SPLK-5001 Splunk Certified Cybersecurity Defense Analyst Questions and Answers

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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TheSecurity Architectis primarily responsible for designing and selecting the security infrastructure, including tools and technologies that security analysts use to perform their tasks effectively. According to theSplunk Cybersecurity Defense Analyst Study Guide, the Security Architect role involves developing security solutions that align with organizational policies, compliance requirements, and operational needs. This role encompasses understanding both technical and business risks and ensuring that the deployed systems provide the necessary coverage for threat detection, prevention, and response.

TheSecurity Architectdevelops frameworks, specifies hardware and software components, and works closely with security engineers and SOC teams to implement these designs.

TheSecurity Engineertypically implements and maintains the infrastructure but does not design the overarching architecture.

TheSOC Manageroversees operations and personnel but does not focus on tool selection or infrastructure design.

TheThreat Intelligence Analystfocuses on gathering and analyzing threat data to inform defensive measures rather than designing infrastructure.

TheSplunk documentation and trainingexplicitly cite that security architecture involves planning and integrating security technologies and systems to create effective defense layers, supporting analyst workflows.

Thecoalescefunction in Splunk is used to return the first non-null value from a list of fields. The SPL| eval src = coalesce(src,machine_name)allows the analyst to dynamically populate thesrcfield with the value frommachine_nameifsrcis empty. This is a useful technique when dealing with inconsistent data sources or during field extraction issues, ensuring that the analyst can continue their investigation without missing critical events.

TheIdentity Centerdashboard in Splunk Enterprise Security is the primary interface for managing and reporting on user identities, including users currently on watchlists. This dashboard allows analysts to track user behavior, alerts, and notable events related to specific user identities, with the ability to view and manage watchlist membership.

Identity Centerprovides focused visibility into user-centric data and investigations.

Access Trackeris more general for access logs and authentication events but does not provide watchlist management.

Access CenterandIdentity Trackerare not standard dashboard names in Splunk ES.

TheSplunk Enterprise Security User Guiderecommends the Identity Center for watchlist monitoring and user-focused investigations.

The scenario described is an example ofLeast Frequency of Occurrence Analysis. This threat-hunting technique focuses on identifying events or behaviors that occur infrequently, under the assumption that rare activities could indicate abnormal or suspicious behavior. By filtering out users who log in frequently and focusing on those with rare login attempts, the threat hunter aims to identify potentially suspicious activity that warrants further investigation. This technique is particularly effective in detecting stealthy attacks that might evade more common detection methods.

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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.

TheevalSPL expression in Splunk supports several categories of functions, includingJSON functions(e.g.,spath),Text functions(e.g.,substr,trim), andComparison and Conditional functions(e.g.,if,case). However,Threat functionsis not a valid category within theevalcommand. Theevalcommand is primarily used for transforming and manipulating data in various ways, but it does not include a category specifically for threat-related functions.

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.

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 a successful Continuous Monitoring initiative, when an analyst identifies the need for more context or additional information, the request typically escalates to aSecurity Engineer. Security Engineers are responsible for the integration and configuration of additional data sources, and they can alter correlation rules or enhance data ingestion pipelines to provide the necessary context for analysts.

Security Engineer:

Manages and optimizes security tools and systems, including SIEM (like Splunk), and ensures that the necessary data sources are integrated into the monitoring environment.

Responsible for creating and tuning correlation rules and maintaining the infrastructure required for continuous monitoring.

Incorrect Options:

A. SOC Manager:Oversees the overall operations of the SOC but does not typically handle the technical integration of data sources.

B. Security Analyst:Primarily focuses on monitoring, detecting, and responding to security incidents, rather than configuring systems.

D. Security Architect:Focuses on the overall design of the security infrastructure, not on the day-to-day integration of data sources.

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.

Mean Time to Respond (MTTR)is a critical SOC metric measuring the average time from detection of a security incident to the point where it is resolved or dispositioned. According to theSplunk Enterprise Security and SOC Operations documentation, the typical start time for MTTR calculations iswhen a Notable Event is triggeredin Splunk ES. Notable Events represent the initial alert generated by correlation searches or detection mechanisms, marking the point where the SOC becomes aware of a potential incident requiring analyst review.

Starting MTTR at the time the malicious event occurs (option A) is impractical since detection is rarely instantaneous.

SOC Manager notification (option B) is too late in the workflow, as response metrics are analyst-focused.

Notifying end users (option D) is a post-response activity and does not define the start of response time.

This approach standardizes MTTR calculation across security operations and provides actionable insights into detection-to-response performance.

A threat hunt is an iterative process where a hypothesis is developed and tested against data in an environment to detect the presence of threats or adversarial tactics, techniques, and procedures (TTPs).

Understanding the Hypothesis:

The hypothesis here is that a threat actor might userundll32for proxy execution of malicious code and leverage Cobalt Strike for command and control (C2).

The hunt would involve looking for indicators of these specific activities in logs and artifacts like Sysmon logs, netflow, IDS alerts, and EDR data.

Search and Analysis:

The threat hunter performed an extensive search across multiple log sources and found no evidence of Cobalt Strike or the specific malicious activities hypothesized.

Evaluation of the Hypothesis:

If the hypothesis were proven true,the presence of Cobalt Strike or related artifacts would be detected.

However, the hypothesis was not proven; instead, the hunt provided strong evidence that Cobalt Strike and the hypothesized malicious activities are not present.

