Splunk SPLK-5001 Splunk Certified Cybersecurity Defense Analyst Exam Practice Test

According to Splunk CIM (Common Information Model) documentation, thesrc_userfield in the Authentication Data Model represents the user who initiated an action, including privilege escalation. This field is used to track the source user responsible for generating an authentication event, which is critical in understanding and responding to potential security incidents involving privilege escalation. The other fields likedest_userorusernamehave different roles, focusing on the target of the action or the general username involved.

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TheAsset and Identityframework within Splunk Enterprise Security provides additional automatic context and correlation to fields that exist within raw data. By associating IP addresses, usernames, and other identifiers with known assets and identities within the organization, this framework enhances the context of security events and facilitates moreaccurate and meaningful analysis. This allows analysts to better understand the impact of security incidents and to prioritize their responses based on the criticality of the assets involved.

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Splunk Enterprise Security provides a feature calledFramework Mappingthat allows correlation searches to be mapped to specific cybersecurity frameworks, including NIST 800-171, which is crucial for DoD contractors. This mapping provides context to the analyst by showing how particular searches align with compliance requirements, aiding in continuous monitoring and reassessment as mandated by the DoD. This feature is integral for organizations that need to demonstrate compliance with NIST guidelines and other security frameworks.

According to Splunk’s Common Information Model (CIM) documentation, when investigating network alerts, the IP address of the host from which an attacker is moving (source) is typically stored in thesrc_ipfield. Thehostfield generally refers to the name of the host that logged the event,destrefers to the destination IP, andsrc_nt_hostrefers to the NetBIOS name of the source host. Thesrc_ipfield is specifically used to denote the source IP address in the context of network communication, which is critical for tracing lateral movement.

The log entry indicates aPOST /cgi-bin/shutdown/request, which suggests that a command was sent to shut down the server via a CGI script. This kind of activity is indicative of aDenial of Service (DoS) attackbecause it involves sending a specific command that causes the server to stop functioning or shut down. This is different from a Distributed Denial of Service (DDoS) attack, which typically involves overwhelming the server with traffic rather than exploiting a specific command.

AnIntrusion Detection System (IDS)typically sits at the network perimeter and is designed to detect suspicious traffic, including command and control (C2) traffic and other potentially malicious activities.

Intrusion Detection Systems:

IDS are deployed at strategic points within the network, often at the perimeter, to monitor incoming and outgoing traffic for signs of malicious activity.

These systems are configured to detect various types of threats, including C2 traffic, which is a key indicator of compromised systems communicating with an attacker-controlled server.

Incorrect Options:

A. Host-based firewall:This is more focused on controlling traffic at the endpoint level, not at the network perimeter.

B. Web proxy:Primarily used for controlling and filtering web traffic, but not specifically designed to detect C2 traffic.

C. Endpoint Detection and Response (EDR):Focuses on endpoint protection rather than monitoring network perimeter traffic.

Network Security Practices:IDS implementation is a standard practice for perimeter security to detect early signs of network intrusion.

The correct Splunk search that returns results in the most performant way isindex=foo host=i-478619733 | stats range(_time) as duration by src_ip | bin duration span=5min | stats count by duration, host. This search is optimized by:

Starting with the most specific search criteria (index and host) to reduce the data set.

Applying aggregation functions (stats) early, which helps minimize the amount of data processed in subsequent commands.

Usingbinto group data efficiently before performing further statistical calculations.

Search Optimization:

Efficient Indexing:By specifyingindex=fooandhost=i-478619733at the start, the search limits the scope of data that needs to be processed, which significantly improves performance.

Early Aggregation:Thestatscommand is used early in the search to aggregate data bysrc_ip, which reduces the volume of data passed to the next stages of the pipeline.

Use ofbin:Grouping durations withbinbefore performing a secondstatsaggregation reduces the number of unique values, making the final stats calculation more efficient.

Performance Considerations:

Order of Operations:Splunk processes search commands from left to right. Starting with a broad data retrieval and then narrowing down with stats and bin commands ensures that the least amount of data is processed in the final stages.

Avoiding Suboptimal Patterns:The other options either apply operations in a less efficient order or involve unnecessary steps that increase processing time and resource usage.

Splunk Search Documentation:The official Splunk documentation provides guidelines on how to construct efficient searches, including the best practices for usingstats,bin, and indexing.

Splunk Performance Tuning Guides:These guides offer in-depth advice on optimizing searches for speed and efficiency, with examples of common pitfalls and how to avoid them.

References:

The primary difference between a Distributed Denial of Service (DDoS) attack and a Denial of Service (DoS) attack is in the source of the attack. ADDoSattack involves multiple compromised systems (often part of a botnet) attacking a single target, overwhelming it with traffic or requests. In contrast, aDoSattack typically involves a single source attacking the target. The goal of both attacks is to make a service unavailable, but DDoS attacks are usually more difficult to defend against because of their distributed nature.

