Contrastive_Model_net.py - cmikke97/Automatic-Malware-Signature-Generation GitHub Wiki
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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
from .utils.Net import Net as baseNet
Net (class) - Contrastive (Siamese) Network built using the MTJE model as base and optimized using the Online Triplet Loss.
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__init__(self, feature_dimension, embedding_dimension, layer_sizes, dropout_p, activation_function, normalization_function)(member function) - Initialize net.-
feature_dimension(arg) - Dimension of the input data feature vector (default: 2381) -
embedding_dimension(arg) - Latent space size (unused) (default: 32) -
layer_sizes(arg) - Layer sizes (array of sizes) (default: None -> use [128, 64]) -
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")
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forward(self, data)(member function) - Forward batch of data through the net.-
data(arg) - Current batch of data (features)
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compute_loss(predictions, labels, loss_wts)(static member function) - Compute Net loss. Not Implemented. Defined here to make this class compatible to the Net class.-
predictions(arg) - A dictionary of results from the Net -
labels(arg) - A dictionary of labels -
loss_wts(arg) - Weights to assign to each head of the network (if it exists); defaults to values used in the ALOHA paper (1.0 for malware, 0.1 for count and each tag)
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normalize_results(labels_dict, results_dict, 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. Not Implemented. Defined here to make this class compatible to the Net class.-
labels_dict(arg) - Labels (ground truth) dictionary -
results_dict(arg) - Results (predicted labels) dictionary -
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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