ECCouncil 312-85 Practice Test - Questions Answers

Tracy, as a Chief Information Security Officer (CISO), requires intelligence that aids in understanding broader business and cybersecurity trends, making informed decisions regarding new technologies, security budgets, process improvements, and staffing. This need aligns with the role of a strategic user of threat intelligence. Strategic users leverage intelligence to guide long-term planning and decision-making, focusing on minimizing business risks and safeguarding against emerging threats to new technology and business initiatives. This type of intelligence is less about the technical specifics of individual threats and more about understanding the overall threat landscape, regulatory environment, and industry trends to inform high-level strategy and policy.

Reference:

'The Role of Strategic Intelligence in Cybersecurity,' Journal of Cybersecurity Education, Research and Practice

'Cyber Threat Intelligence and the Lessons from Law Enforcement,' by Robert M. Lee and David Bianco, SANS Institute Reading Room

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.

Reference:

'Cyber Threat Intelligence: Sources and Methods,' by Max Kilger, Ph.D., SANS Institute Reading Room

'Open Source Intelligence (OSINT): An Introduction to the Basic Concepts and the Potential Benefits for Information Security,' by Kevin Cardwell, IEEE Xplore

The network administrator collected log files generated by a traffic monitoring system, which falls under the category of low-level data. This type of data might not appear useful at first glance but can reveal significant insights about network activity and potential threats upon thorough analysis. Low-level data includes raw logs, packet captures, and other granular details that, when analyzed properly, can help detect anomalous behaviors or indicators of compromise within the network. This type of information is essential for detection and response efforts, allowing security teams to identify and mitigate threats in real-time.

Reference:

'Network Forensics: Tracking Hackers through Cyberspace,' by Sherri Davidoff and Jonathan Ham, Prentice Hall

'Real-Time Detection of Anomalous Activity in Dynamic, Heterogeneous Information Systems,' IEEE Transactions on Information Forensics and Security

Daniel's activities align with those typically associated with organized hackers. Organized hackers or cybercriminals work in groups with the primary goal of financial gain through illegal activities such as stealing and selling data. These groups often target large amounts of data, including personal and financial information, which they can monetize by selling on the black market or dark web. Unlike industrial spies who focus on corporate espionage or state-sponsored hackers who are backed by nation-states for political or military objectives, organized hackers are motivated by profit. Insider threats, on the other hand, come from within the organization and might not always be motivated by financial gain. The actions described in the scenario---targeting personal and financial information for sale---best fit the modus operandi of organized cybercriminal groups.

Reference:

ENISA (European Union Agency for Cybersecurity) Threat Landscape Report

Verizon Data Breach Investigations Report

Analysis of Competing Hypotheses (ACH) is an analytic process designed to help an analyst or a team of analysts evaluate multiple competing hypotheses on an issue fairly and objectively. ACH assists in identifying and analyzing the evidence for and against each hypothesis, ultimately aiding in determining the most likely explanation. In the scenario where a team of threat intelligence analysts has various theories on a particular malware, ACH would be the most appropriate method to assess these competing theories systematically. ACH involves listing all possible hypotheses, collecting data and evidence, and assessing the evidence's consistency with each hypothesis. This process helps in minimizing cognitive biases and making a more informed decision on the most consistent theory.

Reference:

Richards J. Heuer Jr., 'Psychology of Intelligence Analysis,' Central Intelligence Agency

'A Tradecraft Primer: Structured Analytic Techniques for Improving Intelligence Analysis,' Central Intelligence Agency

Normalization in the context of data analysis refers to the process of organizing data to reduce redundancy and improve efficiency in storing and sharing. By filtering, tagging, and queuing, Miley is effectively normalizing the data---converting it from various unstructured formats into a structured, more accessible format. This makes the data easier to analyze, store, and share. Normalization is crucial in cybersecurity and threat intelligence to manage the vast amounts of data collected and ensure that only relevant data is retained and analyzed. This technique contrasts with sandboxing, which is used for isolating and analyzing suspicious code; data visualization, which involves representing data graphically; and convenience sampling, which is a method of sampling where samples are taken from a group that is conveniently accessible.

Reference:

'The Application of Data Normalization to Database Security,' International Journal of Computer Science Issues

SANS Institute Reading Room, 'Data Normalization Considerations in Cyber Threat Intelligence'

Red Teams are tasked with emulating potential adversaries to test and improve the security posture of an organization. They require intelligence on the latest vulnerabilities, threat actors, and their TTPs to simulate realistic attack scenarios and identify potential weaknesses in the organization's defenses. This information helps Red Teams in crafting their attack strategies to be as realistic and relevant as possible, thereby providing valuable insights into how actual attackers might exploit the organization's systems. This need contrasts with the requirements of other teams or roles within an organization, such as strategic decision-makers, who might be more interested in intelligence related to strategic risks or Blue Teams, which focus on defending against and responding to attacks.

Reference:

Red Team Field Manual (RTFM)

MITRE ATT&CK Framework for understanding threat actor TTPs

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.

Reference:

'Intelligence Analysis: A Target-Centric Approach,' by Robert M. Clark

'Structured Analytic Techniques for Intelligence Analysis,' by Richards J. Heuer Jr. and Randolph H. Pherson

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.

Reference:

SANS Institute InfoSec Reading Room, 'Using Passive DNS to Enhance Cyber Threat Intelligence'

'Passive DNS Replication,' by Florian Weimer, FIRST Conference Presentation