To investigate which process initiated a network connection, an analyst would use theEndpointdata model in Splunk Enterprise Security. The Endpoint data model contains fields related to processes, file activity, and host-level data, which are essential for tracing back the source of suspicious network activity to the specific process or application that initiated it. This is crucial for understanding the scope of an attack and determining the origin of malicious network traffic.

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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.

Splunk CIM Documentation:The official documentation provides comprehensive guides on how to implement and use CIM for various data sources, including detailed field mappings and examples.

Splunk Security Essentials:This resource offers practical examples and pre-built use cases that utilize CIM for effective security operations.

Community Blogs and Discussions:Many experienced Splunk users share best practices for using CIM in forums and blogs, where they discuss real-world applications and troubleshooting tips.

References:

When creating performant searches in Splunk, it is a best practice to utilize specific fields to return only the data that is required. This approach minimizes the amount of data processed and speeds up search performance. By explicitly specifying the fields of interest using commands likefields, you reduce the overhead on Splunk’s processing engine, leading to faster and more efficient queries. In contrast, using wildcards or overly broad searches can lead to slower performance due to the increased data volume being processed.

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In Splunk Enterprise Security, when assets are properly defined and enabled, the fieldsrc_categoryis automatically added to search results. This field categorizes the source IP addresses according to their asset classification, which helps in analyzing and filtering search results based on the type of assets involved in an event. Proper asset and identity management within Splunk ES enhances the ability to contextualize and prioritize security incidents.

The step-by-step description provided is an example of aTechniqueas defined in the MITRE ATT&CK framework. Techniques are the specific methods adversaries use to achieve their objectives during an attack, such as establishing command and control (C2) channels or delivering payloads via phishing emails. In this scenario, the attacker uses a non-default beacon profile in Cobalt Strike, sends a malicious document via email, and establishes a C2 channel once the victim interacts with the document, all of which are examples of adversary techniques.

TheTERM()search command in Splunk allows an analyst to match a specific term exactly as it appears, even if it contains characters that are usually considered minor breakers, such as periods or underscores. By usingTERM(), the search engine treats everything inside the parentheses as a single term, which is especially useful for searching log data where certain values (like IP addresses or filenames) should be matched exactly as they appear in the logs.

In Splunk’s Risk-Based Alerting (RBA) framework, aRisk Objectrefers to the specific entity (such as a user account, IP address, or host) that is associated with risk observations. When auser account generates multiple risk observations, it is labeled as a Risk Object, allowing security teams to track and manage risk more effectively.

Risk Object:

The Risk Object is central to Splunk’s RBA approach, which aggregates and evaluates risk across entities within an environment. This allows for a focused response to high-risk entities based on the accumulation of risk events.

Incorrect Options:

A. Risk Factor:This might refer to specific criteria or conditions that contribute to risk but does not denote the entity itself.

B. Risk Index:Could refer to a collection of risk-related data, not the specific entity.

C. Risk Analysis:Refers to the process of analyzing risk, not the entity under observation.

Splunk RBA Documentation:Detailed descriptions of how Risk Objects function within the Risk-Based Alerting framework.

References:

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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Theforeachcommand in Splunk is used to iterate over a list of fields that match a wildcard expression and apply a subsearch or function to each of them. This is particularly useful when you need to perform an operation across multiple fields dynamically identified by a wildcard pattern. None of the other options (rex,makeresults, ortransaction) are designed for this specific purpose. Theforeachcommand allows for flexible and efficient processing of multiple fields without having to explicitly name them all.

The Lockheed Martin Cyber Kill Chain® is a widely recognized framework that breaks down the stages of a cyber attack. The stages are: Reconnaissance, Weaponization, Delivery, Exploitation, Installation, Command and Control (C2), and Actions on Objectives. The scenario described—modifying the registry on a compromised Windows system to ensure malware runs at boot time—fits into theInstallationphase. This phase involves placing a persistent backdoor or other malicious software on the victim's system, ensuring it can be executed again, even after a system reboot. By modifying the registry, the attacker is achieving persistence, a classic example of the Installation phase.

NetFlow data is a powerful data source for monitoring and analyzing network traffic patterns within an organization. It provides detailed information about the flow of data between devices on a network, including source and destination IP addresses, ports, and protocols. By analyzing NetFlow data, security analysts can detect unusual communication patterns that may indicate malicious activity, such as lateral movement, data exfiltration, or communication with command and control servers. Other options like EDS (Endpoint Detection Systems), Email, and IAM (Identity and Access Management) are also valuable, but NetFlow is specifically designed for network traffic analysis.

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