Cisco 300-220 Exam Dumps

The correct answer is Structured threat hunting. In this scenario, the SOC team has already confirmed malicious activity---a compromised user account, anomalous VPN access, and indicators consistent with data exfiltration. Once an incident has been validated and attributed to adversary behavior, the next professional step is to perform structured threat hunting to uncover additional attacker actions across the environment.

Structured threat hunting is hypothesis-driven and based on known attacker tactics, techniques, and procedures (TTPs), often mapped to frameworks such as MITRE ATT&CK. Here, the team can form hypotheses like: ''If the adversary accessed the file server for exfiltration, they may have also attempted lateral movement, persistence, or privilege escalation.'' Analysts then systematically query endpoint, identity, VPN, file server, and network telemetry to confirm or disprove these hypotheses.

Option A (Unstructured) is typically used at the earliest stages when little is known and analysts are exploring weak signals or anomalies without a defined adversary model. That phase has already passed in this case. Option B (AI-driven) refers to tooling or analytics methods rather than a threat hunting methodology. Option C (Proactive) is a general mindset applied to all hunting activities, not a specific hunting type used to investigate known attacker behavior.

From a professional SOC and threat hunting perspective, structured hunting enables full attack chain reconstruction. It helps identify secondary objectives such as data staging locations, additional compromised accounts, persistence mechanisms, and command-and-control activity. The outcome is a more complete understanding of the breach, improved containment, and stronger detection logic for future incidents.

This approach reflects mature security operations: once compromise is confirmed, hunt the adversary---not just the alert. Structured threat hunting ensures attackers are fully evicted and prevents repeat compromise through overlooked footholds.

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 answer is Connection status. In this scenario, the key challenge for the security team is differentiating legitimate outbound traffic from malicious or DDoS-related traffic originating from the same web server. Since both types of traffic coexist in the logs, analysts must rely on an attribute that meaningfully distinguishes normal behavior from abnormal patterns.

The exhibit shows numerous TCP connections from the web server to many different external IP addresses, with varying TCP states such as ESTABLISHED, TIME_WAIT, and FIN_WAIT. These connection states are highly valuable for threat hunting and network analysis. During DDoS activity---especially reflected or amplification-style attacks, or when a server is abused as part of an attack---connections often remain half-open, rapidly transition to TIME_WAIT, or fail to fully establish. In contrast, legitimate web traffic typically results in stable, short-lived ESTABLISHED sessions that follow predictable patterns.

Option B (destination port) is not useful here because most web traffic---both legitimate and malicious---commonly uses ports 80 or 443. Option C (IP address of the web server) provides no filtering value because all traffic already originates from that server. Option D (protocol) is also ineffective, as both normal and DDoS traffic in this case use TCP.

From a professional SOC and threat hunting standpoint, connection state analysis is a foundational technique for detecting volumetric attacks, beaconing behavior, and abnormal session churn. By filtering logs based on connection status, analysts can quickly isolate suspicious patterns such as excessive short-lived connections, abnormal teardown behavior, or asymmetric session states that are characteristic of DDoS-related activity.

This approach aligns with mature threat hunting practices: when indicators overlap, pivot to behavioral attributes. Connection status provides the necessary behavioral signal to separate expected traffic from attack traffic and supports faster, more accurate incident response.

The correct answer is standardizing hunt documentation and hypotheses. Mature threat hunting programs move beyond ad-hoc, intuition-driven efforts.

Standardization enables:

Knowledge sharing

Consistent methodology

Repeatable hunts

Easier onboarding of new analysts

Option A and B support operations but do not improve hunting maturity. Option D is unrealistic and risky.

By documenting hypotheses, data sources, queries, findings, and outcomes, organizations institutionalize knowledge and continuously improve detection capabilities.

This is a defining characteristic of high-maturity threat hunting programs.

Therefore, option C is correct.

The correct answer is behavioral analysis of outbound traffic patterns. Advanced attackers intentionally use standard protocols such as HTTP and HTTPS to blend exfiltration traffic with normal activity.

Hash-based and signature-based methods are ineffective because:

No malware may be present

Traffic appears legitimate

Infrastructure is frequently rotated

Behavioral analysis detects anomalies such as:

Unusual data transfer volumes

Abnormal session timing

Beaconing patterns

Rare destinations

This approach aligns with network threat hunting best practices and forces attackers to significantly alter behavior, increasing adversary cost.

Therefore, option B is correct.