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.

The fastest SPL (Search Processing Language) search in Splunk is one that leverages index and sourcetype constraints upfront to minimize the amount of data scanned, thereby reducing search latency significantly. According to theSplunk Cybersecurity Defense Analyst Study Guide, searches that specifyindexandsourcetypein the base search filter data more efficiently by restricting the search scope immediately at the indexing layer before any processing occurs. This contrasts with searches that filter fields only after the raw data is retrieved, which is less efficient.

Option D explicitly specifies theindex-networkandsourcetype=netflow, combined with the exact field filterssrc_ip=1.2.3.4,src_port=2938, andprotocol=top. This precise filtering directly reduces the dataset Splunk has to scan.

Options A and C use theindex-networkbut filter some fields after the initial search (e.g., using search src_ip=1.2.3.4 after stats aggregation), causing unnecessary processing overhead.

Option B lacks any index or sourcetype specification, meaning Splunk performs a much broader search across all indexes and data, which is slower.

TheSplunk Cybersecurity Defense Analyst official documentationemphasizes that applying early filtering with index and sourcetype limits is crucial for search performance, particularly in high-volume network data scenarios.

Web Proxy Logsare the most direct and relevant data source for determining whether a user visited a malicious website. Proxy servers act as intermediaries for user HTTP/HTTPS requests and record detailed metadata about web traffic including URLs visited, timestamps, user IDs, and other HTTP headers.

Web Proxy Logsprovide explicit visibility into outbound web requests from users, which is essential for detecting visits to malicious domains or IPs.

Active Directory Logstrack authentication and directory service activities but do not record web browsing data.

Intrusion Detection Logsmonitor suspicious network or host behaviors but may not have precise records of web destinations.

Web Server Logslog incoming requests to a specific server but do not reflect user browsing outside that server.

Splunk’s security analyst documentation emphasizes leveraging proxy logs for user activity tracking and threat detection related to web-based threats.

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.

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.

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.

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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