Free ECCouncil 312-39 Practice Exam with Questions & Answers | Set: 6

Static analysis is the correct approach when the requirement is to understand what the script is intended to do without executing it. For PowerShell embedded in documents, static analysis includes extracting the script content, de-obfuscating it (common techniques include base64 decoding, string reconstruction, and analyzing encoded commands), and reviewing functions, URLs/IPs, file paths, registry keys, and command-line arguments. This allows the SOC to determine likely behaviors such as downloading payloads, establishing persistence, credential theft, or disabling security controls—without risking system impact. Dynamic or behavioral analysis involves running code in a controlled sandbox to observe actions, which can be valuable but violates the constraint “without triggering it,” and can be risky if containment fails or the malware has evasive logic. Network traffic analysis can help once execution has occurred or in a sandbox run, but it cannot fully explain logic that never ran. Static analysis is also useful for creating detections (hashes, strings, YARA-like patterns, command-line indicators) and for scoping across the environment by searching for matching script fragments or document markers.

Unstructured hunting is best suited when you have a weak but concerning signal (like unusual encrypted bursts to an unfamiliar IP) without a clear hypothesis tied to a known technique or indicator. In this scenario, there are no known IoCs and no alert from traditional tools, so the hunt starts from an intuition-driven anomaly and develops into hypotheses through exploration: examining which hosts are involved, what processes initiate connections, whether destinations vary, whether the behavior aligns with legitimate business tooling, and whether there are associated persistence or credential access signals. This is characteristic of unstructured hunts—analyst-driven exploration based on suspicious observations. Structured hunting typically starts with a defined hypothesis or known adversary behavior mapped to a framework and uses planned queries to confirm or refute it. Situational/entity-driven hunting focuses on a specific entity (a VIP user, crown-jewel server) or a known incident context. Reactive hunting is driven by alerts or confirmed incidents. Here, the hunt is prompted by an anomaly without predefined IoCs or alerts, making unstructured hunting the most appropriate approach to uncover IoAs and then map findings to adversary behaviors.

HTTP status codes are categorized into fiveclasses, where each class is represented by the first digit of the status code. The 5XX series of status codes indicates server errors, which means that the server is aware that it has encountered an error or is otherwise incapable of performing the request. Common examples of 5XX status codes include 500 (Internal Server Error), 501 (Not Implemented), 502 (Bad Gateway), etc. These indicate that the request was valid, but the server failed to fulfill the request due to some issue on the server side.

The scenariodepicted suggests an attacker is injecting a script into the URL of the website “www.example.com” which triggers an alert message. This behavior is characteristic of a Cross-site Scripting (XSS) attack. In XSS attacks, attackers exploit vulnerabilities in web applications to inject malicious scripts into web pages viewed by other users. The injected scripts can steal user data, deface web pages, or redirect users to malicious sites.

The specific attack vector here involves the attacker adding a script to the URL that causes the website to display an alert message. This indicates that the website is not properly sanitizing its inputs, which is how the attacker is able to execute the script in the context of the user’s browser session.

Machine learning is the key AI technology for detecting suspicious activity without predefined signatures by learning patterns from data and identifying anomalies, outliers, and high-risk behaviors. In SOC contexts, ML can model normal baselines for users, hosts, and applications, then flag deviations such as unusual authentication patterns, unexpected data transfers, or rare process behaviors—capabilities that are particularly useful against zero-days and APTs that evade signature-based tools. NLP is valuable for processing human-language text (tickets, email content, narrative logs), but it is not the primary engine for behavioral anomaly detection across telemetry. Static IP blocking is a manual control that can be bypassed and does not “learn” or adapt. Heuristic-based signatures still rely on predefined patterns, even if they are generalized, and are not the same as adaptive learning. From a SOC perspective, ML can improve detection coverage when combined with strong telemetry and tuning, but it also requires governance: monitoring model drift, validating outputs, and ensuring explainability for analysts. Because the scenario prioritizes signatureless detection and real-time adaptation, machine learning is the best fit.

