312-85 Questions and Answers

ABC cyber-security company, which has implemented automation for tasks such as data enrichment and indicator aggregation and has joined various communities to increase knowledge about emerging threats, is demonstrating characteristics of a Level 3 maturity in the threat intelligence maturity model. At this level, organizations have a formal Cyber Threat Intelligence (CTI) program in place, with processes and tools implemented to collect, analyze, and integrate threat intelligence into their security operations. Although they may still be reactive in detecting and preventing threats, the existence of structured CTI capabilities indicates a more developed stage of threat intelligence maturity.References:

• "Building a Threat Intelligence Program," by Recorded Future
• "The Threat Intelligence Handbook," by Chris Pace, Cybersecurity Evangelist at Recorded Future

Internal intelligence feeds are derived from data and information collected within an organization's own networks and systems. Jian's activities, such as real-time assessment of system activities and acquiring feeds from honeynets, P2P monitoring, infrastructure, and application logs, fall under the collection of internal intelligence feeds. These feeds are crucial for identifying potential threats and vulnerabilities within the organization and form a fundamental part of a comprehensive threat intelligence program. They contrast with external intelligence feeds, which are sourced from outside the organization and include information on broader cyber threats, trends, and TTPs of threat actors.References:

• "Building an Intelligence-Led Security Program" by Allan Liska
• "Threat Intelligence: Collecting, Analysing, Evaluating" by M-K. Lee, L. Healey, and P. A. Porras

Karry's method of collecting data, which involves no active engagement with participants and is purely based on analysis and observation of activities within the organization, is known as passive data collection. This method is characterized by the non-intrusive monitoring of data and events, allowing analysts to gather intelligence without alerting potential adversaries or disrupting ongoing processes. Passive data collection is essential for maintaining operational security and obtaining an unaltered view of system and network activities.References:

• "Passive Data Collection in Cybersecurity," by Cybersecurity Guide
• "Understanding Passive and Active Data Collection for Cyber Threat Intelligence," by ThreatConnect

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

In the Traffic Light Protocol (TLP), the color amber signifies that the information should be limited to those who have a need-to-know within the specified community or organization, and not further disseminated without permission. TLP Red indicates information that should not be disclosed outside of the originating organization. TLP Green indicates information that is limited to the community but can be disseminated within the community without restriction. TLP White, or TLP Clear, indicates information that can be shared freely with no restrictions. Therefore, for information meant to be shared within a particular community with some restrictions on further dissemination, TLP Amber is the appropriate designation.References:

• FIRST (Forum of Incident Response and Security Teams) Traffic Light Protocol (TLP) Guidelines
• CISA (Cybersecurity and Infrastructure Security Agency) TLP Guidelines

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

The information shared by Alice, which was highly technical and included details such as threat actor tactics, techniques, and procedures (TTPs), malware campaigns, and tools used by threat actors, aligns with the definition of tactical threat intelligence. This type of intelligence focuses on the immediate, technical indicators of threats and is used bysecurity operation managers and network operations center (NOC) staff to protect organizational resources. Tactical threat intelligence is crucial for configuring security solutions and adjusting defense mechanisms to counteract known threats effectively.References:

• "Tactical Cyber Intelligence," Cyber Threat Intelligence Network, Inc.
• "Cyber Threat Intelligence for Front Line Defenders: A Practical Guide," by James Dietle

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.References:

• "Intelligence Analysis for Problem Solvers" by John E. McLaughlin
• "The Cyber Intelligence Tradecraft Project: The State of Cyber Intelligence Practices in the United States (Unclassified Summary)" by the Carnegie Mellon University's Software Engineering Institute

The phase described where John, after gaining initial access, is attempting to obtain administrative credentials to further access systems within the network, is known as the 'Expansion' phase of an Advanced Persistent Threat (APT) lifecycle. This phase involves the attacker expanding their foothold within the target's environment, often by escalating privileges, compromising additional systems, and moving laterally through the network. The goal is to increase control over the network and maintain persistence for ongoing access. This phase follows the initial intrusion and sets the stage for establishing long-term presence and eventual data exfiltration or other malicious objectives.References:

• MITRE ATT&CK Framework, specifically the tactics related to Credential Access and Lateral Movement
• "APT Lifecycle: Detecting the Undetected," a whitepaper by CyberArk

In the trust model described, where trust between two organizations depends on the degree and quality of evidence provided by the first organization, the model in use is 'Validated Trust.' This model relies on the validation of evidence or credentials presented by one party to another to establish trust. The validation process assesses the credibility, reliability, and relevance of the information shared, forming the basis of the trust relationship between the sharing partners. This approach is common in threat intelligence sharing where the accuracy and reliability of shared information are critical.References:

• "Building a Cybersecurity Culture," ISACA
• "Trust Models in Information Security," Journal of Internet Services and Applications