1 | initial version |
Sure, here's a potential implementation of the result_analysis() function:
import numpy as np
import pandas as pd
def result_analysis(data, detected, benign):
"""
Generates a report of statistical analysis for an intrusion detection system.
Parameters:
data (pandas dataframe): A dataframe containing the true labels for each instance.
detected (list): A list of indices where the intrusion detection system detected an intrusion.
benign (list): A list of indices where the intrusion detection system classified an instance as benign.
Returns:
None (prints out statistical analysis report)
"""
# Calculate overall accuracy
accuracy = sum(data['label'] == data['prediction']) / len(data)
print(f"Overall accuracy: {accuracy:.2%}")
# Calculate precision, recall, and F1 score for intrusion detection
true_positives = sum(data['label'][detected] == 1)
false_positives = len(detected) - true_positives
false_negatives = sum(data['label'] == 1) - true_positives
precision = true_positives / (true_positives + false_positives)
recall = true_positives / (true_positives + false_negatives)
f1_score = 2 * precision * recall / (precision + recall)
print(f"\nIntrusion detection performance:")
print(f"Precision: {precision:.2%}")
print(f"Recall: {recall:.2%}")
print(f"F1 Score: {f1_score:.2%}")
# Calculate precision, recall, and F1 score for benign classification
true_negatives = sum(data['label'][benign] == 0)
false_negatives = len(benign) - true_negatives
false_positives = sum(data['label'] == 0) - true_negatives
precision = true_negatives / (true_negatives + false_negatives)
recall = true_negatives / (true_negatives + false_positives)
f1_score = 2 * precision * recall / (precision + recall)
print(f"\nBenign classification performance:")
print(f"Precision: {precision:.2%}")
print(f"Recall: {recall:.2%}")
print(f"F1 Score: {f1_score:.2%}")
# Calculate confusion matrix and print it out
confusion_matrix = pd.crosstab(data['label'], data['prediction'])
print(f"\nConfusion Matrix:")
print(confusion_matrix)
Here's an example of how you could use this function:
# Load in your data (make sure it has columns called 'label' and 'prediction')
data = pd.read_csv('data.csv')
# Detect intrusions
detected = [10, 20, 30, 40]
# Classify benign instances
benign = [100, 200, 300, 400]
# Generate report
result_analysis(data, detected, benign)
This would output a report of statistical analysis that includes overall accuracy, precision, recall, and F1 score for both intrusion detection and benign classification, as well as a confusion matrix. Of course, you would need to modify this function to fit your specific dataset and model.