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API Reference

PhishingDetector Class​

Methods​

__init__(model_type: str = 'url')​

Initializes the phishing detector with the specified model type.

  • Parameters:
    • model_type (str): The type of model to use for detection. Options are:
      • 'url' for URL phishing detection.
      • 'email' for email phishing detection.

predict(input_data: str) -> List[Dict]​

Predicts whether the given input data is phishing or legitimate.

  • Parameters:
    • input_data (str): The input URL or email text to classify.
  • Returns: A list of dictionaries, where each dictionary contains:
    • input (str): The original input.
    • label (str): The classification result ('phishing' or 'legitimate').
    • confidence (float): The confidence score of the prediction.

Example:

from phishing_detection_py import PhishingDetector

detector = PhishingDetector(model_type="url")
result = detector.predict("http://example-phishing-site.com")
print(result)
# Output: [{'input': 'http://example-phishing-site.com', 'label': 'phishing', 'confidence': 0.98}]

predict_proba(input_data: str) -> Dict​

Returns the probabilities for each class ('phishing' or 'legitimate') for the given input.

  • Parameters:
    • input_data (str): The input URL or email text to classify.
  • Returns: A dictionary containing:
    • input (str): The original input.
    • probabilities (Dict[str, float]): The probabilities for each class.

Example:

result = detector.predict_proba("http://example-phishing-site.com")
print(result)
# Output: {'input': 'http://example-phishing-site.com', 'probabilities': {'phishing': 0.98, 'legitimate': 0.02}}

BatchProcessor Class​

Methods​

__init__(model_type: str = 'url')​

Initializes the batch processor for handling multiple inputs.

  • Parameters:
    • model_type (str): The type of model to use for batch processing. Options are 'url' or 'email'.

process(inputs: List[str]) -> List[Dict]​

Processes a batch of URLs or email texts and returns detection results for each input.

  • Parameters:
    • inputs (List[str]): A list of URLs or email texts to classify.
  • Returns: A list of dictionaries, each containing:
    • input (str): The original input.
    • label (str): The classification result.
    • confidence (float): The confidence score.

Example:

batch_processor = BatchProcessor(model_type="url")
inputs = ["http://phishing-url.com", "https://safe-url.org"]
results = batch_processor.process(inputs)
print(results)
# Output: [
#   {'input': 'http://phishing-url.com', 'label': 'phishing', 'confidence': 0.98},
#   {'input': 'https://safe-url.org', 'label': 'legitimate', 'confidence': 0.95}
# ]

utils Module​

Functions​

load_config(config_path: str) -> Dict​

Loads configuration from a YAML file.

  • Parameters:
    • config_path (str): Path to the YAML configuration file.
  • Returns: A dictionary containing configuration data.

Example:

from phishing_detection_py.utils import load_config

config = load_config("config.yaml")
print(config)

log_message(message: str, level: str = 'info')​

Logs messages at the specified level.

  • Parameters:
    • message (str): The message to log.
    • level (str): The log level ('info', 'warning', 'error').

Example:

from phishing_detection_py.utils import log_message

log_message("Phishing detection started", level="info")