How to Calculate Network Detection accuracy in omnet++
To calculate the network detection accuracy in OMNeT++ normally involves assessing how well a network-based detection system like an Intrusion Detection System (IDS) finds malicious activities or anomalies. It can be measured by comparing the number of properly identified events (true positives and true negatives) against the total number of events.
Given below is a procedure how to implementing and calculating network detection accuracy in OMNeT++:
Step-by-Step Implementations:
- Define Detection Metrics
Earlier calculating accuracy, we want to describe the related metrics:
- True Positives (TP): The number of properly identified malicious events.
- True Negatives (TN): The number of correctly identified non-malicious (normal) events.
- False Positives (FP): The number of normal events wrongly identified as malicious.
- False Negatives (FN): The number of malicious events wrongly identified as normal.
- Set up the Detection System
Execute or add a detection system into the OMNeT++ simulation. It should be an IDS, anomaly detection algorithm, or any extra form of detection logic.
- Simulate Network Traffic
Make network traffic that contains both normal and malicious activities. We can perform this by:
- Defining Normal Traffic: Mimic regular network communication.
- Injecting Malicious Traffic: Present specific events or messages that denote attacks or anomalies.
- Track Detection Events
Adjust the detection system to track the outcomes of each detection attempt. We can use counters to have track of TP, TN, FP, and FN.
int truePositives = 0;
int trueNegatives = 0;
int falsePositives = 0;
int falseNegatives = 0;
void evaluateDetection(bool isMalicious, bool isDetected) {
if (isMalicious && isDetected) {
truePositives++;
} else if (!isMalicious && !isDetected) {
trueNegatives++;
} else if (!isMalicious && isDetected) {
falsePositives++;
} else if (isMalicious && !isDetected) {
falseNegatives++;
}
}
- Implement Detection Logic
In the OMNeT++ simulation model, execute the detection logic. When a message is received, the model should assess whether it is malicious and whether the detection system properly identifies it.
void handleMessage(cMessage *msg) override {
bool isMalicious = checkIfMalicious(msg);
bool isDetected = detect(msg);
evaluateDetection(isMalicious, isDetected);
// Continue with normal message processing
}
- Calculate Detection Accuracy
To compute the detection accuracy using the following formula after tracking TP, TN, FP, and FN:
Accuracy=TP+TNTP+TN+FP+FN\text{Accuracy} = \frac{TP + TN}{TP + TN + FP + FN}Accuracy=TP+TN+FP+FNTP+TN​
We can implement this calculation in the OMNeT++ simulation at the end of the simulation run.
double calculateAccuracy() {
int total = truePositives + trueNegatives + falsePositives + falseNegatives;
if (total == 0) return 0.0; // Avoid division by zero
return (double)(truePositives + trueNegatives) / total;
}
void finish() override {
double accuracy = calculateAccuracy();
EV << “Detection Accuracy: ” << accuracy * 100 << “%” << endl;
}
- Emit and Record Accuracy
We can also emit the accuracy as a signal for more analysis:
simsignal_t accuracySignal;
void initialize() override {
accuracySignal = registerSignal(“detectionAccuracy”);
}
void finish() override {
double accuracy = calculateAccuracy();
emit(accuracySignal, accuracy);
}
- Analyze the Results
After the simulation, use OMNeT++’s analysis tools or transfer the results to analyse the detection accuracy across various scenarios or configurations.
Example Scenario
Given below is an example of how we might integrate this logic into an OMNeT++ simulation:
class NetworkNode : public cSimpleModule {
private:
int truePositives = 0;
int trueNegatives = 0;
int falsePositives = 0;
int falseNegatives = 0;
simsignal_t accuracySignal;
protected:
virtual void initialize() override {
accuracySignal = registerSignal(“detectionAccuracy”);
}
virtual void handleMessage(cMessage *msg) override {
bool isMalicious = checkIfMalicious(msg);
bool isDetected = detect(msg);
evaluateDetection(isMalicious, isDetected);
// Normal message processing
send(msg, “out”);
}
bool checkIfMalicious(cMessage *msg) {
// Logic to determine if the message is malicious
return false; // Placeholder
}
bool detect(cMessage *msg) {
// Your detection logic
return false; // Placeholder
}
void evaluateDetection(bool isMalicious, bool isDetected) {
if (isMalicious && isDetected) {
truePositives++;
} else if (!isMalicious && !isDetected) {
trueNegatives++;
} else if (!isMalicious && isDetected) {
falsePositives++;
} else if (isMalicious && !isDetected) {
falseNegatives++;
}
}
double calculateAccuracy() {
int total = truePositives + trueNegatives + falsePositives + falseNegatives;
if (total == 0) return 0.0;
return (double)(truePositives + trueNegatives) / total;
}
virtual void finish() override {
double accuracy = calculateAccuracy();
EV << “Detection Accuracy: ” << accuracy * 100 << “%” << endl;
emit(accuracySignal, accuracy);
}
};
- Run the Simulation
Perform the simulation with the described scenarios and analyse the detection accuracy. This will help to know the efficiency of the network detection system.
In conclude, we are expressed how to executing and calculating Network Detection Accuracy in OMNeT++. We will deliver further data as per your needs. Omnet-manual.com will assist you with the network detection accuracy in the omnet++ tool for your project, so please provide us with your parameter information. We continue network project performance depending on your parameters.