Successful Threat Hunt:

The goal of threat hunting is not always to find a threat but to gain confidence in the security posture of the environment.

Outcome Dcorrectly identifies that the hunt was successful because it provided strong evidence supporting the absence of the hypothesized threat, thus improving confidence in the organization's security.

TheInvestigation Managementframework in Splunk Enterprise Security (ES) is specifically designed to help analysts manage the lifecycle of security incidents. This framework allows for the identification of incidents from aggregated notable events, and provides tools to manage incident ownership, track the triage process, and monitor the state or status of ongoing investigations.

Investigation Managementoffers workflows to assign incidents, add comments, document findings, and close investigations, ensuring a structured response process.

TheNotable Eventframework refers to the generation of alerts based on correlation searches but does not provide incident lifecycle management.

Asset and Identityenrich data with contextual information but is not focused on incident management.

Adaptive Responserefers to automated actions but not the management framework itself.

Splunk’sEnterprise Security User Guidedetails Investigation Management as the central tool for orchestrating SOC response workflows and ensuring effective case management.

The key requirement is to calculate arunning total of distinct IP addressesover time, which involves both aggregation (stats dc(clientip) by _time) and cumulative calculation (streamstats dc(ipa) as "Cumulative total").

Option Acorrectly bins events by day (bin _time span=1d), calculates distinct counts (dc(clientip)) per day, and then applies streamstats to compute the running (cumulative) count across the timeline.

Option Bcalculates daily distinct counts but does not provide a running total.

Option Csimply formats a table without aggregation.

Option Daggregates by user, not by day, and misses cumulative counts.

TheSplunk Search Manualrecommends using streamstats with distinct count fields to obtain running totals for time series data.

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.

In Splunk,streaming commandsprocess each event individually as it is passed through the search pipeline and should be placed beforeaggregating commands, which operate on the entire set of results at once. This best practice ensures efficient processing and minimizes resource usage, as streaming commands reduce the amount of data before aggregation occurs. This approach leads to faster and more efficient searches. In contrast, the other options, such as using wildcards excessively or searching over all time, can lead to performance issues and excessive data processing.

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.

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.

InSplunk SOAR, the primary feature used to automate security workflows isPlaybooks. Playbooks are collections of automated and orchestrated tasks designed to streamline security processes such as alert triage, enrichment, containment, and remediation. By automating these routine tasks, playbooks enable analysts to focus on higher-level analysis and investigations.

Workbooksare documentation or guided investigations but are not automation tools.

Analytic Storiesare a feature in Splunk Enterprise Security to visualize complex attack scenarios, not workflow automation.

Adaptive Actionsare specific automated responses triggered within Splunk ES but are part of the ES platform, not SOAR’s primary automation mechanism.

TheSplunk SOAR official documentationdefines playbooks as modular, reusable workflows that can integrate multiple security tools and processes.

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 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.

Therexcommand in Splunk can be used to extract or replace data using regular expressions. To dynamically replace values with a specific pattern, such as replacing credit card numbers with "X", the mode needs to be set tosed. Thesedmode allows for string replacement within a field using regular expressions, enabling the substitution of matching patterns with a specified string.

Thetstatscommand in Splunk is designed for highly efficient querying of large datasets because it operates primarily onindexed metadatarather than the full raw event data. Indexed metadata consists of pre-extracted, summarized information such as field values, timestamps, and host information, which is stored in a highly optimized format. This allowststatsto return results much faster than thestatscommand, which processes raw event data after retrieval.

tstatsqueries accelerated data models or index metadata directly, reducing disk I/O and CPU usage.

statsoperates on raw events, which can be expensive in terms of resources for large datasets.

The SQL-like syntax oftstats(Option C) is a convenience but not the reason for its performance benefits.

tstatsdoes not search raw logs for extracted fields; that is the domain of thestatscommand.

Splunk’s official documentation and theCybersecurity Defense Analyst Study Guideemphasize the importance oftstatsfor performance-optimized queries in security operations and large-scale data analysis.

Adaptive Response is a feature in Splunk's Enterprise Security (ES) framework that allows security teams to automate actions and responses based on alerts or notable events. This feature is pivotal for orchestrating automated incident response processes, reducing the time between detection and response, and integrating Splunk with external systems to trigger appropriate actions.

Purpose:Adaptive Response enables the automation of specific tasks or workflows based on security events detected by Splunk ES. For instance, it can trigger actions such as isolating a compromised host, blocking IP addresses, or enriching data by querying additional sources when a notable event occurs.

Mechanism:When a notable event is identified within the Splunk platform, Adaptive Response can execute a series of predefined actions. These actions can be configured within the Splunk interface, allowing them to run automatically or with manual approval depending on the organization's needs. This capability is essential for streamlining security operations, especially in environments where quick response is critical.

Integration with External Applications:One of the key features of Adaptive Response is its ability to integrate with third-party security tools and solutions. This integration extends the capabilities of Splunk by allowing it to interact with other systems likefirewalls, intrusion prevention systems (IPS), endpoint detection and response (EDR) tools, and ticketing systems. This ensures a coordinated and comprehensive defense mechanism.

Usage in Security Operations:Security analysts often rely on Adaptive Response for managing and automating common security tasks, such as:

Quarantine or isolate a hostin response to malware detection.

Trigger a full disk scanwhen suspicious activity is detected.

Notify relevant stakeholdersthrough ticketing systems or direct communication tools.

Update firewall rulesto block traffic from a suspicious IP address.

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.