C1000-162 Sample Questions Answers

The MITRE ATT&CK heatmap within the QRadar Use Case Manager app visualizes how well your security defenses align against the MITRE ATT&CK framework. The colors on the heatmap are determined by the following:

• Level of Mapping Confidence: The confidence level with which QRadar has associated offenses to specific MITRE ATT&CK techniques. The colors indicate:
• Number of Offenses Generated: The frequency of offenses observed that map to a particular MITRE ATT&CK technique. A higher number of offenses will result in a deeper shade within the color gradient (i.e., dark red vs. light red).

References

• IBM Security QRadar Use Case Manager Documentation:
• MITRE ATT&CK Website: Provides foundational information about the framework itself (https://attack.mitre.org/).

Here's a breakdown of reference data collections in QRadar:

• Primary Types:

• 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.

The magnitude rating of an offense in IBM Security QRadar SIEM is a measure of the relative importance of a particular offense. It is a weighted value calculated from several factors, including severity, relevance, and credibility . These parameters are used to assess the potential impact of an offense, taking into account its seriousness (severity), its applicability or significance to the protected environment (relevance), and the reliability of the source or the confidence in the accuracy of the data (credibility). This multifaceted approach ensures that offenses are prioritized in a manner that reflects both their potential impact and the confidence in the underlying data, enabling security analysts to focus on the most critical issues first.

• 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.

Widget chart types - IBM Documentation

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​​​​.

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.

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​​.

A Quick filter search in IBM Security QRadar SIEM operates on raw event or flow data. This type of search allows users to rapidly filter through large volumes of data to find specific events or flows of interest without the need for complex query syntax. Quick filter searches are particularly useful for conducting initial analyses or when looking for specific indicators within the raw data streams. The ability to search directly on raw event or flow data enables analysts to work with the most granular level of information available, facilitating detailed investigations and the identification of subtle patterns or anomalies that might indicate security issues .

For pie charts in the Pulse app of QRadar, the available aggregation types include "Total" and "Average." These aggregation types allow for the representation of data in a manner that summarizes the total sum of the data points or their average value, respectively, providing insightful and concise visualizations of the data within the Pulse app dashboards. This information is implied from the general capabilities of dashboard items in QRadar, as detailed in the provided documentation, which typically includes such aggregation options for data visualization​​.

In IBM Security QRadar SIEM V7.5, custom rules play a crucial role in detecting and responding to potential security threats. These rules can be created from various tabs within the QRadar interface, offering flexibility in how and where analysts choose to define their custom detection logic. Specifically, custom rules can be created from the Offenses, Log Activity, or Network Activity tabs. From the Offenses tab, analysts can create rules that are triggered by specific offense characteristics or patterns. The Log Activity and Network Activity tabs allow for the creation of rules based on observed events or network flows, respectively. This multi-faceted approach to rule creation enables analysts to tailor their detection strategies to different aspects of their environment, leveraging the rich data and insights provided by QRadar to identify and mitigate threats effectively.

References:

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

Offense chaining in IBM Security QRadar SIEM V7.5 is based on the offense index field specified in the rule. This means that if a rule is configured to use a specific field, such as the source IP address, as the offense index field, there will only be one offense for that specific source IP address while the offense is active. This mechanism is crucial for tracking and managing offenses efficiently within the system​​.

Within IBM Security QRadar's Ariel Query Language (AQL), functions play a crucial role in manipulating data, performing calculations, and formatting results for more insightful analysis. Among the options provided, "LOWER" (C) and "STRLEN" (D) are valid AQL functions used for formatting and calculations, respectively. The "LOWER" function is used to convert a string to lowercase, which can be useful for case-insensitive comparisons or data normalization. The "STRLEN" function calculates the length of a string, providing valuable information about the data content, such as detecting unusually long or short values that might indicate anomalies or issues within the event or flow data .

To find information about an IP or URL within QRadar, analysts can use the right-click menu option "X-Force Exchange Lookup." This option is available when right-clicking an IP address or URL from the Offenses tab or event details windows, providing direct access to the X-Force Exchange interface for detailed threat intelligence and contextual information​​.

Selecting "False Positive" from the right-click menu in the Log Activity tab opens a window that enables users to tune out events that are known to be false positives, preventing them from generating offenses. This feature is crucial for minimizing noise and focusing on genuine threats, thereby enhancing the efficiency of threat detection and response processes within QRadar​​.

• Magnitude:The Magnitude metric in QRadar represents a calculated severity or importance score assigned to an offense. Here's how it helps:

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.

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

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 eventtook 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.

• Common Rules: The foundation of QRadar's event analysis. They operate on structured events representing activity from various log sources.
• Event Processor: Responsible for normalizing and categorizing raw log data from various sources into structured QRadar events.

A "Flow Bias" Quick Search can be performed from the Network Activity tab in QRadar, providing insights into network flows and potential anomalies or biases in the traffic patterns​​.

• 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.

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, sayusername, 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 theusernamefield 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.

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​​.

• Time Series Charts in QRadar: Time series charts visualize how event or flow data changes over time. They are primarily used to spot trends and anomalies.
• Interactivity: QRadar's time series charts allow zooming, panning, and hovering to see specific data points. This helps analysts drill down into patterns.
• Search-Based: Time series charts always reflect the results of a search query. Changing the time range or refining the search alters the chart.

Inaccurate Statements

• A. Static time series: QRadar's charts are not static; the interactivity is key.
• C & D. Export Time: These focus on data export, which isn't directly related to the nature of time series charts themselves.

References

• IBM QRadar Documentation - Time Series Charts: https://www.ibm.com/docs/en/qradar-common?topic=pulse-aggregating-data-create-time-series-chart

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.

In IBM Security QRadar SIEM, generating a report using the QRadar Report Wizard requires a "Saved Search" and a "Layout." A Saved Search is a predefined search criterion that users save in QRadar to reuse for various reporting or analysis purposes. It acts as the data source for the report, defining what data will be included. The Layout component refers to the structure and presentation of the report, including how the data from the Saved Search is organized and displayed. It encompasses the formatting, charts, tables, and other visual elements that make up the final report. Together, these components ensure that reports are not only informative but also well-organized and readable, catering to the specific informational needs and preferences of the users or stakeholders.

• 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.

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