IBM C1000-162 IBM Security QRadar SIEM V7.5 Analysis Exam Practice Test

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​​.

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 

Pie Chart Logic: Pie charts represent proportions of a whole.expand_more QRadar Pulse supports the following aggregations suitable for this:

Total (Sum): Calculates the sum of a selected field's values, displaying each slice relative to the whole.

First: Takes the first value encountered in a field, useful for categorical data to show initial distribution.

In the Dynamic Search window on the Admin tab of QRadar, the available data sources include "Assets" and "Offenses." These options allow administrators and analysts to construct queries based on asset information or offense data, enabling targeted searches and analyses tailored to specific security concerns within the organization​​.

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 supports different types of content extensions that can be downloaded from the IBM X-Force Exchange portal. Among the supported content extensions are "Custom Functions" and "Offenses." These extensions allow for enhanced functionality and customization within QRadar, providing users with the ability to tailor the system to specific security needs and requirements​​.

Here's why this is the correct statement:

Purpose of the Assets Tab: The Assets tab is QRadar's central repository for information about discovered assets on your network.expand_more Assets include network devices, servers, applications, and more.

Discovery Process: QRadar discovers assets by passively analyzing log and flow data, as well as through active scans if configured.

Searchable Columns: In QRadar's "My Offenses" and "All Offenses" tabs, you can search by:

Source IPs: Filter offenses based on the originating IP address. Id: Search using the unique offense ID number.

Incorrect Options:

Impact, Relevance, Weight: These are offense attributes, but you often filter by them rather than directly searching within the column data.

References:

IBM QRadar Documentation - Searching for Offenses 

In IBM Security QRadar SIEM V7.5, to search for all events containing a specific keyword such as "access", an analyst should navigate to the "Log Activity" tab. This section of the QRadar interface is dedicated to viewing and analyzing log data collected from various sources. By running a quick search with the "access" keyword in the Log Activity tab, the analyst can filter out events that contain this term in any part of the log data. This functionality is crucial for identifying specific activities or incidents within the vast amounts of log data QRadar processes, allowing analysts to quickly hone in on relevant information for further investigation or action.

The AQL (Ariel Query Language) statement provided would return all fields from the 'events' table where the 'username' column contains the string 'ERS', regardless of case. The 'ILIKE' operator in AQL is used for case-insensitive pattern matching, which means that it will match 'ers', 'Ers', 'ErS', etc​​.

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​​​​.

In the context of YARA rules, which are used for malware identification and classification, this example rule is designed to flag content that matches specific conditions. The rule named "ibm_forensics" contains both hexadecimal and string conditions. The Shex1 represents a hexadecimal string pattern, and Sstr1 represents a literal string "IBM Security!". The condition Shex1 and (#str1 > 3) means that for the rule to match, both the hexadecimal pattern must be present, and the string "IBM Security!" must appear more than three times within the scanned content. YARA rules are a powerful tool in forensics and malware analysis, allowing researchers and analysts to define complex patterns and conditions that identify malicious or suspicious content within files, memory, or network traffic.

Indexing: QRadar indexes certain fields to create a structured way to quickly locate matching data.

Search Optimization: Including indexed fields in queries allows QRadar to leverage pre-built indexes rather than scanning all data.

Filtering: A well-constructed search with indexed fields significantly narrows the dataset, speeding up operations.

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.

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.

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

The Ariel Query Language (AQL) is the internal structured language used in QRadar for interacting with the Ariel database, which stores event and flow data. AQL allows users to perform complex queries to extract, filter, and analyze this data, enabling detailed investigations and insights into security incidents and network activity. By using AQL, analysts can tailor their queries to meet specific informational needs, making it a powerful tool for data extraction and manipulation within the QRadar environment​​.

In the Reports tab of QRadar, the message "Queued (position in the queue)" indicates that the report is queued for generation. The message provides the position of the report within the generation queue, which helps users understand the report's status and expected generation time​

Rules that have tests against both event and flow data in QRadar are typically known as "Anomaly rules." These rules are designed to detect unusual or unexpected patterns of activity that deviate from the norm, which can be indicative of security threats. By analyzing both event data (which could include log entries, system alerts, etc.) and flow data (which represents network traffic), anomaly rules can provide a comprehensive view of potential security incidents, identifying anomalies that might not be evident when looking at event or flow data in isolation.

Offense Summary Window: Provides a centralized view of offense details.

Event/Flow Count Column: This column displays the number of events (and potentially flows) that contributed to the offense.

Accessing Events: Clicking on the number in this column typically opens a list or detailed view of the associated events.

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.

Quick Filter Behavior: The Quick Filter heavily restricts search results to items matching the keywords you've entered. If your valid AQL doesn't match the Quick Filter, you won't get results.

Disabling to Verify: The easiest way to confirm this is to temporarily disable the Quick Filter and rerun your AQL search.

