How to Implement PUEA Detection in OMNeT++

To implement Primary User Emulation Attack (PUEA) detection in OMNeT++ has several steps that include generating a cognitive radio network (CRN) where secondary users (SUs) must identify malicious behaviour by other SUs pretending to be primary users (PUs). The aim is to classify these attacks and take proper action, like preventing the compromised channel or alerting other network nodes. The following is the procedure to implement the PUEA detection in OMNeT++:

Steps to Implement PUEA Detection in OMNeT++

Install OMNeT++ and INET Framework:
- Make sure that OMNeT++ and the INET framework are installed. INET delivers the essential tools for replicating the wireless networks, which are necessary for modelling cognitive radio networks.
Define the Network Topology:
- Generate a network topology using a .ned file that involves PUs, legitimate SUs, and possibly malicious SUs that attempt to perform PUEAs. The PUs has priority access to the spectrum, and the SUs need to identify and evade PUEAs.
Implement PUEA Detection Mechanism:
- Improve detection techniques that permit legitimate SUs to find PUEAs and it contains techniques such as signal strength analysis, localization, or machine learning-based approaches to distinguish among real PUs and attackers.
Simulate PUEA Scenarios:
- Design scenarios where malicious SUs attempt to simulate PUs, and legitimate SUs must identify these attacks. The detection mechanism should differentiate among the legitimate PUs and attackers.
Configure the Simulation Environment:
- Use the .ini file to configure parameters like signal characteristics, detection thresholds, mobility patterns, and the behaviour of legitimate and malevolent users.
Run the Simulation and Analyze Results:
- Implement the simulation and measure the efficiency of the PUEA detection mechanism and the parameters such as detection accuracy, false positives, and the effects on network performance.

Example: Implementing Basic PUEA Detection in OMNeT++

Define the Network Topology in a .ned File

// PUEADetectionNetwork.ned

package networkstructure;

import inet.node.inet.WirelessHost;

network PUEADetectionNetwork

{

parameters:

int numPUs = default(2); // Number of primary users

int numLegitimateSUs = default(5); // Number of legitimate secondary users

int numMaliciousSUs = default(2); // Number of malicious secondary users

submodules:

primaryUser[numPUs]: WirelessHost {

@display(“p=100,100”);

numApps = 1;

app[0].typename = “PrimaryUserApp”;

}

legitimateSU[numLegitimateSUs]: WirelessHost {

@display(“p=300,200”);

numApps = 1;

app[0].typename = “LegitimateSUApp”;

}

maliciousSU[numMaliciousSUs]: WirelessHost {

@display(“p=500,200”);

numApps = 1;

app[0].typename = “MaliciousSUApp”;

}

connections:

// Wireless communication is modeled, so no fixed connections are necessary

}

Implement the PUEA Detection Mechanism

Generate a C++ class for the liable secondary user application that has contains a simple PUEA detection algorithm.

#include <omnetpp.h>

#include <inet/applications/base/ApplicationBase.h>

using namespace omnetpp;

using namespace inet;

class LegitimateSUApp : public ApplicationBase

{

protected:

virtual void initialize(int stage) override;

virtual void handleMessageWhenUp(cMessage *msg) override;

void detectPUEA();

public:

virtual int numInitStages() const override { return NUM_INIT_STAGES; }

};

Define_Module(LegitimateSUApp);

void LegitimateSUApp::initialize(int stage)

{

ApplicationBase::initialize(stage);

if (stage == INITSTAGE_APPLICATION_LAYER) {

// Schedule initial detection

scheduleAt(simTime() + uniform(1, 3), new cMessage(“detectPUEA”));

}

void LegitimateSUApp::handleMessageWhenUp(cMessage *msg)

{

if (strcmp(msg->getName(), “detectPUEA”) == 0) {

detectPUEA();

scheduleAt(simTime() + uniform(1, 3), msg); // Re-schedule detection

} else {

delete msg;

}

void LegitimateSUApp::detectPUEA()

{

EV << “Detecting PUEA.” << endl;

// Example: Simple signal strength analysis for PUEA detection

double signalStrength = uniform(-90, -50); // Example signal strength

if (signalStrength > -60) { // Example threshold for detecting a potential PUEA

EV << “Potential PUEA detected. Signal strength: ” << signalStrength << endl;

