ECCouncil Certified Threat Intelligence Analyst (CTIA) 312-85 Exam Dumps: Updated Questions & Answers (December 2025)

True attribution in the context of cyber threats involves identifying the actual individual, group, or nation-state behind an attack or intrusion. This type of attribution goes beyond associating an attack with certain tactics, techniques, and procedures (TTPs) or a known group and aims to pinpoint the real-world entity responsible. True attribution is challenging due to the anonymity of the internet and the use of obfuscation techniques by attackers, but it is crucial for understanding the motive behind an attack and for forming appropriate responses at diplomatic, law enforcement, or cybersecurity levels.

In the Analysis of Competing Hypotheses (ACH) process, the stage where Mr. Bob is applying analysis to reject hypotheses and select the most likely one based on listed evidence, followed by preparing a matrix with screened hypotheses and evidence, is known as the 'Refinement' stage. This stage involves refining the list of hypotheses by systematically evaluating the evidence against each hypothesis, leading to the rejection of inconsistent hypotheses and the strengthening of the most plausible ones. The preparation of a matrix helps visualize the relationship between each hypothesis and the available evidence, facilitating a more objective and structured analysis.

To retrieve historical information about a company's website, including content that may have been removed or altered, Alison should use the Internet Archive's Wayback Machine, accessible at https://archive.org . The Wayback Machine is a digital archive of the World Wide Web and other information on the Internet, providing free access to snapshots of websites at various points in time. This tool is invaluable for researchers and analysts looking to understand the evolution of a website or recover lost information.

For gathering strategic threat intelligence that provides a high-level overview of the current cybersecurity posture, potential financial impacts of cyber activities, and overarching threats, sources such as Open Source Intelligence (OSINT), Cyber Threat Intelligence (CTI) vendors, and Information Sharing and Analysis Organizations (ISAOs)/Information Sharing and Analysis Centers (ISACs) are invaluable. OSINT involves collecting data from publicly available sources, CTI vendors specialize in providing detailed threat intelligence services, and ISAOs/ISACs facilitate the sharing of threat data within specific industries or communities. These sources can provide broad insights into threat landscapes, helping organizations understand how to align their cybersecurity strategies with current trends and threats.

The stage described involves analyzing gathered information and understanding known threats. This aligns with the Known Knowns stage.

Known Knowns represent threats that have already been identified, understood, and documented. Analysts in this stage work with existing data to refine and interpret known indicators or threat actor behaviors.

Why the Other Options Are Incorrect:

Unknown unknowns: Threats that are entirely unknown and undetectable with current knowledge.

Known unknowns: Threats suspected to exist but not yet clearly identified.

Unknown knowns: Information that exists but has not been analyzed or recognized as relevant.

Conclusion:

Michael is analyzing existing and understood threat data, placing him in the Known Knowns stage of cyber-threat intelligence.

Final Answer: D. Known knowns

Explanation Reference (Based on CTIA Study Concepts):

In the CTIA framework, known knowns refer to threats that are fully understood and documented, forming the basis for structured analysis.

The phase described involves defining and setting up what intelligence needs to be collected before the actual collection process begins. This aligns with the Intelligence Requirements phase of the threat intelligence lifecycle.

Intelligence Requirements define what information is needed and why it is needed to support decision-making or intelligence production. These requirements guide the collection and analysis processes by specifying the goals and priorities of intelligence gathering.

Kira’s focus should be on determining the exact intelligence needs that will later drive the production of actionable insights.

Why the Other Options Are Incorrect:

A. Production requirements: Concerned with how intelligence reports and outputs will be formatted and disseminated after analysis, not what data should be collected.

C. Business requirements: Focus on organizational goals or project objectives, not specific intelligence needs.

D. Collection requirements: Define how and from where to gather data, but are based on intelligence requirements, which come first.

Conclusion:

Kira should define Intelligence Requirements, which determine what must be collected to fulfill intelligence production needs.

Final Answer: B. Intelligence requirements

Explanation Reference (Based on CTIA Study Concepts):

In the CTIA threat intelligence lifecycle, defining intelligence requirements is the first stage and establishes the foundation for effective intelligence collection and production.

A gateway in a network functions as a node that routes traffic between different networks, such as from a local network to the internet. In the context of cyber threats, a gateway can be utilized to monitor and control the data flow to and from the network, helping in the identification and analysis of malware communications, including traffic to external command and control (C2) servers. This makes it an essential component in detecting installed malware within a network by observing anomalies or unauthorized communications at the network's boundary. Unlike repeaters, hubs, or network interface cards (NICs) that primarily facilitate network connectivity without analyzing the traffic, gateways can enforce security policies and detect suspicious activities.

The phase where threat intelligence analysts convert raw data into useful information by applying various techniques, such as machine learning or statistical methods, is known as 'Processing and Exploitation'. During this phase, collected data is processed, standardized, and analyzed to extract relevant information. This is a critical step in the threat intelligence lifecycle, transforming raw data into a format that can be further analyzed and turned into actionable intelligence in the subsequent 'Analysis and Production' phase.

The "known unknowns" stage in cyber-threat intelligence refers to the phase where an analyst has identified threats but the specific details, implications, or full nature of these threats are not yet fully understood. Michael, in this scenario, has obtained information on threats and is in the process of analyzing this information to understand the nature of the threats better. This stage involves analyzing the known data to uncover additional insights and fill in the gaps in understanding, thereby transitioning the "unknowns" into "knowns." This phase is critical in threat intelligence as it helps in developing actionable intelligence by deepening the understanding of the threats faced.

In this scenario, the robot learns through trial and error, receiving positive or negative feedback to improve its actions over time. This describes Reinforcement Learning (RL).

Reinforcement Learning is a machine learning approach where an agent interacts with an environment to achieve a goal. It learns optimal behavior by taking actions, receiving feedback (rewards or penalties), and refining its strategy to maximize cumulative rewards.

This method is widely used in robotics, game theory, and autonomous systems where explicit labeled data is not available, but performance can be measured by rewards.

Why the Other Options Are Incorrect:

Unsupervised learning: Involves finding patterns or clusters in unlabeled data without feedback.

Semi-supervised learning: Combines a small set of labeled data with a large amount of unlabeled data.

Supervised learning: Requires labeled datasets to train models on known input-output pairs.

Conclusion:

The robot uses Reinforcement Learning to optimize its performance based on feedback loops.

Final Answer: C. Reinforcement learning

Explanation Reference (Based on CTIA Study Concepts):

Under the CTIA topic “Machine Learning in Threat Intelligence,” reinforcement learning is defined as feedback-driven learning through reward and punishment signals.