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

Building Blocks in QRadar are foundational elements that are used to construct more complex rules. They are essentially a collection of conditional tests or criteria that define specific behaviors, characteristics, or patterns within the network data but do not, by themselves, trigger any responses or actions when those conditions are met.

Building Blocks are designed to be reused in multiple rules, making rule creation more efficient and standardized. For example, a Building Block might define a set of commonmalicious IP addresses or unusual traffic patterns. This Building Block can then be incorporated into several different rules that might deal with various types of threats, each of which requires identifying traffic from or to these malicious IPs as part of their logic.

The reusability of Building Blocks ensures that changes to common criteria, such as updating the list of malicious IP addresses, only need to be made in one place. This approach enhances the maintainability and consistency of the rule set within QRadar, making the system more agile and responsive to changes in the threat landscape.

Building Blocks are a powerful feature within QRadar that promote modularity and efficiency in rule creation, helping organizations tailor their threat detection capabilities to their specific needs without requiring actions or responses to be defined within these foundational elements themselves.

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​​​​.

• 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.

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​

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.

The magnitude rating of an offense in IBM Security QRadar SIEM V7.5 is calculated based on three key parameters: severity, relevance, and credibility. Severity indicates the level of threat, relevance determines the offense's impact on the network, and credibility reflects the integrity of the offense as determined by the credibility rating configured in the log source. This combination of factors helps prioritize offenses and guide analysts on which ones to investigate first​​.

The IBM QRadar Use Case Manager application assists in tuning QRadar to ensure it is optimally configured for accurate threat detection throughout the attack chain. This application provides guided tips to help administrators adjust configurations, making QRadar more effective in identifying and mitigating security threats. The QRadar Use Case Manager plays a significant role in maintaining the effectiveness of the QRadar deployment​​.

Event rules in QRadar test against incoming log source data processed in real time by the QRadar Event Processor. This real-time processing enables QRadar to analyze and respond to security events as they occur, enhancing the system's ability to detect and mitigate threats promptly​​.

Before configuring the QRadar Use Case Manager app, it is essential to ensure that the app has the necessary permissions to function correctly. This typically involves creating an authorized service token which provides the app with the permissions to access and manage the QRadar environment​​.

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​​.

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

The "Start Time" timestamp represents when an event is received by a QRadar Event Collector, marking the moment QRadar first becomes aware of the event. This is crucial for understanding the timing of event processing and potential delays in the event pipeline​​.

In IBM Security QRadar SIEM V7.5, the integration with MITRE ATT&CK framework is a valuable feature that enhances the understanding of threat tactics and techniques. The Use Case Manager within QRadar provides detailed insights into various MITRE ATT&CK tactics and techniques associated with different offenses or alerts. To get more information about a specific MITRE entry, users can click on the Tactic’s Explore icon associated with the entry. This action opens the corresponding page on the MITRE ATT&CK website, providing detailed information about the tactic or technique, including its description, examples of use, and mitigation strategies. This direct link to the MITRE ATT&CK website enriches the user's knowledge and aids in the analysis of security incidents, making it easier to understand the context and implications of specific attack behaviors observed in the monitored environment.

• 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.

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​​.

• Specificity: The question states that the addresses are specifically IPv6-formatted. Using the 'IPv6' type ensures precision in the reference set.
• IP Matching by QRadar: QRadar's rule engine will properly match against IPv6 addresses when the reference set type is 'IPv6'.

• 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.

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.

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 .

• 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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When defining the network hierarchy in QRadar, it is recommended to organize systems and networks by role or similar traffic patterns to differentiate network behavior effectively. Additionally, it is advised not to configure a network group with more than 15 objects to avoid difficulties in viewing detailed information for each object and to ensure efficient management of network groups​​.

• 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

While the documentation does not explicitly list "Stored" and "Parsed" as categories comprising events, it discusses high-level event categories and the process of categorizing incoming events for easy searching. Without specific mention of the categories "Stored" and "Parsed," the provided documentation does not verify any of the options directly. Further insight into event categories is provided by discussing how events are grouped into high-level categories for organizational purposes​​.

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

References:

• IBM QRadar Documentation - Searching for Offenses https://www.ibm.com/docs/en/qradar-on-cloud?topic=searches-searching-offenses-my-offenses-all-offenses-pages

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.

• X-Force Exchange: The primary repository for contributed QRadar content, including compliance-focused content packs.
• HIPAA Packs: Likely contain:
• Other Options (less suitable):

References:

• IBM X-Force Exchange: https://exchange.xforce.ibmcloud.com/hub (Search for HIPAA)

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.

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 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/).

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​​.