SPLK-5001 Splunk Certified Cybersecurity Defense Analyst Questions and Answers

The MITRE ATT&CK framework categorizes Tactics, Techniques, and Procedures (TTPs) used by attackers. It is a globally accessible knowledge base of adversarial tactics and techniques based on real-world observations, and it is widely used by cybersecurity professionals to understand and defend against various cyber threats.

Tactics, Techniques, and Procedures (TTPs):

Tactics:The high-level goals that attackers aim to achieve during an attack. For example, gaining initial access to a network.

Techniques:The specific methods used to achieve these tactics, such as phishing or exploiting a vulnerability.

Procedures:The detailed steps and tools that attackers use to implement a technique.

MITRE ATT&CK Framework:MITRE ATT&CK organizes these TTPs into a matrix that reflects different stages of an attack lifecycle, from initial access to exfiltration. The framework helps security teams by:

Mapping Detection and Defense:Organizations can map their existing detection capabilities against the ATT&CK matrix to identify gaps.

Threat Hunting:Security teams use the framework to guide their threat-hunting activities, focusing on specific techniques associated with known adversaries.

Incident Response:During incident response, analysts can use the ATT&CK framework to understand the behaviors exhibited by an attacker, which helps in creating effective containment and eradication strategies.

Why MITRE ATT&CK:Unlike compliance-focused frameworks like NIST 800-53 or ISO 27000, which provide security controls and best practices, MITRE ATT&CK is specifically focused on the behavior of adversaries. This focus makes it an invaluable resource for understanding how attacks unfold and how to counteract them.

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.

Pyramid of Pain Overview:The Pyramid of Pain categorizes indicators based on how difficult they are for attackers to alter:

Hash Values (Low Impact):Attackers can easily change a file's hash by altering even a single byte, rendering this indicator obsolete.

IP Addresses, Domain Names (Moderate Impact):Slightly harder to change than hash values but still relatively easy for attackers to adapt.

TTPs (High Impact):Tactics, Techniques, and Procedures are the most difficult for attackers to alter because they involve the fundamental ways attackers operate. Detecting and responding to TTPs can significantly disrupt an attacker’s strategy.

Why Hash Values Are Least Effective:

Ease of Evasion:Attackers can quickly generate new hash values by modifying files, making it a weak indicator for continuous monitoring.

Short Lifespan:Once detected and blocked, a hash value is of limited use because attackers can simply recompile or pack the malware to create a new hash.

In theSplunk Common Information Model (CIM), the Authentication Data Model defines standardized field names to normalize data from various sources. For privilege escalation events, the user who initiates the escalation (i.e., the actor performing the elevated action) is represented by the fieldsrc_user. This field captures the source or originating user account involved in the authentication event.

src_user: Typically refers to the user who initiated the action, such as the account escalating privileges.

dest_user: Refers to the target user account involved, such as the user being impersonated or the elevated account.

src_user_idandusernameare either deprecated or less precise in CIM for privilege escalation contexts.

TheSplunk CIM documentation(specifically the Authentication Data Model section) states thatsrc_useris the correct normalized field to capture the initiating user in escalation or impersonation events, supporting consistent searches, alerts, and dashboards across varied log sources.

In Splunk, field extractions are essential for transforming raw log data into structured fields that are easier to work with during analysis. When the question refers to an analyst identifying helpful information in the raw logs that assists them in determining suspicious activity, the most effective way to streamline this process is throughfield extraction. This allows the Splunk system to automatically parse and tag the necessary data, making it more accessible for searches, dashboards, and alerts.

Let’s break down whyoption A: Create a field extraction for this informationis the best approach:

Field Extraction Overview:

Field extraction is a process within Splunk that takes unstructured log data and converts it into structured fields.

This makes it possible to directly query and display these fields, allowing analysts to quickly find and use relevant data in their investigations.

For example, if the logs contain IP addresses, user IDs, file names, or activity types, extracting these fields enables the analyst to filter and correlate data much more effectively without manually scanning the raw logs.

Why Field Extraction?

In this case, the question suggests that the raw logs contain information that helps determine whether activity is malicious. By creating field extractions for the relevant data points, analysts can use those structured fields to build queries and visualizations, drastically speeding up analysis time.

Analysts can write custom Splunk queries to isolate events that meet specific conditions, such as matching specific cloud sharing activities associated with risk notables.

Field extraction improves not only real-time analysis but also supports retrospective analysis and incident correlation across multiple events.

Comparison to Other Options:

Option B: Add this information to the risk message– While adding more context to a risk message could be useful for reviewing individual alerts, it doesn’t improve the efficiency of log analysis. The analyst still would need to go back and manually inspect raw logs for more detailed data.

Option C: Create another detection for this information– Creating additional detections adds more rules, but doesn't solve the fundamental issue of having raw logs that aren’t easily searchable. You can only build effective detections when you have structured data available.

