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Usage Guide

Detect Phishing URLs​

Quickly identify phishing attempts in URLs using the PhishingDetector class.

Example:​

from phishing_detection_py import PhishingDetector

detector = PhishingDetector(model_type="url")
result = detector.predict("http://example-phishing-site.com")
print(result)

This code will output the detection result, such as whether the URL is a phishing attempt and the confidence score.

Detect Phishing Emails​

Analyze email content for phishing indicators.

Example:​

from phishing_detection_py import PhishingDetector

detector = PhishingDetector(model_type="email")
result = detector.predict("Urgent: Your account is locked. Click here to unlock it.")
print(result)

This code evaluates the email text and provides phishing detection results.

Batch Processing​

You can process multiple URLs or emails at once using the BatchProcessor class.

Example:​

from phishing_detection_py.batch_processor import BatchProcessor

inputs = ["http://phishing-url.com", "https://safe-url.org"]
batch_processor = BatchProcessor(model_type="url")
results = batch_processor.process(inputs)
print(results)

This approach is ideal for handling large datasets efficiently.

Advanced Usage​

Customize your detection workflow or configurations.

Using Custom Configurations​

from phishing_detection_py.utils import load_config

config = load_config("path/to/config.yaml")
print(config)

This allows you to load and apply custom configurations for your detection process.

Interpreting Results​

The output of detection methods includes:

  • Labels: phishing or legitimate
  • Confidence Scores: A numerical value indicating the model's certainty.

Example Output:

[
    {"input": "http://phishing-url.com", "label": "phishing", "confidence": 0.98},
    {"input": "https://safe-url.org", "label": "legitimate", "confidence": 0.95}
]