In QRadar, when you save search criteria, especially on the Offenses tab, the configured search criteria are retained for future use and do not expire. This permanence ensures that users can quickly access and reuse their preferred search configurations, thereby streamlining the process of monitoring and investigating offenses over time​​.

Behavioral rule test parameters in QRadar SIEM are crucial for configuring how anomaly detection functions within rules. Here's a breakdown of each parameter:

Season: This is the most important parameter. It defines the historical time period used to establish a baseline of "normal" behavior. Consider the nature of the traffic you're monitoring when choosing a season:

Network traffic with human interaction: A season of 1 week might be appropriate.

Daily patterns: A season of 24 hours would be more suitable.

Current traffic level: Represents the current value of the property being monitored by the rule (e.g., number of login failures, bandwidth usage, etc.).

Predicted value: This is an estimation of what the traffic level "should" be, based on the established season and historical trends.

How the Parameters Work Together

Behavioral rules primarily identify deviations between the Current traffic level and the Predicted value within the context of the defined Season. Significant discrepancies can trigger alerts.

References

IBM Security QRadar Documentation - Anomaly Detection Rules: ( ). Search for "behavioral rule test parameter options" within the relevant documentation for QRadar SIEM V7.5.

The Application Overview dashboard in QRadar includes various default items1. Two of these items are Top Applications (Total Bytes) and Outbound Traffic by Country (Total Bytes)1.

Default dashboards - IBM Documentation

According to the IBM Security QRadar SIEM V7.5 documentation, the Application Overview dashboard by default includes items such as "Inbound Traffic by Country (Total Bytes)," "Outbound Traffic by Country (Total Bytes)," and "Top Applications (Total Bytes)" among others. This confirms that options C and D are displayed by default on the Application Overview dashboard​​.

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.

To perform an IP address X-Force Exchange Lookup in QRadar, you can follow these steps2:

Select the Log Activity or the Network Activity tab.

Right-click the IP address that you want to view in X-Force Exchange.

Select More Options > Plugin Options > X-Force Exchange Lookup to open the X-Force Exchange interface2.

The procedure to perform an IP address X-Force Exchange Lookup in QRadar involves selecting either the Log Activity or the Network Activity tab, right-clicking the IP address of interest, and then navigating through More Options > Plugin Options > X-Force Exchange Lookup to access the X-Force Exchange interface​​.

Anomaly Detection Focus: Anomaly rules specialize in identifying deviations from established baselines or normal patterns.

Outlier Identification: Outliers are often the result of unusual volume changes, which anomaly rules are suited to detect.

Other Rule Types (less ideal):

Behavioral: Broader focus on activity patterns, not just volume.

Custom: Can be built for this, but anomaly rules have it as core functionality.

Threshold: Trigger based on specific values, less dynamic than anomaly rules.

The logical operator != in an AQL (Ariel Query Language) query is used to compare two values and returns true if the values are unequal. This operator is a common element in various programming and query languages, and its purpose is consistent across these environments, including in IBM Security QRadar SIEM V7.5.

For instance, in an AQL query, if you are analyzing event or flow data and want to filter out records where a specific field, say username, does not equal a certain value, you could use the != operator in your query like so: SELECT * FROM events WHERE username != 'admin'. This query would return all records where the username field does not equal 'admin'.

The use of the != operator is crucial in data analysis and threat hunting within QRadar, as it allows security analysts to exclude certain data points and focus on the relevant data that might indicate security incidents or breaches.

On the Offenses tab within QRadar, the "Offense Type" column explains the cause of the offense. The offense type is determined by the rule that triggered the offense, and it dictates the kind of information displayed in the Offense Source Summary pane. This helps analysts understand the nature and origin of the offense, facilitating more effective investigation and response actions​​.

Network Attacks: In security investigations, the Source IP typically represents the attacking device. It's the origin of the malicious activity.

Offense Data: QRadar offenses gather information about the incident, including the Source IP as a crucial property.

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​​.

When investigating an offense in QRadar, finding the number of flows or events associated with it can be achieved through the "List Events/Flows" option. This functionality allows analysts to view a detailed list of all the individual events and flows that are related to a specific offense, offering insights into the nature and scope of the activities involved. By examining this list, analysts can better understand the context of the offense, including the types of network traffic and system actions that triggered the security alerts, facilitating a more informed investigation process.

When an analyst right-clicks on the Source IP or Destination IP that is associated with an offense at the Offense Summary in QRadar, two of the top-level options are DNS Lookup and WHOIS Lookup1. These options provide additional information about the IP address, such as its domain name (DNS Lookup) and registration information (WHOIS Lookup)1.

By default, QRadar stores payload indexes for a duration of 30 days. This retention period is configurable, allowing administrators to adjust how long specific data is retained based on their requirements​​.