312-49v11 Exam Dumps - Computer Hacking Forensic Investigator (CHFIv11)

This question aligns with CHFI v11 objectives under Network and Web Attacks and Network Log Analysis . In digital forensics, network infrastructure logs are critical sources of evidence for detecting, analyzing, and reconstructing network-based attacks. CHFI v11 specifically emphasizes the forensic value of logs generated by network devices such as Cisco switches, VPN gateways, and DNS servers .

Cisco switch logs provide information about device connections, port activity, MAC address mappings, VLAN assignments, and potential unauthorized access within the internal network. VPN logs reveal details about remote connections, including authentication attempts, user identities, IP addresses, session durations, and encrypted tunnel activity—crucial for identifying compromised credentials or unauthorized remote access. DNS server logs record domain name queries and responses, which help investigators detect command-and-control communication, data exfiltration attempts, malware beaconing, and access to malicious domains.

Together, these logs allow investigators to correlate events across the network, trace attacker movement, identify affected systems, and establish timelines of security incidents. The other options are incorrect because browser history is host-based evidence, and these logs are highly relevant to forensic investigations. Therefore, consistent with CHFI v11 network forensics principles, these logs provide insights into network traffic, device connections, and security incidents.

Under the CHFI v11 Mobile and IoT Forensics domain, investigators are required to extract and analyze application-level artifacts from mobile devices to reconstruct user activity. Web browsers such as Google Chrome store valuable forensic data on Android devices, including browsing history, cookies, cached files, saved form data, session tokens, and timestamps , which can be critical in cybercrime investigations.

Magnet AXIOM is a comprehensive digital forensics platform explicitly supported and referenced in CHFI v11 for mobile device forensic analysis . It is capable of performing logical and file system extractions from Android devices and includes built-in parsers for Chrome artifacts . Magnet AXIOM can automatically locate Chrome databases (such as History, Cookies, and cache directories), decode SQLite databases, and present the extracted data in a forensically structured and timeline-based view. This makes it highly effective for correlating browser activity with other evidence.

The other tools listed are not suitable for this task. LOIC is a network stress-testing/DoS tool, Orbot Proxy is used to route traffic through the Tor network, and DroidSheep is a network sniffing tool for session hijacking. None of these tools are designed for forensic extraction or analysis of browser artifacts from Android devices.

Therefore, in alignment with CHFI v11 Mobile and IoT Forensics objectives , the correct and most suitable tool for extracting Chrome artifacts from an Android device is Magnet AXIOM , making Option D the correct answer.

Option A is the best answer because the scenario already suggests that the attacker likely gained access through a remote connection using compromised credentials . The most logical next forensic step is to examine server logs for unusual login patterns , including failed logons, successful remote logins at abnormal times, privileged account use, and suspicious authentication sources. CHFI v11 explicitly includes event logs , types of logon events , evaluating account management events , and SQL Server logs as relevant evidence sources in operating system and application forensics.

Although identifying the source IP may later become important, investigators usually first validate the unauthorized access path through log review and then correlate it with IP data, account activity, and timeline evidence. A complete system scan is too broad as the next step, and reviewing recently accessed files does not directly answer how the attacker entered the database environment.

Because CHFI emphasizes log analysis , authentication event review , and timeline reconstruction when investigating intrusions, the strongest next step is to evaluate the server logs for unusual login patterns that explain how the attacker gained access.

This scenario aligns with CHFI v11 objectives under Anti-Forensics Techniques , specifically techniques used by attackers to prevent investigators from accessing digital evidence. Data encryption is a well-known and widely used anti-forensic method where files are encrypted using strong cryptographic algorithms and protected with complex passwords. While encryption is a legitimate security control, adversaries often misuse it to deliberately obstruct forensic analysis and delay investigations.

CHFI v11 explains that encrypted files render data unreadable without the correct decryption key, making it extremely difficult for investigators to examine file contents within acceptable timeframes. This can significantly hinder evidence discovery, timeline reconstruction, and incident scoping. Investigators must then rely on password cracking, key recovery, memory forensics, or legal assistance to access the data—each of which introduces complexity, cost, and time delays.

Data manipulation involves altering or deleting evidence, data obfuscation focuses on making data confusing but still accessible, and data hiding conceals information in alternate locations. In contrast, the defining characteristic in this scenario is password-protected encrypted files , which directly corresponds to data encryption. Therefore, consistent with CHFI v11 classifications, data encryption is the correct anti-forensic technique being employed.

