Can you create a function called result_analysis(data, detected, benign) that generates a report of statistical analysis for your intrusion detection system?

edit

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.