SPLK-5001 Exam Dumps : Splunk Certified Cybersecurity Defense Analyst

TheContinuous Monitoringcycle begins with theDefine and Predictphase, where the security team establishes what needs to be monitored (defining assets, risks, controls) and predicts potential threats and vulnerabilities. This foundational phase sets objectives and scope for the monitoring program.

Subsequent phases likeMonitor and Protect,Assess and Evaluate, andRespond and Recoverfollow the initial definition.

This phased approach aligns with frameworks such as NIST’s Risk Management Framework and Continuous Monitoring guidelines.

Splunk’s cybersecurity documentation highlights the importance of this first phase for establishing a proactive and informed security posture.

Unusual Traffic Patterns:

The key observation here is that one of the servers is sending out a significantly large amount of data to a single external system, with no corresponding increase in incoming traffic.

Possible Threat Activities:

A. Data Exfiltration:

This scenario typically aligns with data exfiltration, where an attacker has successfully compromised a system and is sending out large volumes of stolen data to an external server.

Data exfiltration often involves consistent or large data transfers over time to an external IP address, which matches the description provided.

B. Network Reconnaissance:

While reconnaissance involves scanning and probing, it generally does not produce large outbound data flows but rather small, frequent connection attempts or queries.

C. Data Infiltration:

Infiltration would involve incoming data to the compromised server, which contradicts the scenario as there is no observed increase in incoming traffic.

D. Lateral Movement:

Lateral movement would involve traffic between internal systems rather than large amounts of data being sent to an external system.

Scenario Analysis:Conclusion:Given the evidence of large data transfers to a single external system without corresponding inbound traffic,data exfiltrationis the most likely scenario. This suggests that an adversary has compromised the server and is extracting valuable or sensitive data from the organization.

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.