IBM C1000-162 - IBM Security QRadar SIEM V7.5 Analysis

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​​.

Custom Properties: QRadar allows you to extract custom properties from raw log and flow data, enriching your analysis capabilities.

Supported Expression Types:

Regex (Regular Expressions): Powerful patterns for extracting specific strings or values from textual data.

JSON (JavaScript Object Notation): Extracts values from structured JSON data within events and flows.

Unsupported Types:

XLS: Excel spreadsheet format. QRadar isn't designed to parse spreadsheets directly.

YAML: A data serialization language. QRadar's extraction is more focused on data within events and flows rather than standalone configuration files.

HTML: Markup language used for web pages. Event data is unlikely to be solely in HTML format.

References:

IBM QRadar Documentation - Custom Property Expression Types: https://www.ibm.com/docs/en/qradar-on-cloud?topic=expressions-configuring-custom-property-expression

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.

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​​.

Event Details Page in QRadar: The event details page in QRadar provides comprehensive information about each event, including metadata, payload, and correlation details.

Checking Fully Matched Rules:

The event details page includes a section that lists all the rules that were fully matched for that specific event.

This information is crucial for analysts to understand why an event was flagged and how it contributes to the overall offense.

Navigating to Event Details:

To view the event details page, an analyst can click on the event from the offense details or directly from the event list.

Within the event details, the matched rules are typically listed under the "Rules" or "Correlation" section.

Reference Confirmation: According to IBM QRadar documentation, the event details page is the location where analysts can see which rules were fully matched for a specific event.

References:

IBM QRadar documentation on event investigation and details page layout confirms that fully matched rules are displayed on the event details page .

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​​.

Understanding Scatter Charts in QRadar Pulse: QRadar Pulse is a visualization application used to create and view different types of charts for better data analysis and interpretation.

Types of Y-Axis:

Linear Axis: This type of axis displays data points at equal intervals. It's suitable for evenly distributed data and shows trends in a straightforward manner.

Logarithmic (Log) Axis: This axis type displays data on a logarithmic scale, which is useful for data that covers several orders of magnitude or for data that grows exponentially.

Selection for Scatter Charts: When creating scatter charts in QRadar Pulse, the application allows users to choose between linear and logarithmic (log) Y-axis types to best represent their data.

Reference Confirmation: According to IBM QRadar documentation, both linear and logarithmic Y-axis types are supported for scatter charts in the Pulse app, making them the correct answers.

References:

IBM QRadar documentation on Pulse app charting options confirms the availability of linear and logarithmic Y-axis types.

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: (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.

Understanding Flow Payload in QRadar: QRadar captures and analyzes network flow data, which includes payload information. However, due to storage and performance considerations, payload data may be truncated if it exceeds a certain size.

Default Payload Size: The default value size for flow payloads in QRadar is 256 bytes. When the payload exceeds this size, the remaining data is truncated, and only the first 256 bytes are stored and displayed for analysis.

Impact of Truncation: Truncated payloads may omit critical information, which can impact the depth of analysis. Analysts need to be aware of this limitation and may need to adjust settings or use additional tools for a complete payload view if necessary.

Reference Confirmation: According to IBM QRadar documentation, the default payload size that, when exceeded, leads to truncation is 256 bytes.

References:

IBM QRadar documentation on flow data analysis and payload size limitations confirms the default truncation threshold of 256 bytes .