According to the CHFI v11 Computer Forensics Fundamentals and Investigation Process , one of the most critical steps in forming a specialized cybercrime investigation team is clearly assigning roles and responsibilities to team members . This step ensures that every aspect of the investigation is handled efficiently, lawfully, and without overlap or conflict.

CHFI v11 emphasizes that cybercrime investigations are multidisciplinary by nature and require role-based coordination . Typical roles include first responders, incident responders, forensic examiners, evidence handlers, photographers, documentation specialists, and legal advisors. Clearly defining these roles at the outset ensures proper evidence handling, adherence to legal procedures, and effective incident response . It also supports maintaining the chain of custody , minimizing contamination of evidence, and ensuring accountability throughout the investigation lifecycle.

While legal advice and external support are important, they are supplementary functions that support the investigation after the core team structure is established. Conducting digital forensics analysis is an operational activity that occurs later in the forensic process, not during team formation.

CHFI v11 explicitly highlights building the investigation team and assigning responsibilities as foundational steps before evidence collection and analysis begin. Without clearly defined roles, investigations risk procedural errors, legal challenges, and inefficiencies.

Therefore, the most crucial step in forming a specialized cybercrime investigation team—fully aligned with CHFI v11 objectives—is assigning roles to team members , making Option D the correct answer.

As per the CHFI v11 Cloud Forensics objectives , cloud-based identity and access management solutions that provide Single Sign-On (SSO) , Multi-Factor Authentication (MFA) , centralized authentication, and fine-grained authorization controls—managed entirely by a third-party provider —are classified as Identity-as-a-Service (IDaaS) .

IDaaS is a specialized cloud service model designed specifically for identity management , including authentication, authorization, user provisioning, role-based access control, and centralized logging of authentication events. In forensic investigations, IDaaS platforms are critical evidence sources because they generate detailed authentication logs , login timestamps, MFA challenges, IP addresses, device identifiers, and anomaly alerts. These logs allow investigators to correlate user identities with access patterns and trace unauthorized or malicious actions across multiple systems.

The CHFI v11 blueprint explicitly differentiates IDaaS from other cloud service models. IaaS focuses on infrastructure resources such as virtual machines and networks, not identity enforcement. PaaS is used for developing and deploying custom applications, which is not indicated here since the authentication is handled by a third party. DaaS delivers virtual desktops and does not inherently manage enterprise-wide authentication and authorization.

Therefore, based on the presence of third-party-managed SSO, MFA, centralized access control, and authentication log analysis, the correct answer—fully aligned with CHFI v11 documentation—is Identity-as-a-Service (IDaaS) .

This question maps directly to CHFI v11 objectives under Malware Forensics , specifically malware persistence mechanisms and behavior analysis . Persistent malware is designed to survive system reboots and removal attempts by embedding itself into startup locations, registry keys, scheduled tasks, services, boot sectors, or firmware. CHFI v11 emphasizes that identifying persistence mechanisms is a critical step in malware analysis and incident response.

From a forensic perspective, understanding how malware maintains persistence allows investigators to fully eradicate the threat and prevent reinfection. If persistence artifacts are not identified and removed, the malware can continuously reinstall itself, rendering cleanup efforts ineffective and allowing attackers to maintain long-term access. CHFI v11 highlights registry-based persistence, startup folders, services, cron jobs, launch agents, and boot-level persistence as common techniques that must be analyzed.

Additionally, identifying persistence helps investigators reconstruct the attack timeline, understand attacker intent, and determine the scope of compromise. The other options are not primary forensic objectives—system performance, malware geography, or network optimization are unrelated to persistence analysis. Therefore, in accordance with CHFI v11 malware forensics principles, identifying malware persistence is essential to prevent future infections and ensure the long-term security of the system.

Option B. RAID 3 is the correct answer because the question describes a RAID level that uses byte-level striping with a dedicated parity disk and requires at least three disks . That combination matches RAID 3. CHFI v11 explicitly includes RAID Storage System , RAID and Virtualization , and the broader understanding of storage structures under digital evidence fundamentals, so candidates are expected to recognize RAID types and how they affect evidence acquisition and recovery.

The key clues are the dedicated parity drive and the fact that the system can continue functioning after a single disk failure . RAID 3 is designed to provide fault tolerance for one failed drive by reconstructing data from the remaining striped disks and the parity information. This makes it different from RAID 0 , which has no redundancy, RAID 1 , which mirrors data rather than using dedicated parity, and RAID 10 , which combines striping and mirroring in a different way.

Because the scenario precisely describes byte-level striping plus one parity disk with single-disk fault tolerance, RAID 3 is the only answer that fully fits the described configuration.