312-85 Exam Dumps : Certified Threat Intelligence Analyst (CTIA)

In the context of bulk data collection for threat intelligence, data is often initially collected in an unstructured form from multiple sources and in various formats. This unstructured data includes information from blogs, news articles, threat reports, social media, and other sources that do not follow a specific structure or format. The subsequent processing of this data involves organizing, structuring, and analyzing it to extract actionable threat intelligence. This phase is crucial for turning vast amounts of disparate data into coherent, useful insights for cybersecurity purposes.References:

• "The Role of Unstructured Data in Cyber Threat Intelligence," by Jason Trost, Anomali
• "Turning Unstructured Data into Cyber Threat Intelligence," by Giorgio Mosca, IEEE Xplore

Passive DNS monitoring involves collecting data about DNS queries and responses without actively querying DNS servers, thereby not altering or interfering with DNS traffic. This technique allows analysts to track changes in DNS records and observe patterns that may indicate malicious activity. In the scenario described, Enrique is employing passive DNS monitoring by using a recursive DNS server to log the responses received from name servers, storing these logs in a central database for analysis. This approach is effective for identifying malicious domains, mapping malware campaigns, and understanding threat actors' infrastructure without alerting them to the fact that they are being monitored. This method is distinct from active techniques such as DNS interrogation or zone transfers, which involve sending queries to DNS servers, and dynamic DNS, which refers to the automatic updating of DNS records.References:

• SANS Institute InfoSec Reading Room, "Using Passive DNS to Enhance Cyber Threat Intelligence"
• "Passive DNS Replication," by Florian Weimer, FIRST Conference Presentation

Game theory is a mathematical framework designed for understanding strategic situations where individuals' or groups' outcomes depend on their choices and the choices of others. In the context of threat intelligence analysis, game theory can be used as a de-biasing strategy to help understand and predict the actions of adversaries and defenders. By considering the various strategies and potential outcomes in a 'game' where each player's payoff is affected by the actions of others, analysts can overcome their biases and evaluate hypotheses more objectively. This approach is particularly useful in scenarios involving multiple actors with different goals and incomplete information.References:

• "Game Theory and Its Applications in Cybersecurity" in the International Journal of Computer Science and Information Security
• "Applying Game Theory to Cybersecurity" by the SANS Institute