How to Start Stenography Projects Using OMNeT++

To start a Steganography project in OMNeT++, we need to replicate the scenarios in which data is out of sights in network communication. In network traffic, the target is learning the steganographic methods, its applications, and their detection. Steganography is designed for covert interaction, and to know it can also support within enhancing countermeasures versus abuse .If you have any query you can share all your project details to us .

Given below is a detailed instruction to get started:

Steps to Start Steganography Projects in OMNeT++

Step 1: Understand Steganography in Networks

Key Concepts:

  • Steganography: The training of hiding information in other data such as embedding a secret message within a packet header or payload.
  • Cover Medium: The cover medium in which the secret data is hidden like packet payload, image files, or traffic patterns.
  • Steganalysis: Mechanisms to identify and examine the hidden messages.

Applications:

  • Data watermarking.
  • Malware command-and-control interaction.
  • Covert communication for secure messaging.

Step 2: Define the Project Scope

Choose a certain project use case or issues like:

  • Implementation of Steganographic Techniques: To insert data within network packets.
  • Steganalysis: To identify and examine the hidden data.
  • Performance Impact: Learn how steganography impact the network performance.
  • Secure Communication: To utilize the steganography for covert messaging.

Example Problem Statement:

  • “Implement and analyze a packet-based steganographic technique to embed hidden messages in IoT device communication.”

Step 3: Prepare the OMNeT++ Environment

  1. Install OMNeT++:
    • We should download and install the new version of OMNeT++ on the system.
  2. Install INET Framework:
    • To simulate network with packet generation and traffic handling to utilize the INET framework.
  3. Optional Tools:
    • Python or MATLAB: For embedding and obtaining the steganographic information with the help of external tools such as python or Matlab.
    • Wireshark: It is used for packet analysis and validation.

Step 4: Develop the Network Model

Define Topology:

  • Sender Nodes:
    • Devices to insert hidden data within traffic.
  • Receiver Nodes:
    • Devices to obtain hidden messages.
  • Intermediate Nodes:
    • Intermediate nodes like routers or gateways to send the steganographic traffic.

Traffic Models:

  • Make typical traffic patterns including legitimate payloads.
  • Incorporate steganographic traffic using embedding hidden messages.

Step 5: Implement Steganography

Choose a Steganographic Technique:

  1. Packet Payload:
    • In the network packets payload, implant the secret messages.
  2. Packet Headers:
    • It hides data in unused fields of protocol headers such as IP or TCP headers.
  3. Traffic Patterns:
    • Make use of timing or volume patterns to encrypt data.

Embed and Extract Data:

  • Embedding Logic:
    • Prolong the OMNeT++ modules to change packets containing hidden information during transmission.
  • Extraction Logic:
    • Execute the logic at the receiver, obtaining and rebuilding hidden data.

Simulate Variants:

  • Insert various kinds of data such as text, keys, or commands.
  • Focus on detection thresholds to utilize the diverse levels of embedding.

Step 6: Configure the Simulation

Edit the omnetpp.ini File:

  • Network Settings:
    • Specify sender and receiver nodes, interaction links, and traffic patterns.
  • Steganography Settings:
    • Configure the metrics for embedding size, frequency, and location.
  • Metrics:
    • Measure the performance indicators such as latency, packet overhead, detection rates, and throughput.

Example Configuration:

network = StegNetwork

sim-time-limit = 100s

*.sender.enableSteganography = true

*.receiver.extractHiddenData = true

*.gateway.trafficMonitoring = true

*.stegoParameters.embeddingRatio = 0.1

Step 7: Run Simulation Scenarios

Example Scenarios:

  1. Covert Communication:
    • Make use of packet payloads to mimic hidden message switch among the nodes.
  2. Steganalysis:
    • Observe the traffic on intermediate nodes, identifying the anomalies that are triggered by hidden data.
  3. Performance Impact:
    • Estimate the impact of steganography on metrics like latency and throughput.

Step 8: Analyze Results

Key Metrics:

  • Detection Rate: How frequently steganalysis detects the hidden messages correctly.
  • False Positive Rate: Legitimate traffic drooped like steganographic.
  • Latency: More delay launched by embedding or extraction.
  • Throughput: Influence over complete data transfer rate.

Analysis Tools:

  1. Wireshark:
    • Analyse the packet payloads and headers for hidden data using wireshark tool.
  2. Python/Custom Scripts:
    • Examine the traffic patterns for anomalies to utilize python or custom scripts.

Step 9: Enhance with Advanced Features

  1. Machine Learning for Steganalysis:
    • Program ML models on typical vs. steganographic traffic identifying the hidden models.
  2. Dynamic Steganography:
    • Execute the adaptive mechanisms, depends on the network conditions which modify the embedding strategies.
  3. Blockchain Integration:
    • For secure logging or verification, we need to utilise blockchain confirming steganographic data.

Step 10: Document and Refine

  • Document Implementation:
    • It offers comprehensive insights of the embedding/extraction logic and network topology.
  • Analyze and Iterate:
    • Improve embedding or detection methods depend on the simulation findings.
  • Visualize Results:
    • Make graphs to indicate the detection rates, performance impact, or traffic patterns using visualization tools.

Example Use Case: Packet Payload Steganography in IoT

  1. Scenario:
    • IoT devices insert secret commands within packet payloads that are transmitted to a central controller.
  2. Objective:
    • Make sure that secret communication devoid of impacting the performance of network.
  3. Evaluation:
    • Estimate the metrics such as latency, detection rates, and the robustness of embedded data.

With the help of OMNeT++ tool, we executed the comprehensive simulation steps for replicating and analysing Steganography projects, with the capacity to provide further guidance regarding implementing specific steganographic techniques, detection algorithms, or advanced analysis tools in OMNeT++ if required.