Option D: Allowlist more events based on this information– Allowlisting is generally used to reduce noise or irrelevant logs, but it doesn't help extract the necessary details for analysis. It may reduce unnecessary alerts, but won’t help analyze the suspicious events that do arise.

Cybersecurity Defense Analyst Best Practices:

Field extractionsshould be created for any important log source or data point, especially when handling complex or multi-part log entries (e.g., cloud sharing logs). This ensures logs are searchable and actionable, allowing for faster identification of anomalies and malicious activity.

Analysts should collaborate with engineers to ensure these extractions are tuned and validated. The extraction should be tailored to isolate the fields most relevant for identifying suspicious activity.

Once fields are extracted, analysts can create dashboards, real-time alerts, or retrospective searches based on the structured data for more effective incident response.

TheAsset and Identityframework within Splunk Enterprise Security (ES) is designed to add enriched context and correlation to the raw data fields by mapping logs to known assets (such as devices, servers, endpoints) and identities (users and accounts). This framework enhances the meaning of raw event data by associating events with relevant organizational entities, which helps in prioritization, detection, and investigation workflows.

TheAsset and Identityframework automatically tags and correlates data points with asset categories and user identities, improving detection fidelity and enabling targeted risk scoring.

Adaptive Responseis a mechanism for triggering automated actions, not a contextual framework.

Threat Intelligenceframework integrates external intelligence data but does not inherently add context to raw data fields.

Riskframework calculates risk scores based on multiple factors but builds upon context enriched by Asset and Identity.

TheSplunk Enterprise Security User Guidestates that the Asset and Identity framework is critical for making raw event data actionable through contextual enrichment and is foundational to the ES correlation searches and dashboards.

When an event is identified as suspicious but is expected or normal behavior within the environment, the appropriate disposition to assign isBenign. This categorization distinguishes such events from true security incidents or false positives, providing analysts with clarity on what to expect in their environment.

ATrue positiveindicates a confirmed security incident or threat that requires further investigation or response.

False positiverefers to an alert incorrectly triggered by benign activity that resembles malicious behavior but is not.

Informationaldisposition is typically reserved for events that provide data without implying suspicion or threat.

Benignis used for events that may look suspicious but are accepted as normal or expected in the specific environment context, helping reduce noise and improve analyst efficiency.

TheSplunk Cybersecurity Defense Analyst documentationadvises SOC teams to clearly define and apply dispositions consistently to ensure proper event triage and avoid alert fatigue.

In Splunk, to filter users with over a thousand occurrences, the pipeline| stats count by user | where count > 1000 | sort - countis most effective. Thestats count by usercommand generates a count of occurrences for each user. Thewhereclause then filters out only those users who have more than 1000 occurrences. Finally,sort - countsorts the results in descending order by count. This approach is efficient for identifying outliers, such as users with a high number of events.

Splunk Enterprise Security (ES) provides various features to enhance security monitoring, analysis, and incident response. One of the powerful features in Splunk ES isAnnotations. This feature allows security analysts to map and categorize correlation search results according to well-known industry frameworks such as the CIS Critical Security Controls, MITRE ATT&CK, and the Lockheed Martin Cyber Kill Chain®.

Purpose of Annotations:

Annotations help analysts understand and categorize security events by aligning them with recognized security frameworks. This alignment provides context, making it easier to understand the nature of threats and how they fit within broader threat models or attack strategies.

How Annotations Work:

When a correlation search in Splunk ES triggers an alert, Annotations can automatically tag the alert with relevant tactics, techniques, and procedures (TTPs) from frameworks like MITRE ATT&CK. This helps in categorizing the event within the context of known attack patterns, offering insights into potential next steps by an attacker and recommended defensive actions.

Annotations can be manually added or configured to be applied automatically based on the nature of the search results.

Integration with Frameworks:

MITRE ATT&CK:Annotations can map alerts to specific techniques and tactics in the MITRE ATT&CK framework, which provides a detailed knowledge base of adversary behaviors, tactics, and techniques.

CIS Critical Security Controls:These controls can also be mapped through annotations, allowing the organization to measure and improve its security posture against these controls.

Lockheed Martin Cyber Kill Chain®:This model focuses on the stages of a cyberattack, and annotations can help identify where in the kill chain a particular alert fits, providing a clearer understanding of the attack’s progression.

Annotations in Splunk ES:Practical Example:Consider a correlation search that detects unusual behavior indicating potential lateral movement within a network. If this alert is annotated with a reference to the MITRE ATT&CK framework, it might map to techniques like "T1021 - Remote Services," which is associated with the lateral movement tactic. This mapping not only categorizes the event but also helps in planning the next steps for containment and investigation.

Efficiency in Response:By aligning alerts with industry frameworks, annotations help in quickly identifying the nature and potential impact of a threat.

