Free Cisco 300-220 Exam Dumps September 2026

The correct answer is it reflects the attacker's operational preferences. Attribution relies on understanding how attackers operate, not just what tools they use.

Operational preferences---such as avoiding PowerShell logging, disabling AMSI, and favoring WMI---are behavioral signatures. These patterns often persist across campaigns and are documented in threat intelligence reports associated with specific adversaries.

Option A is incorrect because malware families change frequently. Option B is unreliable due to infrastructure rotation. Option D is unrelated to post-access tradecraft.

Professional attribution focuses on:

Execution methods

Defensive evasion choices

Tooling preferences

Workflow consistency

Mapping these behaviors to MITRE ATT&CK techniques enables analysts to compare findings against known threat actor profiles. This provides higher confidence attribution than artifact-based indicators.

Thus, option C is the correct answer.

The correct answer is document findings and operationalize detections. In Cisco's threat hunting methodology, confirmation of malicious activity is not the end of the hunt.

The most critical next step is to:

Document attacker behavior

Identify detection gaps

Create or improve SIEM, EDR, or NDR detection rules

This ensures the organization does not repeatedly rediscover the same threat. Options C and D are incident response and communication activities, not threat hunting lifecycle steps. Option A skips the crucial improvement phase.

The CBRTHD blueprint strongly emphasizes:

Continuous improvement

Feedback loops

Detection engineering

By operationalizing findings, the SOC increases maturity and forces adversaries to change tactics.

Therefore, Option B is correct.

The correct answer is detecting abnormal authentication behavior across VPN and cloud access. This outcome targets behavioral detection, which sits significantly higher on the Pyramid of Pain than static indicators.

Options A and C rely on domains and hashes, which attackers can trivially change. Option D is a response action, not a hunting outcome.

Credential misuse is one of the most common initial access vectors, especially in cloud and remote-access environments. Detecting abnormal authentication behavior---such as:

Impossible travel

Unusual login times

Excessive failed logins

Geographic anomalies

forces attackers to change how they operate, not just what infrastructure they use.

Cisco tools such as:

Secure Network Analytics

Secure Endpoint

Secure Firewall

Identity telemetry via VPN and SSO

enable this higher-fidelity detection approach. This aligns directly with CBRTHD blueprint objectives focused on identity-based threat hunting.

Therefore, Option B is correct.

The correct answer is correlating attacker behavior across multiple MITRE ATT&CK techniques. This approach focuses on behavioral detection, which is the cornerstone of effective threat hunting and advanced security operations.

Attackers who abuse legitimate administrative tools---often referred to as living-off-the-land techniques---intentionally avoid malware-based detections. File hashes, signatures, and known indicators provide minimal value because there may be no malicious files at all. Options A and D sit at the lowest levels of the Pyramid of Pain, making them easy for adversaries to evade.

By correlating behavior across multiple ATT&CK techniques---such as credential access, lateral movement, privilege escalation, and command execution---defenders detect how the attacker operates rather than what tools they use. This forces adversaries to fundamentally change tradecraft, which is costly, risky, and time-consuming.

Option C improves visibility but does not inherently raise attacker cost. Threat intelligence feeds are reactive and often lag behind active campaigns.

From a professional threat hunting perspective, correlating multiple low-signal behaviors into a high-confidence attack pattern is how mature SOCs detect stealthy intrusions. This method also supports scalable detection engineering, improved alert fidelity, and reduced false positives.

This strategy directly aligns with higher tiers of the Threat Hunting Maturity Model and the top of the Pyramid of Pain, making option B the correct answer.

The correct answer is Credential Access. In the MITRE ATT&CK framework, password spraying is classified under the Credential Access tactic (TA0006), specifically technique T1110.003 -- Password Spraying. This classification is based on the attacker's primary objective: gaining valid credentials by systematically attempting a small number of common or weak passwords across many user accounts.

Password spraying differs from brute-force attacks in that it intentionally avoids rapid or repeated attempts against a single account, thereby evading account lockout controls and basic detection mechanisms. Instead, attackers ''spray'' one password (for example, Winter2025! or Password123) across a large number of users, exploiting the likelihood that at least one account will use that password.

Although successful password spraying often leads to initial access, MITRE classifies it under Credential Access because the technique's defining action is the acquisition of credentials, not the system entry itself. Initial access is the outcome, while credential theft is the method. This distinction is critical for threat hunters, as it guides where detections and controls should be focused.

From a professional threat hunting perspective, defenders monitor authentication telemetry such as failed and successful logins across identity providers, VPNs, cloud services, and email platforms. Indicators include multiple authentication failures across many accounts from a single source IP, followed by one or more successful logins. Identity-centric logging and anomaly detection are foundational here, reinforcing the principle that identity is the primary attack surface in modern environments.

Understanding password spraying as a credential access technique helps organizations prioritize protections such as strong password policies, MFA enforcement, adaptive authentication, and detection logic tuned for low-and-slow authentication abuse.

The correct answers are B. Processes in nonstandard file paths and C. Common processes with modified names. These two detection rules are highly effective for identifying malicious processes spawned by phishing-delivered malware.

Phishing payloads commonly drop executables into nonstandard directories such as AppData, Temp, Downloads, or user profile subfolders. Legitimate Windows binaries rarely execute from these locations. Monitoring for process execution from such paths is a proven technique for detecting malware loaders, credential stealers, and post-exploitation tooling.

Additionally, attackers frequently masquerade malware as legitimate processes by using slightly modified names, such as lsasss.exe, svch0st.exe, or expl0rer.exe. These tactics are designed to evade casual inspection and basic allowlisting. Detecting common Windows process names with anomalies---such as incorrect spelling, unexpected parent processes, or abnormal execution paths---is a high-fidelity behavioral signal.

Option A is too broad; nearly all processes are created by users directly or indirectly, making it noisy. Option D (process ownership changes) and Option E (startup time changes) are less relevant to detecting credential-harvesting processes at execution time and may miss the initial malicious activity.

From a threat hunting and detection engineering perspective, options B and C align with MITRE ATT&CK -- Defense Evasion and Credential Access techniques. These rules focus on behavioral detection, not static indicators, making them resilient against attacker variation.

In short, detecting where a process runs from and what it pretends to be provides strong coverage against phishing-delivered malware, making B and C the correct and professionally validated choices.