How to Calculate Network Accuracy in omnet++
To calculate the network accuracy in OMNeT++ has several steps that based on the setting of the network emulation and what feature of the network we are concerned in measuring the accuracy for. In the networking, the accuracy is defined to numerous parameters like:
- Classification Accuracy: How accurately a network system like intrusion detection system identifies the packets or network events.
- Transmission Accuracy: How accurately data is transmitted without errors.
- Detection Accuracy: How accurately a system classifies the certain network events such as detecting attacks, anomalies, etc.
Example 1: Classification Accuracy
If we are evaluating the accuracy of a classification techniques like in an Intrusion Detection System that would usually estimate accuracy as:
Accuracy=Number of Correct PredictionsTotal Number of Predictions×100\text{Accuracy} = \frac{\text{Number of Correct Predictions}}{\text{Total Number of Predictions}} \times 100Accuracy=Total Number of PredictionsNumber of Correct Predictions×100
Implementing Classification Accuracy in OMNeT++
- Track Correct and Incorrect Predictions:
- During simulation, keep track of the number of correct and incorrect predictions made by the system.
int correctPredictions = 0;
int totalPredictions = 0;
- Update Counters Based on Predictions:
- Each time a prediction is made like whether a packet is malicious or benign, update the counters.
void updatePrediction(bool isCorrect) {
totalPredictions++;
if (isCorrect) {
correctPredictions++;
}
}
- Calculate and Record Accuracy:
double accuracy = (double)correctPredictions / totalPredictions * 100;
recordScalar(“Classification Accuracy (%)”, accuracy);
EV << “Classification Accuracy: ” << accuracy << ” %” << endl;
Example Implementation:
class MyClassifier : public cSimpleModule {
private:
int correctPredictions;
int totalPredictions;
simsignal_t accuracySignal;
protected:
virtual void initialize() override {
correctPredictions = 0;
totalPredictions = 0;
accuracySignal = registerSignal(“classificationAccuracy”);
}
virtual void handleMessage(cMessage *msg) override {
// Assuming msg is a packet or event to be classified
bool actualClass = /* true if positive, false if negative */;
bool predictedClass = /* result of classification logic */;
bool isCorrect = (actualClass == predictedClass);
updatePrediction(isCorrect);
delete msg;
}
void updatePrediction(bool isCorrect) {
totalPredictions++;
if (isCorrect) {
correctPredictions++;
}
}
virtual void finish() override {
if (totalPredictions > 0) {
double accuracy = (double)correctPredictions / totalPredictions * 100;
emit(accuracySignal, accuracy);
recordScalar(“Classification Accuracy (%)”, accuracy);
EV << “Classification Accuracy: ” << accuracy << ” %” << endl;
}
}
};
Example 2: Transmission Accuracy
If we are evaluating the accuracy of data transmission that includes to comparing the number of successfully transmitted packets to the number of packets sent, we can use a similar approach.
Transmission Accuracy=Number of Successfully Received PacketsTotal Number of Sent Packets×100\text{Transmission Accuracy} = \frac{\text{Number of Successfully Received Packets}}{\text{Total Number of Sent Packets}} \times 100Transmission Accuracy=Total Number of Sent PacketsNumber of Successfully Received Packets×100
Implementing Transmission Accuracy:
- Track Sent and Received Packets:
int packetsSent = 0;
int packetsReceived = 0;
- Update Counters Based on Transmission:
void onPacketSent() {
packetsSent++;
}
void onPacketReceived() {
packetsReceived++;
}
- Calculate and Record Transmission Accuracy:
double accuracy = (double)packetsReceived / packetsSent * 100;
recordScalar(“Transmission Accuracy (%)”, accuracy);
EV << “Transmission Accuracy: ” << accuracy << ” %” << endl;
Example 3: Detection Accuracy
In scenarios where we are identifying the particular events, like attacks or network anomalies, accuracy can be calculated using:
Detection Accuracy=True Positives+True NegativesTotal Number of Events×100\text{Detection Accuracy} = \frac{\text{True Positives} + \text{True Negatives}}{\text{Total Number of Events}} \times 100Detection Accuracy=Total Number of EventsTrue Positives+True Negatives×100
Implementing Detection Accuracy:
- Track True Positives, True Negatives, False Positives, and False Negatives:
int truePositives = 0;
int trueNegatives = 0;
int falsePositives = 0;
int falseNegatives = 0;
- Update Counters Based on Detection Results:
void onEventDetected(bool actual, bool predicted) {
if (actual && predicted) truePositives++;
if (!actual && !predicted) trueNegatives++;
if (!actual && predicted) falsePositives++;
if (actual && !predicted) falseNegatives++;
}
- Calculate and Record Detection Accuracy:
int totalEvents = truePositives + trueNegatives + falsePositives + falseNegatives;
double accuracy = (double)(truePositives + trueNegatives) / totalEvents * 100;
recordScalar(“Detection Accuracy (%)”, accuracy);
EV << “Detection Accuracy: ” << accuracy << ” %” << endl;
In this module, we clearly learned about how to estimate the accuracy for the particular network system that was applied in OMNeT++ tool. If you need more information related to the network accuracy we will support and provide that too.
In order to calculate network accuracy using the OMNeT++ tool for your research, please provide us with your parameter details, and we will offer you the best guidance available.