they might have been overlooked or not matched by any existing rules. High Level Category Unknown Events are those that do not fit into any of the predefined categories in QRadar, suggesting that the CRE might not have rules to handle or categorize these events properly. These types of events are crucial for analysts to review to ensure that no significant incidents are missed and to refine the rule set for better detection in the future.