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

QRadar offers several chart types for visualizing security data on dashboards. Here's a breakdown of why the options are correct or incorrect:

• Supported:
• Unsupported or Partially Supported:

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​​.

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.

The MITRE heat map in the Use Case Manager app within QRadar uses several factors to determine the colors displayed, among which the number of rules mapped to MITRE ATT&CK tactics and techniques and the level of mapping confidence are crucial. These factors help visualize the coverage and reliability of rule mappings against the comprehensive MITRE ATT&CK framework, aiding in the identification of potential gaps or areas for improvement in threat detection capabilities​​.

Here's the breakdown of why this approach is the most suitable:

• Focused Export: The "Event Export (with AQL)" option allows targeted exporting of events based on specific AQL queries. This ensures you only extract the necessary data.
• Usability: The Log Activity tab's interface, including the Test and Export functionality, makes it easy to use even for less technical users familiar with basic QRadar concepts.
• CSV Format: CSV offers a readable, widely compatible format for data review outside of QRadar.

• 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 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​​.

In configuring the frequency of report execution in the QRadar Report wizard, users have several scheduling options to automate or manually initiate report generation. Among the options provided, "Monthly" (C) and "Manually" (E) are valid choices within the QRadar environment. The "Monthly" option allows users to schedule reports to run at specific intervals each month,providing regular insights into the security posture and events within the monitored environment. The "Manually" option gives users the flexibility to generate reports on an ad-hoc basis, depending on specific needs or investigative activities, without adhering to a predetermined schedule .

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​​.

• 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

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​​​​.

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.
• Reasons Why Other Options Are Less Ideal:

• 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.

• 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.

• 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):

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​​.

The Domain attribute governs who can view the value of a reference set data element, ensuring that only users with appropriate domain access or tenant assignments can view the data. This is essential for maintaining data visibility and access control within a multi-tenant QRadar environment​​.

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​​​​.

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 IBM Security QRadar SIEM V7.5, the feature that utilizes existing asset profile data to define unknown server types and assign them to server definitions in building blocks and in the network hierarchy is known as "Server Discovery." This feature grants permission to discover servers, thereby enabling administrators to identify and classify various server types within their network infrastructure, enhancing the overall asset management and security posture​​.

Threshold rules in QRadar are designed to test events or flows for activities that are greater than or less than a specified range. These rules are particularly useful for detecting significant changes such as bandwidth usage variations, failed services, changes in the number of connected users, and large outbound data transfers. By setting acceptable limits within threshold rules, administrators can effectively monitor for and respond to abnormal activities within the network​​.

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​​.

Here's why an AQL statement will default to running for 5 minutes:

• Timeout Protection: QRadar implements timeouts to prevent queries from running indefinitely and potentially overloading the system.
• Default Timeout: If no explicit time criteria is specified, the standard timeout in QRadar is 5 minutes.exclamation
• Modifying Timeouts: Advanced users can change this default, but it requires modification of QRadar configuration settings.

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.

When deleting a generated content report in QRadar, all reports that were generated from the report template are deleted, but the report template itself is retained. This ensures that the structure for generating future reports remains intact, while only the instances of reports generated from that template are removed​​.

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 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​​.

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​​.

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.

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 themost importantparameter. 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:
• 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 theCurrent traffic leveland thePredicted valuewithin the context of the definedSeason. Significant discrepancies can trigger alerts.

References

• IBM Security QRadar Documentation - Anomaly Detection Rules: (https://www.ibm.com/docs/en/qradar-on-cloud?topic=rules-anomaly-detection). Search for "behavioral rule test parameter options" within the relevant documentation for QRadar SIEM V7.5.