The correct answer isit reveals operational discipline and intent. Attribution relies heavily on understandinghow attackers think and operate, not just the tools they use.

Operational discipline—such as careful reconnaissance, avoiding noisy exploitation, and operating during business hours—is ahuman behavioral pattern. These patterns are far more stable than infrastructure or malware and often correlate strongly with specific threat actor groups.

Option A and D focus on tooling, which changes frequently. Option B relates to persistence, not attribution.

Threat intelligence professionals use operational characteristics to distinguish between opportunistic criminals and advanced adversaries. Business-hour activity, careful lateral movement, and deliberate escalation often indicatetargeted intrusions, espionage, or financially motivated but sophisticated actors.

This information helps analysts align observed behavior with known threat actor profiles, improving attribution confidence. Thus, optionCis correct.

The correct answer isendpoint process execution and memory access events. During thedata collection and processing phase, the goal is to gatherhigh-fidelity telemetrythat supports hypothesis validation.

Credential harvesting often occurswithout dropping malwareand instead relies on:

Memory scraping

LSASS access

Credential dumping tools

In-memory execution

Cisco Secure Endpoint provides deep visibility into:

Process creation and parent-child relationships

Memory access attempts

Privilege abuse

Fileless execution

Option A provides enrichment but not raw behavioral evidence. Option C supports context but does not replace endpoint telemetry. Option D is reactive and unreliable for structured hunts.

Within theCBRTHD threat hunting lifecycle, this phase emphasizesevidence over indicators. Without endpoint execution and memory telemetry, hunters cannot reliably confirm credential access techniques.

This aligns withMITRE ATT&CK Credential Accesstactics and Cisco’s emphasis onendpoint behavioral analytics.

Thus,Option Bis the correct answer.

The correct answer isdocument findings and create permanent detections. While containment actions are necessary, they areincident response tasks, not threat hunting outcomes.

Cisco’s threat hunting lifecycle emphasizes that once malicious behavior is confirmed, teams must:

Document attacker techniques

Identify detection gaps

Convert findings into automated detections

Options A and B are tactical responses that address the current incident but do not prevent recurrence. Option D delays improvement and increases risk.

Operationalizing hunt findings ensures:

Repeated attacker behavior is detected automatically

Future dwell time is reduced

SOC maturity increases

This step directly aligns with theCBRTHD blueprint’s focus on continuous improvement and feedback loopsbetween hunting and monitoring.

Therefore,Option Cis the correct answer.

The correct answer isinternal systems authenticating to multiple hosts using SMB in a short time. This behavior is a classic indicator ofcredential-based lateral movement.

When attackers obtain valid credentials, they often move laterally by:

Accessing administrative shares (e.g., C$, ADMIN$)

Using SMB, WMI, WinRM, or RDP

Authenticating to multiple systems rapidly

Cisco Secure Network Analytics excels at identifyingeast-west traffic anomalies, which are central to lateral movement detection. A single host authenticating to many internal systems over SMB in a short time deviates strongly from normal user behavior.

Option A relates to external traffic, not lateral movement. Option C may indicate command-and-control or staging but not lateral movement. Option D aligns more with beaconing behavior.

This technique aligns withMITRE ATT&CK – Lateral Movementand is explicitly covered in theCBRTHD blueprintunder network-based threat hunting.

Thus,Option Bis the correct answer.

The correct answer isMonitoring failed AWS console login attempts. The Bash script shown in the exhibit is clearly designed toparse AWS CloudTrail logsand extract specific authentication-related events.

Breaking down the script behavior from a professional cloud security perspective:

gunzip -c *.json.gz indicates the script is processingcompressed CloudTrail log files, which are typically stored in .json.gz format.

jq -c '.Records[]' parses individual CloudTrail records, a common approach when analyzing AWS activity logs.

The filter conditions explicitly check for:

eventSource == "signin.amazonaws.com"

eventName == "ConsoleLogin"

responseElements.ConsoleLogin == "Failure"

These fields are definitive indicators offailed AWS Management Console login attempts. Additionally, the script extracts contextual fields such as:

Event time

Source IP address

Error message

AWS region

Username

MFA usage status

This data is exactly what security teams use to detectcredential abuse, password spraying, brute-force attempts, and compromised IAM accounts. Monitoring failed console logins is a foundational cloud threat hunting activity, especially for identifying early stages of account takeover.