Consistency in Analysis:Provides a standardized method for categorizing and responding to alerts, ensuring that all analysts interpret and react to threats in a consistent manner.

Improved Reporting:Allows for better visualization and reporting of threats according to established frameworks, making it easier to communicate risks and actions to stakeholders.

ThemetadataSPL command in Splunk can be used to retrieve various types of metadata information indexed in Splunk. To list the differentsourcetypes(i.e., the categorization of the incoming data), the analyst should use the argument metadata type=sourcetypes.

metadata type=sourcetypes returns a list of all sourcetypes currently indexed, which is essential for understanding what kinds of data are available.

metadata type=hosts lists hosts sending data but does not categorize data types.

metadata type=cdn and metadata type=assets are not valid or standard types for this command in Splunk.

This usage is documented in Splunk’s official search command reference and is fundamental for data management and onboarding processes.

In Splunk, therexcommand is used to extract fields from raw event data using regular expressions. This command allows analysts to dynamically extract additional fields as part of a search pipeline, which is crucial for creating new fields during search time based on specific patterns found in the log data. Therexcommand is highly flexible and powerful, making it essential for refining and manipulating data in a Splunk environment. The other options (fields,regex,eval) have their uses, butrexis specifically designed for dynamic field extraction.

TheAccess dashboardsin Splunk Enterprise Security focus on authentication, login activity, access controls, and related security events. These dashboards provide analysts with insights into successful and failed authentications, anomalous access patterns, and identity-related events.

Audit dashboardsgenerally focus on compliance and audit logs.

Asset and Identity dashboardsprovide broader context about users and devices but are not specifically focused on authentication events.

Endpoint dashboardsconcentrate on host and endpoint telemetry such as process execution and file activity.

Splunk’sEnterprise Security User Guideclearly identifies Access dashboards as the primary interface for monitoring and investigating authentication and access behaviors.

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.

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 a user 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.

Indicators of Compromise (IOCs) are artifacts that are used to identify potential malicious activity within a network or system. Common IOCs include domains, IP addresses, and file hashes that are associated with malicious activity. However, a specific password, while potentially sensitive, is not typically considered an IOC because it is more of a credential than an artifact indicating a compromise. IOCs are used to detect and respond to threats, while compromised credentials are a result of those threats.

Notable Events in Splunk Enterprise Security are configured as part of a correlation search, where an Adaptive Response Action can be set to create a Notable Event when certain conditions are met. These correlation searches are pre-defined or custom searches that look for specific patterns of interest, such as security incidents or anomalies. The use of Adaptive Response Actions within these searches allows for the automated creation of Notable Events, which can then be investigated by security analysts. This configuration is a crucial part of Splunk's security operations capabilities.

Splunk Answersis a community-driven Q&A platform where users can ask questions and share knowledge about Splunk. It is known for providing community-sourced answers to a wide range of questions, including SPL (Search Processing Language) queries, configuration issues, and general best practices. Users can contribute by answering questions based on their own experiences, making it a valuable resource for troubleshooting and learning.

B. Splunk Lantern:This is a resource for best practices, how-tos, and use case guides, but it’s not a community-sourced Q&A platform.

C. Splunk Guidebook:This is not a known resource in the context of community-sourced answers.

D. Splunk Documentation:While highly detailed and official, it is not community-sourced but rather maintained by Splunk's own teams.

The log entry showing the same request repeated millions of times indicates aDenial of Service (DoS) Attack, where the server is overwhelmed by a flood of requests to a specific resource, in this case, the/login/page. This type of attack is aimed at making the server unavailable to legitimate users by exhausting its resources.

Denial of Service Attack:

A DoS attack involves a single attacker (or sometimes a small number of sources) sending a massive number of requests to a target server, overwhelming its ability to handle legitimate traffic.

The repetitive log entries reflect the server's inability to process legitimate requests due to the overwhelming number of identical requests.

Incorrect Options:

B. Distributed Denial of Service Attack:This involves multiple sources attacking simultaneously. The log provided does not indicate multiple sources, which would be characteristic of a DDoS attack.

C. Cross-Site Scripting Attack:This attack involves injecting malicious scripts into webpages viewed by other users, not overwhelming a server with traffic.

D. Database Injection Attack:This is an attack against a database via SQL injection or similar techniques, not overwhelming a server with traffic.

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.

Splunk SOAR (Security Orchestration, Automation, and Response) playbooks are designed to automate security tasks, makingtaking containment action on a compromised hostthe best-suited use case. A SOAR playbook can automate the response actions such as isolating a host, blocking IPs, or disabling accounts, based on predefined criteria. This reduces response time and minimizes the impact of security incidents. The other options, like forming hypotheses for threat hunting or visualizing datasets, are more manual processes and less suited for automation via a playbook.

