How to Implement Phishing Defence in ns3

To implement the phishing defence in ns3 has need to emulate the network where the node can identifies and respond to phishing attempts and has concludes the numerous strategies like classifying the malicious URLs, measure the email content or implement the machine learning to verify and detect the any malicious behaviour. Here are the procedures on how to implement the phishing defence in ns3:

Step-by-Step Implementation:

Step 1: Set Up ns3 Environment

1. Install ns3: install the ns3 in the system.
2. Familiarize yourself with ns-3: Read through the ns3 tutorial to know the simple concepts and structure of ns3 simulations.

Step 2: Set Up Machine Learning Library

1. Install a Machine Learning Library: we must utilize Python libraries like scikit-learn for phishing detection. Guaranteed Python and scikit-learn are installed on your system.
2. Install the Library: Follow the installation instructions for scikit-learn. For instance:

bash

Copy code

pip install scikit-learn

Step 3: Define the Network Topology

1. Create a Simple Network: Describe a simple network topology using ns3 and encompasses creating nodes, setting up channels, and configuring IP addresses. We’ll use a simple topology for this example.

Step 4: Implement Phishing Defense

To implement phishing defense, we can use techniques like:

1. URL Analysis: Analyze URLs to detect malicious links.
2. Email Content Analysis: Analyze email content to detect phishing attempts.
3. Machine Learning: Use machine learning models to classify emails or URLs as phishing or legitimate.

Here’s an instance of how to execute a basic phishing defence mechanism using URL analysis and machine learning:

C++ Code for ns3 Simulation (main.cc)

#include “ns3/core-module.h”

#include “ns3/network-module.h”

#include “ns3/internet-module.h”

#include “ns3/point-to-point-module.h”

#include “ns3/applications-module.h”

#include <iostream>

#include <fstream>

using namespace ns3;

class PhishingDefenseApp : public Application

{

public:

PhishingDefenseApp() : m_packetsReceived(0) {}

virtual ~PhishingDefenseApp() {}

void Setup(Address address, uint16_t port)

{

m_peerAddress = address;

m_peerPort = port;

}

private:

virtual void StartApplication()

{

m_socket = Socket::CreateSocket(GetNode(), TypeId::LookupByName(“ns3::UdpSocketFactory”));

m_socket->Bind();

m_socket->Connect(InetSocketAddress(m_peerAddress, m_peerPort));

// Schedule the first packet transmission

Simulator::Schedule(Seconds(1.0), &PhishingDefenseApp::SendPacket, this);

// Set up the receive callback

m_socket->SetRecvCallback(MakeCallback(&PhishingDefenseApp::ReceivePacket, this));

}

virtual void StopApplication()

{

if (m_socket)

{

m_socket->Close();

m_socket = 0;

}

}

void SendPacket()

{

std::string message = “http://example.com”; // Example URL

Ptr<Packet> packet = Create<Packet>((uint8_t *)message.c_str(), message.size());

m_socket->Send(packet);

// Schedule the next packet transmission

Simulator::Schedule(Seconds(5.0), &PhishingDefenseApp::SendPacket, this);

}

void ReceivePacket(Ptr<Socket> socket)

{

Ptr<Packet> packet = socket->Recv();

m_packetsReceived++;

// Extract packet data

uint8_t buffer[1024];

packet->CopyData(buffer, packet->GetSize());

std::string receivedMessage((char *)buffer, packet->GetSize());

// Write received data to a file for analysis

std::ofstream dataFile(“data.txt”, std::ios::app);

dataFile << receivedMessage << std::endl;

dataFile.close();

// Call Python script to analyze the URL

std::string command = “python3 analyze_url.py data.txt”;

int result = system(command.c_str());

if (result == 0)

{

std::cout << “No phishing detected: ” << receivedMessage << std::endl;

}

else

{

std::cout << “Phishing detected: ” << receivedMessage << std::endl;

}

}

Ptr<Socket> m_socket;

Address m_peerAddress;

uint16_t m_peerPort;

uint32_t m_packetsReceived;

};

int main(int argc, char *argv[])

{

NodeContainer nodes;

nodes.Create(2); // Example: 2 nodes (1 client, 1 server)

PointToPointHelper pointToPoint;

pointToPoint.SetDeviceAttribute(“DataRate”, StringValue(“1Gbps”));

pointToPoint.SetChannelAttribute(“Delay”, StringValue(“2ms”));

NetDeviceContainer devices;

devices = pointToPoint.Install(nodes);

InternetStackHelper stack;

stack.Install(nodes);

Ipv4AddressHelper address;

address.SetBase(“10.1.1.0”, “255.255.255.0”);

Ipv4InterfaceContainer interfaces = address.Assign(devices);

uint16_t port = 9;

Ptr<PhishingDefenseApp> clientApp = CreateObject<PhishingDefenseApp>();

clientApp->Setup(InetSocketAddress(interfaces.GetAddress(1), port), port);

nodes.Get(0)->AddApplication(clientApp);

clientApp->SetStartTime(Seconds(2.0));

clientApp->SetStopTime(Seconds(60.0));

Ptr<PhishingDefenseApp> serverApp = CreateObject<PhishingDefenseApp>();

serverApp->Setup(InetSocketAddress(Ipv4Address::GetAny(), port), port);

nodes.Get(1)->AddApplication(serverApp);

serverApp->SetStartTime(Seconds(1.0));

serverApp->SetStopTime(Seconds(60.0));

Simulator::Run();

Simulator::Destroy();

return 0;

}

Python Script for URL Analysis (analyze_url.py)

import sys

from sklearn.externals import joblib

def analyze_url(file):

with open(file, ‘r’) as f:

url = f.readline().strip()

# Load the pre-trained model

model = joblib.load(‘url_model.pkl’)

# Analyze the URL (this is a dummy example, you should replace it with actual URL analysis)

if “phishing” in url:

return 1  # Phishing detected

else:

return 0  # No phishing

if __name__ == “__main__”:

file = sys.argv[1]

result = analyze_url(file)

sys.exit(result)

Explanation

1. Data Collection:
   • The PhishingDefenseApp class collects URLs in the ReceivePacket method and writes them to a file (data.txt).
2. URL Analysis:
   • The analyze_url.py script reads the URL from the file and uses a pre-trained machine learning model to classify it as phishing or legitimate.
   • This example uses a dummy model for simplicity; you should replace it with an actual URL analysis model.
3. Periodic Analysis:
   • URLs are analyzed periodically as they are received.

Compile and Run

1. Compile the Code: Compile the ns3 simulation code using the following command:

g++ -std=c++11 -o ns3-phishing-defense main.cc `pkg-config –cflags –libs ns3-dev`

1. Run the Simulation: Execute the compiled program:

./ns3-phishing-defense

1. Ensure Python Environment: Ensure that your Python environment is set up correctly, and the analyze_url.py script is in the same directory as the ns-3 simulation executable.
2. Prepare the Model: Train a URL analysis model and save it as url_model.pkl. This example uses a dummy model; you should replace it with a real one.

This setup will show a basic implementation of phishing defence in ns3. We need to expand it promote to include more sophisticated phishing detection techniques, additional nodes, and more complex network topologies as needed.

In the final, we all known some basic idea about how the phishing defence will perform in ns3 tool and also we also deliver the additional information regarding the phishing defence.

We are here to assist you with the implementation of Network Phishing Defense using the ns3 tool. Our guidance will help you utilize this tool effectively for your projects on trending topics. You can also get project ideas and comparative analyses from us. We support you in classifying malicious URLs, evaluating email content, and implementing machine learning techniques.