CompTIA Updated CS0-003 Exam Questions and Answers by julian

Mean time to contain is the metric that the cybersecurity team lead should include in the weekly executive briefs, as it measures how long it takes to stop the spread of malware that enters the network. Mean time to contain is the average time it takes to isolate and neutralize an incident or a threat, such as malware, from the time it is detected. Mean time to contain is an important metric for evaluating the effectiveness and efficiency of the incident response process, as well as the potential impact and damage of the incident or threat. A lower mean time to contain indicates a faster and more successful response, which can reduce the risk and cost of the incident or threat. Mean time to contain can also be compared with other metrics, such as mean time to detect or mean time to remediate, to identify gaps or areas for improvement in the incident response process. 

The signature of the malware is a unique identifier that can be used to compare it with known malware samples and their behaviors. Open-source threat intelligence sources provide information on various types of malware, their indicators of compromise, and their mitigation strategies. By cross-referencing the signature with these sources, the analyst can determine the type of malware and its telemetry. The other options are not relevant for this purpose: configuring the EDR to perform a full scan may not provide additional information on the malware type; transferring the malware to a sandbox environment may expose the analyst to further risks; logging in to the affected systems and running netstat may not reveal the malware activity.

Fuzzing is a testing technique where invalid or random data is inputted into a system to find vulnerabilities, crashes, or unexpected behaviors. It’s commonly used in software security to identify flaws that could lead to security breaches. According to CompTIA’s CySA+ curriculum, fuzzing is a dynamic testing method for exposing application weaknesses. Options like static testing (B) involve analyzing code without execution, while reverse engineering (A) and debugging (D) involve different methodologies for understanding or fixing code, not intentionally stressing it.