Option B is incorrect because the script does not establish AWS CLI sessions or authenticate to accounts. Option C is incorrect because instance errors would involve services like ec2.amazonaws.com and different event names. Option D is incorrect because the script is analyzing—not archiving—records, and it applies filtering logic rather than storage or lifecycle management.

From a threat hunting and cloud security standpoint, this script supportsidentity-focused detection, which is critical in AWS environments where IAM misuse is one of the most common initial access vectors. It aligns withMITRE ATT&CK – Credential Access and Initial Access, particularly techniques involving valid account abuse.

In summary, the script’s clear purpose is tomonitor failed AWS console login attempts, makingOption Athe correct and professionally validated answer.

The correct answer isAttack trees. Attack trees are uniquely suited for modelingmulti-step adversary behavior, which is essential when analyzing complex attack chains such as account takeover followed by data exfiltration.

Attack trees begin with ahigh-level attacker goal(for example, “Exfiltrate customer data”) and then break that goal into multiple branches representing different paths an attacker could take. These paths can include credential compromise, API abuse, privilege escalation, lateral movement, and persistence. This structure mirrors how real adversaries think and operate.

Option A (STRIDE) is useful for identifying broad threat categories—such as spoofing, tampering, or information disclosure—but it does not naturally capturesequential attack paths. Option B (CVSS) focuses on vulnerability severity scoring, not adversary behavior. Option D (DREAD) assesses risk impact but does not visualize how attacks unfold across systems.

For threat hunters and defenders, attack trees provide ashared mental modelbetween architects, SOC teams, and red teams. They directly inform detection engineering by highlightingcritical choke pointswhere attacker behavior must occur, such as token abuse, API enumeration, or anomalous role assumption in cloud environments.

In modern cloud security, where breaches often involvemultiple low-severity issues chained together, attack trees offer far greater strategic value than component-by-component analysis. They also align closely withMITRE ATT&CK mapping, enabling defenders to translate threat models into actionable hunts.

Thus, optionCis the most appropriate and professionally validated answer.

The correct answer isNetwork/host artifacts. To understand why, it is important to map the observed attacker behavior to thePyramid of Pain, a model that ranks indicators by how difficult they are for adversaries to change once detected.

In this scenario, the adversary is usingNmap OS fingerprinting, which involves sending carefully crafted packets and analyzing responses (TCP/IP stack behavior, TTL values, window sizes, flags, and timing characteristics). These behaviors leave behindnetwork and host artifacts, such as distinctive scan patterns, abnormal TCP flag combinations, OS fingerprinting probes, and consistent tool-specific traffic signatures.

On the Pyramid of Pain:

IP addresses (D)sit at the very bottom. Attackers can trivially change IPs using VPNs, proxies, or botnets.

Port probes (B)andUDPs (A)represent low-level indicators that are also easy to modify. An attacker can change scan ports, protocols, or scan timing with minimal effort.

Network/host artifacts (C)sit significantly higher. These include tool-generated behaviors, protocol anomalies, OS fingerprinting patterns, and scan logic inherent to tools like Nmap. Changing these requires attackers to reconfigure tools, write custom scanners, or significantly alter their operational approach.

From a threat hunting and SOC maturity perspective, detecting and alerting onnetwork and host artifactsforces attackers to expend more time and resources, increasing their operational cost. This aligns with the core objective of the Pyramid of Pain:maximize adversary pain by detecting behaviors, not easily replaceable indicators.

Professionally mature SOC teams focus on identifying scanning techniques (e.g., Nmap OS detection, TCP ACK probes, UDP probes) rather than blocking individual IPs. These detections are resilient, scalable, and effective against both commodity attackers and advanced adversaries.

In short, while IPs and ports are useful for short-term containment,network and host artifacts provide the highest-value indicators in this scenario, makingCthe correct answer.