SPLK-5001 Exam Dumps : Splunk Certified Cybersecurity Defense Analyst

The Common Information Model (CIM) in Splunk is a crucial component that allows for the normalization and standardization of data across various sources. By using CIM, disparate data sources can be mapped to a common schema, which makes it significantly easier to correlate and analyze data across different logs and systems.

Purpose of CIM:CIM provides a standardized format for fields and event types across various data sources in Splunk. This normalization allows analysts to use consistent field names and structures when performing searches, regardless of the original data source's format.

Benefit of Easier Correlation:One of the primary challenges in security operations is correlating data from different sources—like firewalls, intrusion detection systems (IDS), endpoint security solutions, and network logs—to identify potential security incidents. CIM facilitates this by ensuring that all relevant data adheres to a common schema, enabling seamless correlation and analysis. For example, CIM allows a security analyst to write a single query that can apply to data from multiple sources, simplifying the detection of complex threats.

How it Works:CIM is implemented through data models in Splunk, which act as a blueprint for mapping and transforming raw data into a structured format. These data models cover a wide range of security domains, such as authentication, network traffic, and malware, ensuring that data from different security tools can be easily integrated and analyzed together.

Use Cases:The primary use cases for CIM include:

Search and Reporting:Creating efficient and standardized searches that apply across multiple data sources.

Dashboards and Visualizations:Building dashboards that pull in data from various sources in a consistent manner.

Correlation Searches:Developing correlation searches that detect patterns or anomalies across different types of data, enhancing threat detection.

Thestatscommand is used to generate statistics, such as counts, over specific fields. In this case, the commandindex=security_logs eventtype=failed_login | stats count as failed_attempts by src_ip | sort -failed_attemptscreates a temporary table that counts the number of failed login attempts (failed_attempts) for each source IP (src_ip). Thesort -failed_attemptsensures the results are ordered by the number of failed attempts in descending order, making it easier for an analyst to identify problematic IPs.

The scenario describes an attack where thousands of failed login attempts are made using various usernames and passwords, which is indicative of aCredential Stuffingattack. This type of attack involves using lists of stolen credentials (usernames and passwords) obtained from previous data breaches to attempt to gain unauthorized access to user accounts. Attackers take advantage of the fact that many users reuse passwords across multiple sites. UnlikePassword Spraying(which tries a few common passwords against many accounts) orPassword Cracking(which tries to guess or decrypt passwords), credential stuffing leverages large datasets of valid credentials obtained from other breaches.

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