This alert is generated because the activity deviates significantly from the server’s established baseline, which is the hallmark of anomaly-based detection. The SIEM is not matching a known signature (so it is not signature-based), and the prompt emphasizes “deviations from normal behavior profile,” which typically means statistical profiling, baselining, or behavior analytics detecting outliers in volume, timing, destination, or frequency. While rule-based detections can also trigger on thresholds, the question explicitly frames the logic as “normal behavior profile,” which implies adaptive baselines rather than a fixed rule alone. Heuristic detection refers to generalized patterns or suspicion scoring, but here the core mechanism is abnormality versus historical norms (5 MB/hour typical vs 500 MB in 10 minutes). From a SOC triage perspective, anomaly alerts require quick validation: confirm the external destination reputation/ownership, verify whether the transfer aligns with authorized jobs, check change tickets, and correlate with authentication/process activity on the database host. Anomaly-based detection is especially valuable for data exfiltration because attackers can avoid known signatures, but they often struggle to mimic normal data movement patterns at scale.

User and Entity Behavior Analytics (UEBA) is a cybersecurity process that uses machine learning, algorithms, and statistical analyses to detect abnormal behavior of users and entities within an organization. UEBA systems analyze patterns of behavior and can identify anomalies that deviate from the norm, which could indicate a potential security threat.

Anomaly-based detection is the technique that aligns with UEBA’s functionality. It contrasts with:

Rule-based detection, which relies on predefined rules to detect threats.

Heuristic-based detection, which uses experience-based techniques.

Signature-based detection, which depends on known patterns orsignatures of malware to identify threats.

Anomaly-based detection systems are designed to be dynamic, continuously learning and establishing what is considered normal to identify deviations. This approach is particularly effective in identifying previously unknown threats, hence its alignment with UEBA.

Operational threat intelligence involves gathering detailed information about specific threats to an organization. It is often derived from various sources, including human intelligence, social media, chat rooms, and other platforms where data about malicious activities can be collected. This type of intelligence is focused on understanding the specifics of a threat, such as the tactics, techniques, and procedures (TTPs) of threat actors, and is used to inform the organization about imminent or ongoing attacks.

In the scenario described, John, a threat analyst, is collecting information from diverse sources to create a report on malicious activity. This aligns with the practices of operational threat intelligence, which is concerned with the details of particular threats and activities, rather than broader strategic trends or technical indicators.

Azure Monitor is the Azure-native platform for collecting, analyzing, and visualizing telemetry across Azure resources, including metrics and logs from infrastructure, applications, and services. For SOC needs, it provides centralized observability: resource metrics, activity logs, diagnostic logs, and integration with log analytics for query and alerting. This supports both performance monitoring and security monitoring by enabling detection of unusual behaviors (unexpected spikes, anomalous access patterns) and providing dashboards and alerting to support rapid response. Azure Firewall is a network security control focused on traffic filtering and policy enforcement; it does not serve as the comprehensive telemetry collection and visualization layer for all Azure resources. Azure Policy focuses on governance and compliance enforcement by evaluating and enforcing resource configuration rules; it’s important but not the main telemetry analysis solution. Azure Active Directory is the identity service (now commonly referred to as Entra ID) and is essential for authentication/authorization, but it is not the cross-resource monitoring platform. Since the question emphasizes broad telemetry collection, analysis, and visualization across Azure resources for continuous monitoring, Azure Monitor is the correct service.

CrowdStrike FalconTM Orchestrator is a tool designed to automate the response to security incidents, including those involving web applications. It integrates with the CrowdStrike Falcon platform to provide a range of capabilities such as real-time response, incident investigation, and remediation. This makes it suitable for recovering from web application incidents by allowing security teams to quickly identify, understand, and resolve threats.

References The EC-Council’s Certified SOC Analyst (CSA) course materials and study guides discuss various tools and their applications in incident response. CrowdStrike FalconTM Orchestrator is recognized in the industry for its incident response capabilities, aligning with the learning resources provided by EC-Council for SOC Analysts.