Family_Classifier_net.py

In this page

• Imported Modules
• Classes and functions

Imported Modules

• import configparser - implements a basic configuration language for Python programs - configparser documentation
• import os - provides a portable way of using operating system dependent functionality - os documentation

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• from torch import nn - a neural network library deeply integrated with autograd designed for maximum flexibility - torch.nn documentation

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• from .utils.Net import Net as baseNet

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Classes and functions

Net (class) - Family Classifier Network built using the MTJE model as base.

• __init__(self, families, feature_dimension, embedding_dimension, layer_sizes, fam_class_layer_sizes, dropout_p, activation_function, normalization_function) (member function) - Initialize net.
  • families (arg) - List of families to predict
  • feature_dimension (arg) - Dimension of the input data feature vector (default: 2381)
  • embedding_dimension (arg) - Joint latent space size (default: 32)
  • layer_sizes (arg) - Layer sizes (array of sizes) (default: None -> use [512, 512, 128])
  • fam_class_layer_sizes (arg) - Layer sizes (array of sizes) for the family classifier (default: None -> use [64, 32])
  • dropout_p (arg) - Dropout probability (default: 0.05)
  • activation_function (arg) - Non-linear activation function to use (may be "elu", "leakyRelu", "pRelu" or "relu") (default: "elu")
  • normalization_function (arg) - Normalization function to use (may be "layer_norm" or "batch_norm") (default: "batch_norm")
• forward(self, data) (member function) - Forward batch of data through the net.
  • data (arg) - Current batch of data (features)
• compute_loss(predictions, labels, loss_wts) (static member function) - Compute Net loss.
  • predictions (arg) - A dictionary of results from the Net
  • labels (arg) - A dictionary of labels
  • loss_wts (arg) - Unused (default: None)
• normalize_results(labels, predicted_probabilities, use_malware, use_count, use_tags) (static member function) - Take a set of results dicts and break them out into a single dict of 1d arrays with appropriate column names that pandas can convert to a DataFrame.
  • labels (arg) - Labels (ground truth)
  • predicted_probabilities (arg) - Family probabilities predicted by the model
  • use_malware (arg) - Whether to use malware/benignware labels as a target (default: False)
  • use_count (arg) - Whether to use the counts as an additional target (default: False)
  • use_tags (arg) - Whether to use SMART tags as additional targets (default: False)

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