Free Practice Questions for Cisco 200-201 Exam

Delivery is the fourth phase of the cyber kill chain, which is a model to describe the stages of a cyberattack. Delivery refers to the transmission of the weaponized payload to the target system, such as via email attachments, web links, USB drives, or network connections. Delivery does not necessarily imply successful installation or execution of the payload, which are subsequent phases of the kill chain. References := Understanding Cisco Cybersecurity Operations Fundamentals (CBROPS) - Cisco, page 31.

The event where a breached workstation is trying to connect to a known malicious domain suggests that the attacker is moving towards their end goals, which typically involves actions on objectives.

In the Cyber Kill Chain framework, "Action on objectives" refers to the steps taken by an attacker to achieve their intended outcomes, such as data exfiltration, destruction, or ransom demands.

This phase involves the attacker executing their final mission within the target environment, leveraging access gained in earlier stages of the attack.

References

Lockheed Martin Cyber Kill Chain

Understanding the Stages of Cyber Attacks

Incident Response and the Cyber Kill Chain

A command and control server (C2 or C&C) is a server that is used by attackers to communicate with and control compromised systems, such as bots, zombies, or backdoors. The C2 server can send instructions to the compromised systems, such as executing commands, downloading files, uploading data, or launching attacks. The C2 server can alsoreceive information from the compromised systems, such as system information, keystrokes, screenshots, or credentials. References:

Understanding Cisco Cybersecurity Operations Fundamentals (CBROPS) v1.0, Module 3: Network Intrusion Analysis, Lesson 3.4: Malware

Cisco Certified CyberOps Associate Overview, Exam Topics, 3.4 Compare and contrast types of malware

 Full packet capture requires the largest amount of storage space because it involves recording all packets that pass through a network, including all headers and payloads. This type of data collection is comprehensive and allows for detailed analysis, but due to the volume of data it encompasses, it demands significant storage capacity1.

References := The Cisco Secure Network Analytics Data Store Design Guide discusses the storage requirements for different types of network data collection, highlighting the substantial storage needs for full packet captures1.

The data shown in the exhibit is typical of what can be captured and displayed using tcpdump, a command-line packet analyzer that allows users to display TCP/IP and other packets being transmitted or received over a network.

During the examination phase of the forensic process, digital forensic investigators use various tools and techniques to extract and analyze information from the collected data. This phase involves detailed scrutiny of the data to uncover relevant evidence and is critical for the success of the forensic investigation.

A dictionary attack is a method used to break into a password-protected computer or server by systematically entering every word in a dictionary as a password. In the context of an authentication system that uses only 4-digit numeric passwords, a dictionary attack would involve trying all possible combinations of 4-digit numbers until the correct one is found.

 When a server is experiencing high CPU and memory load, the first step is to identify the processes that are consuming the most resources. The command “ps -ef” is used to display information about all the running processes, including their IDs, memory and CPU usage, and the commands that started them. This allows the engineer to pinpoint which processes are responsible for the high load and take appropriate action, such as terminating unnecessary processes or optimizing resource usage345. References: Various resources on server management and troubleshooting recommend using the “ps -ef” command as a starting point for investigating high resource usage on servers

 Sliding window anomaly detection is a technique used in cybersecurity to identify unusual patterns or behaviors that deviate from the norm. It involves analyzing segments of data over a period of time, referred to as a ‘window,’ and comparing them against typical patterns. Anomalies are detected when observed behaviors significantly differ from expected patterns, indicating potential security incidents or issues that require further investigation. References: An adaptive sliding window for anomaly detection of time series in wireless sensor networks