212-82 Exam Dumps : Certified Cybersecurity Technician (CCT)

 Risk and impact analysis is the threat-modeling step in which Martin evaluated the severity level of the threat in the above scenario. Threat modeling is a process that involves identifying, analyzing, and mitigating threats and risks to a system or network. Threat modeling can be used to improve the security and resilience of a system or network by applying various methods or techniques, such as STRIDE, DREAD, PASTA, etc. Threat modeling consists of various steps or phases that perform different tasks or roles. Risk and impact analysis is a threat-modeling step that involves assessing the likelihood and consequences of threats and risks to a system or network . Risk and impact analysis can be used to evaluate the severity level of threats and risks and prioritize them for mitigation . In the scenario, Martin received breaching alerts for an application. He identified that a vulnerability in the application allowed attackers to enter malicious input. Martin evaluated the threat severity and extent of damage that could be caused by this vulnerability. He then escalated the issue to the security management team to determine appropriate mitigation strategies. This means that he performed risk and impact analysis for this purpose. Identify vulnerabilities is a threat-modeling step that involves finding and documenting the weaknesses or flaws in a system or network that can be exploited by threats or risks . Application overview is a threat-modeling step that involves defining and understanding the scope, architecture, components, and functionality of a system or network .Decompose the application is a threat-modeling step that involves breaking down a system or network into smaller and simpler elements, such as data flows, processes, assets, etc.

 A data-driven hunting method is a type of threat hunting method that employs existing data collected from various sources, such as DNS and proxy logs, to generate and test hypotheses about potential threats. This method relies on data analysis and machine learning techniques to identify patterns and anomalies that indicate malicious activity. A data-driven hunting method can help discover unknown or emerging threats that may evade traditional detection methods. An entity-driven hunting method is a type of threat hunting method that focuses on specific entities, such as users, devices, or domains, that are suspected or known to be involved in malicious activity. A TTP-driven hunting method is a type of threat hunting method that leverages threat intelligence and knowledge of adversary tactics, techniques, and procedures (TTPs) to formulate and test hypotheses about potential threats. A hybrid hunting method is a type of threat hunting method that combines different approaches, such as data-driven, entity-driven, and TTP-driven methods, to achieve more comprehensive and effective results. 

In the context of developing a cryptographic protocol resilient against quantum computing attacks, SHA-3 is the most appropriate choice. Here’s why:

Quantum Computing Threats: Quantum computers can potentially break current cryptographic methods like RSA and ECC due to Shor's algorithm. Traditional hash functions like SHA-256 might not offer sufficient security in a post-quantum world.

SHA-3 Overview: SHA-3, part of the Secure Hash Algorithm family, was designed with quantum resistance in mind. It was selected through an open competition by NIST, ensuring it incorporates advanced cryptographic techniques.

Resilience: SHA-3’s design is fundamentally different from SHA-2, providing enhanced security properties, including resistance to various attack vectors that might be feasible with quantum computing advancements.

References:

NIST SHA-3 Standard:NIST FIPS PUB 202

Research on quantum-resistant cryptography:IEEE Xplore