An event where a user authenticates from geographically distant locations within an impossibly short timeframe is classified as anAccess Anomaly. This type of anomaly reflects unusual or suspicious access behavior that deviates from normal user patterns.

Access Anomaliestypically focus on login behavior, including impossible travel, unusual times, or atypical locations.

Identity Anomaliesrelate to suspicious behavior linked to user identities but are broader than access specifics.

Endpoint Anomaliesfocus on device or host irregularities.

Threat Anomaliesrefer to indications of malicious activity detected by threat intelligence or detection content.

Splunk’sEnterprise Security documentationcategorizes impossible travel as a classic Access Anomaly, frequently flagged by correlation searches and triggering notable events.

In the context of theTTP (Tactics, Techniques, and Procedures)framework used in cybersecurity, the termProcedurerefers to the specific implementation or method used by an adversary to execute an attack or achieve an objective. Here, "LoudWiner" represents a particular malware or tool used by the adversary to hijack resources for crypto mining, which is an actualprocedure—the concrete steps or methods taken to accomplish the broader technique or tactic.

Tacticis the adversary’s goal or objective (e.g., resource hijacking for financial gain).

Techniqueis a general method used to achieve the tactic (e.g., crypto mining via malware).

Procedureis the specific variant or implementation of the technique (e.g., using LoudWiner malware).

Problemis not a recognized element in the TTP framework.

TheMITRE ATT&CK frameworkdefines procedures as real-world implementations of techniques by adversaries. Splunk’s cybersecurity analyst materials emphasize understanding these distinctions to better map observed adversary behavior.

An executable running from theC:\Windows\Tempdirectory is a significant red flag because temporary directories are often world writable, meaning any user or process can write files to them. This characteristic makes these directories an attractive target for attackers who want to drop, stage, and execute malware without worrying about restrictive file permissions.

Temp Directories Characteristics:

World Writable:Temporary directories likeC:\Windows\Tempare typically writable by all users, which reduces the complexity for an attacker to place and execute malicious files.

Lack of Permissions Control:Since these directories do not have strict permission controls, attackers can easily exploit them to execute malicious payloads without being hindered by file access restrictions.

Security Risks:

Malware Staging:Attackers often use temp directories to stage malware because they can avoid detection by traditional security controls that monitor more critical directories like system folders.

Persistence Mechanisms:Some malware will persist in these directories and execute from them each time the system starts or when a particular trigger is activated.

Privilege Escalation:In some cases, malicious files placed in temp directories can be executed by more privileged processes, leading to privilege escalation.

Investigation Importance:The fact that an executable is running fromC:\Windows\Tempwarrants further investigation to determine whether it is malicious. Analysts should check:

File Origin:How the file got there, which user or process created it.

Behavior:What the executable is doing, such as establishing network connections, modifying system settings, or interacting with other sensitive files.

Integrity:Whether the executable is a legitimate system file or a potential piece of malware.

AnIntrusion Prevention System (IPS)is a network security tool designed to continuously monitor network traffic and actively block or prevent malicious activity in real time. Unlike an Intrusion Detection System (IDS), which only alerts on suspicious activity, an IPS is inline and can take automated preventive actions such as dropping malicious packets or blocking traffic from offending IPs.

Intrusion Detection System (IDS)only detects and alerts but does not block traffic.

Packet Sniffercaptures and analyzes network packets for monitoring but lacks automated response capabilities.

SIEM (Security Information and Event Management)aggregates logs and alerts but does not directly block traffic.

TheSplunk Cybersecurity Defense Analyst Study Guideexplains that IPS devices are critical in defense-in-depth strategies to prevent intrusions before they cause damage.

Thefile_aclfield, which contains access controls associated with files affected by an event, is part of theEndpointdata model in Splunk. The Endpoint data model is designed to include information related to file access, process activity, and user activity on endpoints. Fields likefile_aclare critical for understanding permissions and potential security risks associated with file access and manipulation, which are key aspects of endpoint security monitoring.

Outlier detection is a fundamental technique in anomaly detection used in cybersecurity and data science. The method identifies data points that deviate significantly from the established norm or clusters of typical behavior. Data points that falloutside high-density clusters(the groups of similar data) are labeled asanomaliesbecause they do not conform to expected patterns.

Anomaliesindicate potentially suspicious or malicious activities, such as unusual login times, unexpected network traffic, or data exfiltration attempts.

Inconsistenciesis a broader term but lacks the precise meaning used in clustering or anomaly detection.

Baselinedrefers to establishing normal behavior patterns rather than identifying deviations.

Non-conformativesis not a standard term in this context.

According to theSplunk Cybersecurity Defense Analyst Study GuideandSplunk Machine Learning Toolkit documentation, detecting anomalies through outlier detection algorithms (e.g., DBSCAN, isolation forest) is key to threat detection and investigation workflows.