// Implement further action, such as avoiding the channel or reporting the attack

} else {

EV << “No PUEA detected. Signal strength: ” << signalStrength << endl;

}

Implement Malicious Secondary User Behaviour

Generate a C++ class for the malicious secondary user application that emulates the PUEA behaviour.

class MaliciousSUApp : public ApplicationBase

{

protected:

virtual void initialize(int stage) override;

virtual void handleMessageWhenUp(cMessage *msg) override;

void emulatePrimaryUser();

public:

virtual int numInitStages() const override { return NUM_INITSTAGES; }

};

Define_Module(MaliciousSUApp);

void MaliciousSUApp::initialize(int stage)

{

ApplicationBase::initialize(stage);

if (stage == INITSTAGE_APPLICATION_LAYER) {

// Schedule initial PUEA emulation

scheduleAt(simTime() + uniform(1, 3), new cMessage(“emulatePU”));

}

void MaliciousSUApp::handleMessageWhenUp(cMessage *msg)

{

if (strcmp(msg->getName(), “emulatePU”) == 0) {

emulatePrimaryUser();

scheduleAt(simTime() + uniform(1, 3), msg); // Re-schedule emulation

} else {

delete msg;

}

void MaliciousSUApp::emulatePrimaryUser()

{

EV << “Emulating primary user to create a PUEA.” << endl;

// Simulate PUEA by transmitting a signal that mimics a primary user

cMessage *fakePUSignal = new cMessage(“FakePUSignal”);

send(fakePUSignal, “wlan$o”);

}

Configure the Simulation in the .ini File

network = networkstructure.PUEADetectionNetwork

sim-time-limit = 300s

# Legitimate SU settings

*.legitimateSU[*].wlan.mac.maxQueueSize = 1000;

*.legitimateSU[*].wlan.phy.transmitter.power = 2mW;

*.legitimateSU[*].mobility.bounds = “500m 500m”;

*.legitimateSU[*].app[0].detectionThreshold = -60dBm;

# Malicious SU settings

*.maliciousSU[*].wlan.mac.maxQueueSize = 1000;

*.maliciousSU[*].wlan.phy.transmitter.power = 10mW; # Malicious SUs may transmit at higher power to emulate PUs

*.maliciousSU[*].mobility.bounds = “500m 500m”;

Explanation of the Example

Network Topology (PUEADetectionNetwork.ned):
- The network consists of primary users (PUs), liable secondary users (legitimate SUs), and malicious secondary users (malicious SUs). The legitimate SUs attempt to discover PUEAs generated by the malicious SUs.
PUEA Detection Mechanism (LegitimateSUApp.cc):
- The LegitimateSUApp module has involves a simple PUEA detection techniques based on signal strength analysis. The detection mechanism intermittently test for signals that might signify a PUEA.
Malicious SU Behavior (MaliciousSUApp.cc):
- The MaliciousSUApp module replicate the behaviour of a secondary user endeavouring to implement a primary user, thereby generating a PUEA. This is completed by routing a signal that liable to SUs might mistake for a real PU signal.

Running the Simulation

Compile project in OMNeT++ IDE and execute the simulation.
Use OMNeT++’s tools to monitor how liable secondary users discover PUEAs and how malicious users attempt to implement primary users. Evaluate the parameters such as detection accuracy, false positives, and the effect of PUEAs on network performance.

Extending the Example

Advanced Detection Algorithms: Execute more sophisticated detection techniques like using location verification, machine learning algorithms, or cooperative detection between multiple SUs to enhance the detection accuracy.
Dynamic Behaviour of Malicious SUs: To emulate more complex behaviours for malicious Sus like varying the signal strength or timing of their attacks to avoid detection.
Impact Analysis: Study the effects of PUEA on the overall network performance, involves how it impacts throughput, latency, and spectrum utilization.
Cooperative PUEA Detection: Execute a cooperative detection mechanism where multiple SUs share their observations to enhance the accuracy of PUEA detection.
Realistic Mobility Models: Establish realistic mobility models for both liable and malevolent SUs to familiarize how movement impacts PUEA detection.

Finally, we explore the simple implementation procedures how to simulate the Primary User Emulation Attack (PUEA) detection in OMNeT++ tool and we also provide related information about Primary User Emulation Attack (PUEA) detection.

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