Digital Forensics projects examples using omnet++

Digital forensics concentrates on the examination and exploration of digital data to uncover evidence of cybercrimes or security breaches. While the OMNeT++ is first and foremost of a network simulation tool that can still emulate the scenarios that associated to digital forensics that concentrates on the capture, analysis, and reconstruction of network activities.  The below are the some digital forensics project examples that can be implemented using OMNeT++:

1. Network Traffic Capture and Analysis for Forensic Investigation

• Objective: Mimic the capture and evaluate of network traffic to detect potential security breaches or malicious activities.
• Implementation: Develop a network in which traffic is continuously monitored and logged by a forensic module. Apply traffic analysis technique to identify anomalies, like unusual patterns, unauthorized access, or data exfiltration.
• Extension: To emulate a security breach, capture the pertinent network traffic, and reconstruct the sequence of events prominent up to the breach. Measure the captured data to detect the attacker and the method used.

2. Simulating the Forensic Analysis of a DDoS Attack

• Objective: To emulate a Distributed Denial of Service (DDoS) attack and perform forensic analysis to trace the source of the attack and familarize its impact.
• Implementation: Generate a network topology with a target server and multiple attacking nodes. Internment all network traffic during the DDoS attack and use forensic tools to examine the patterns, classify the attack’s origin, and regulate the attack strategy.
• Extension: Improve methods to relate the traffic from various sources to detect coordinated attacks, and use this information to construct a timeline of the attack.

3. Packet Inspection and Reconstruction for Digital Forensics

• Objective: Mimic packet inspection and reconstruction methods to evaluate and reconstruct malicious activities on the network.
• Implementation: Execute deep packet inspection (DPI) on a network node to capture and evaluate packets. Use forensic approaches to reconstruct sessions, extract payloads, and identify suspicious activities such as data theft or malware communication.
• Extension: Improve a tool that reconstructs fragmented packets and assess their content to detect the hidden communication channels or covert data exfiltration.

4. Simulation of Digital Evidence Collection in Wireless Networks

• Objective: To emulate the process of collecting digital evidence in a wireless network environment for use in forensic investigations.
• Implementation: Generate a wireless network with multiple devices. Execute modules to capture wireless traffic that has an encrypted communication. Measure the captured data to extract metadata, device identifiers, and communication patterns.
• Extension: To emulate the scenarios in which attackers attempt to hide their activities using the approaches such as MAC address spoofing, and improve forensic technique to detect and trace these activities.

5. Forensic Analysis of Data Exfiltration

• Objective: To execute a data exfiltration scenario and conduct forensic analysis to detect how sensitive data was transmitted out of the network.
• Implementation: Configure a network in which sensitive data is stored on a server. To emulate an attacker who gains unauthorized access and exfiltrates data to an external location then Capture network traffic during the exfiltration and measured it to identify the method used like encrypted tunnels, steganography.
• Extension: To design forensic approaches to identify and assess covert channels used for data exfiltration, and generate the reports detailing the exfiltration process.

6. Reconstructing Network Attacks from Logs and Traffic Captures

• Objective: Mimic a network attacks and then use forensic approaches to reconstruct the attack from network logs and traffic captures.
• Implementation: Execute a network with logging mechanisms at different nodes like firewalls, routers and to emulate the several kinds of attacks like SQL injection, cross-site scripting and measure the logs to reconstruct the attack timeline, identify compromised nodes, and trace the attacker’s origin.
• Extension: Improve automated tools to relate log entries with network traffic data, helping forensic investigators rapidly detect and measure the security incidents.

7. Forensic Analysis of Ransomware Activity in a Network

• Objective: To emulate a ransomware attack within a network and perform forensic analysis to trace the origin and influence of the attack.
• Implementation: Generate a network with multiple endpoints and emulate a ransomware attack that spreads across the network. Capture and measure network traffic to detect the initial infection point, the propagation method, and the command-and-control (C2) communication.
• Extension: To design methods to recover encrypted files by identifying and evaluating an encryption keys used by the ransomware, if possible, or monitor the ransomware’s distribution channel.

8. Simulating the Forensic Analysis of Insider Threats

• Objective: To emulate insider threats within a network and perform forensic analysis to identify and measure malicious activities carried out by trusted users.
• Implementation: Configure a network with users who have changing levels of access to sensitive data and emulate an insider who abuses their rights to steal or manipulate data. Capture logs and network traffic to find the insider’s actions, the methods used, and the data compromised.
• Extension: To design the tools that can identify the suspicious insider activities in real-time and produce alerts for auxiliary forensic investigation.

9. Timeline Reconstruction from Network Forensic Data

• Objective: Simulate a network environment where multiple security incidents occur, and use forensic analysis to reconstruct a detailed timeline of events.
• Implementation: To mimic numerous security incidents like unauthorized access, malware infection, and data theft, occurring at various times. Gather and measure network traffic, logs, and system events to reconstruct the sequence of activities and relate them with each incident.
• Extension: Generate visualization tools that denotes to the reconstructed timeline graphically, helping investigators to familiarize the flow of events and test the key moments in the attack sequence.

10. Forensic Analysis of IoT Device Compromise

• Objective: To emulate the compromise of IoT devices within a network and perform forensic analysis to regulate how the devices were breached and what data was retrieved or manipulated.
• Implementation: Configure a network with multiple IoT devices like cameras, sensors. Emulate an attack that compromises one or more devices and captures the network traffic during the attack. Evaluate the captured data to classify the attack vector and the effect on the devices, and any data exfiltrated from the network.
• Extension: Design the forensic approaches to classify and measure firmware vulnerabilities, and propose security measures to mitigate future compromises.

11. Forensic Recovery of Deleted or Obfuscated Network Data

• Objective: To emulate scenarios in which the attackers attempt to delete or obfuscate network data to cover their tracks, and perform forensic analysis to recover and measure this data.
• Implementation: To emulate a network in which an attacker deletes logs, encrypts traffic, or uses steganography to hide their activities and execute forensic approaches to recover deleted data, decrypt traffic, and analyse hidden messages or files.
• Extension: To design automated tools that support in the recovery of complicated data and the reconstruction of the attack scenario from the recovered information.

12. Simulation of Anti-Forensic Techniques and Their Detection

• Objective: Mimic anti-forensic approaches used by attackers to avoid detection and operates in forensic analysis to identify and counter these techniques.
• Implementation: To emulate a network in which the attackers use algorithm such as log manipulation, data wiping, or steganography to cover their tracks. Capture network traffic and logs, and then evaluate them to classify signs of anti-forensic activity and reconstruct the hidden or destroyed evidence.
• Extension: To design the detection tools that can automatically test anti-forensic approaches and preserve the integrity of digital evidence.

Finally, here we learned some of the examples for Digital forensics that performs in OMNeT++ simulation.  Also we offer further elaborated detail regarding the Digital forensics. For optimal project execution strategies related to Digital Forensics utilizing the OMNeT++ tool, we encourage you to reach out with any research inquiries you may have; we are here to assist you further. Our developers specialize in the capture, analysis, and reconstruction of network activities relevant to Digital Forensics projects, ensuring that you achieve high project performance with their expertise.