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| Section | Weight | Objectives |
|---|---|---|
| Malware Analysis | 15% | - Malware family and campaign identification - Reverse engineering principles - Static and dynamic malware analysis - Malware classification and behavior analysis |
| Incident Response Techniques | 30% | - Attack vector analysis and mitigation recommendations - Post-incident analysis and improvement actions - Threat intelligence interpretation: IOCs, IOAs, actor profiling - Correlating host and network activity data - Response to zero-day exploits and vulnerabilities - Interpreting alerts from SIEM, IDS/IPS, syslog - Cisco security solutions for detection and prevention |
| Fundamentals | 20% | - Evidence collection in virtualized environments - Network infrastructure device forensics - YARA rules for malware identification and classification - Root cause analysis reporting components - Encoding and obfuscation techniques - Antiforensic tactics, techniques, and procedures |
| Forensics Processes | 15% | - Legal and compliance considerations - Evidence handling and chain of custody - Antiforensic techniques: debugging, geolocation, obfuscation - Data acquisition: memory, disk, network |
| Forensics Techniques | 20% | - Host-based evidence location and collection - Forensic tools: Volatility, Sysinternals, SIFT, TCPdump - Script analysis (Python, PowerShell, Bash) for log processing - Identifying Indicators of Compromise (IOC) from tools output - MITRE ATT&CK framework for fileless malware analysis |
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NEW QUESTION # 43
Refer to the exhibit.
A web hosting company analyst is analyzing the latest traffic because there was a 20% spike in server CPU usage recently. After correlating the logs, the problem seems to be related to the bad actor activities. Which attack vector is used and what mitigation can the analyst suggest?
Answer: A
Explanation:
Comprehensive and Detailed Explanation:
The log entries show repeated SSH login attempts for various invalid usernames (e.g., admin, phoenix, rainbow, test, user, etc.) from different source ports. These are clear signs of a brute-force attack-an automated process trying multiple usernames and passwords in hopes of gaining access.
Mitigating such attacks includes:
Implementing account lockout policies (e.g., locking an account after several failed login attempts).
Enabling Multi-Factor Authentication (MFA) to ensure that password guessing alone is insufficient for account access.
Therefore, the correct answer is:
D). Brute-force attack; implement account lockout policies and roll out MFA.
NEW QUESTION # 44 
Refer to the exhibit. A network administrator creates an Apache log parser by using Python. What needs to be added in the box where the code is missing to accomplish the requirement?
Answer: C
Explanation:
The goal of the given Python code is to parse an Apache access log and extract IP addresses using regular expressions (regex). In this context, the most appropriate regex pattern to extract IPv4 addresses from log data is:
* r'\d{1,3}.\d{1,3}.\d{1,3}.\d{1,3}'
This pattern matches typical IPv4 addresses, where each octet consists of 1 to 3 digits separated by periods.
For example, it matches addresses like 192.168.1.1 or 10.0.0.123. The pattern uses:
* \d{1,3} to capture between 1 and 3 digits,
* \. to match the dot (escaped since . is a special character in regex),
* repeated 4 times with proper separation to form the full IPv4 structure.
Options A, B, and C either include incorrect syntax, improper escape sequences, or do not represent a valid IP address pattern.
This type of log analysis and pattern extraction is described in the Cisco CyberOps Associate curriculum under basic scripting and automation techniques used in log and artifact analysis.
Reference: CyberOps Technologies (CBRFIR) 300-215 study guide, Section: "Basic Python Scripting for Security Analysts" and "Log Analysis and Data Extraction using Regex."
NEW QUESTION # 45
Which tool should an investigator use to extract information about running processes from RAM?
Answer: A
Explanation:
Volatility is purpose-built for extracting and interpreting artifacts from volatile-memory images. Its Windows pslist, psscan, and pstree capabilities can enumerate active processes, scan for terminated or unlinked process structures, and reconstruct parent-child relationships present when memory was acquired. Sleuth Kit and Autopsy primarily analyze file systems and disk images. The dd utility can acquire raw data, including a device image when used appropriately, but it does not itself interpret operating-system process structures.
SIFT is a broader forensic workstation that can contain numerous tools; it is not the most precise answer when Volatility is offered directly. Cisco includes memory-forensics tools within Fundamentals objective 1.6.d and explicitly lists Volatility among the tools whose purpose and functionality candidates must recognize under Forensics Techniques objective 2.6. The official Volatility documentation confirms that pslist lists processes present in a Windows memory image. Volatility pslist documentation
NEW QUESTION # 46
Refer to the exhibit.
What should be determined from this Apache log?
Answer: A
Explanation:
The error logs indicate multiple PKCS12 and ASN.1 decoding errors, such as:
PKCS12 routines:PKCS12_parse:mac verify failure
rsa routines:old_rsa_priv_decode:RSA lib
PKCS12 routines:PKCS12_key_gen_uni:malloc
These specific errors most commonly occur when:
The private key does not correspond to the certificate being used.
There is a mismatch between the public and private key pair required for SSL handshakes.
This is a well-documented condition in Apache SSL configuration issues and explicitly covered under TLS
/SSL troubleshooting sections in cybersecurity operations contexts. The Cisco CyberOps guide also notes that SSL errors with key verification usually result from " improper key/certificate pairing " rather than file corruption or missing modules.
Thus, the correct answer is:
B). The private key does not match with the SSL certificate.
NEW QUESTION # 47
Refer to the exhibit.
Which type of code is being used?
Answer: A
Explanation:
The code in the exhibit is written in Python. Here's how we can confirm:
The function definition uses Python syntax: def function_name(args):
It uses the b64encode and decode functions - typical of Python's base64 module.
Data structures such as dictionaries are used with curly braces (e.g., form_data = {entry1: enc1, ...}).
The conditional syntax uses "if r.status_code == 200:" which is Pythonic.
The request object "r = post(...)" and use of headers show standard use of the Python requests library.
This type of script is typical in exfiltration scenarios where encoded information is sent via a web form (in this case Google Forms), bypassing detection systems.
Reference: CyberOps Technologies (CBRFIR) 300-215 study guide, Chapter on "Working with Malware and Exploit Scripts," which includes analysis of obfuscated and encoded scripts written in Python used for data exfiltration or C2 communication.
NEW QUESTION # 48
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