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.

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​​.

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.

• 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.

Indexed custom event properties in IBM Security QRadar SIEM are designed to optimize the search process by narrowing down the overall data set. When a property is indexed, QRadar can more efficiently locate events or flows that match the search criteria, thereby reducing the overall volume of data that needs to be searched and enhancing performance. This is reflected in statement B, where indexed filters eliminate portions of the data set that are not relevant to the search query, effectively reducing the number of event or flow logs that must be examined .

Moreover, the use of indexed event and flow properties for optimizing searches is a recommended practice in QRadar. By selectively indexing properties that are frequently used in searches, analysts can significantly improve the speed and efficiency of their queries. This approach is beneficial in environments where quick access to specific event or flow data is crucial for timely threat detection and response. Therefore, statement Ehighlights the importance of utilizing indexed properties to streamline the search process and facilitate more effective security analytics .

• 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

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.

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​​.

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​​.

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​​.

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 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.

• Pulse Dashboards and JSON: The QRadar Pulse app uses JSON (JavaScript Object Notation) to represent dashboard configurations. Here's why:

• 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.

• 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.

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​​.

• 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

• 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'.

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 why this is the most effective approach:

• False Positive Reduction: The goal is to stop legitimate traffic from triggering offenses. This requires fine-tuning the rules generating those offenses.
• Building Blocks: Rules are housed within building blocks in QRadar's hierarchical rule structure. The Custom Rules Editor is the tool to modify them.
• Event-Based Tuning: The optimal approach is to target the specific event that's causing the false positives, making the solution more precise.

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​​.

• 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.

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​​​​.

Adding indexed properties to QRadar can significantly improve the efficiency of searches by reducing the size of the data set required to locate matches for non-indexed search values. Indexing creates references to unique terms in the data and their locations, which means that the search engine can filter the data set by indexed properties first, eliminating irrelevant portions of the data set and thereby reducing the overall volume of data that needs to be searched​​.

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.

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.

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​​.