def analyze_syscalls():
# Parameters
min_word_count = 5 # Minimum word count
context = 10 # Context window size
paths = [
"/var/lib/arhuaco/data/normal_clean.csv",
"/var/lib/arhuaco/data/malicious_clean.csv"
]
labels = [0, 1]
number_samples = 2
number_samples_w2v = 10000
num_epochs = 10
embedding_dim = 10
# Model Hyperparameters
max_length = 7
n_gram = 6
# Create objects
data_helpers = DataHelpers(paths, labels, max_length, n_gram,
number_samples)
w2v = W2V()
sentence_stream = data_helpers.sentence_stream(number_samples_w2v)
params = w2v.train_word2vec_stream(sentence_stream,
num_features=embedding_dim,
min_word_count=min_word_count,
context=context,
num_epochs=num_epochs)
# Create the model
classes = np.array([0, 1])
clf = SGDClassifier(loss='hinge',
penalty="l2",
eta0=0.01,
learning_rate='constant')
# Data load
data_generator = data_helpers.get_data_chunk(params[1])
# Training the model
train_accuracy = []
test_accuracy = []
x_train, y_train = next(data_generator)
clf.partial_fit(x_train, y_train, classes=classes)
for batch in range(num_epochs):
for sample in range(1000):
x_train_aux, y_train_aux = next(data_generator)
# x_test_aux, y_test_aux = next(data_generator)
x_train = np.concatenate([x_train, x_train_aux], 0)
y_train = np.concatenate([y_train, y_train_aux], 0)
# x_test = np.concatenate([x_test, x_test_aux], 0)
# y_test = np.concatenate([y_test, y_test_aux], 0)
clf.partial_fit(x_train, y_train)
print("Batch: %d" % batch)
print('Train Accuracy: %.3f' % clf.score(x_train, y_train))
# print('Test Accuracy: %.3f' % clf.score(x_test, y_test))
train_accuracy.append(clf.score(x_train, y_train))
# test_accuracy.append(clf.score(x_test, y_test))
# Plot the results
plot = Plot()
plot.history2plot([train_accuracy], "Model accuracy", "Epoch", "Accuracy")
def analyze_network():
# Parameters
seed = 5
model_variation = 'svm-non-static'
# Model Hyperparameters
# Max lenght of one sentence
max_length = 5
# Number of lines included in the
# series
n_gram = 1
# Total lenght of the classification
# object
sequence_length = max_length*n_gram
# Size of the vector representing each word
embedding_dim = 10
dropout_prob = (0.25, 0.5)
# Number of neurons in the hidden layer
hidden_dims = 10
# Training parameters
number_samples = 5
number_samples_w2v = 10000
num_epochs = 100
val_split = 0.1
# Word2Vec parameters, see train_word2vec
# Minimum word count
min_word_count = 1
# Number of words that make sense in the context
context = 4
action = "predict"
weights_file = "/var/lib/arhuaco/data/models/weights_file"
sentence = "process ptrace request=11(PTRACE_ATTACH)"
# Training dataset
paths = [ "/var/lib/arhuaco/data/dns_normal.log", "/var/lib/arhuaco/data/dns_malicious.log"]
# Training labels
labels = [ 0, 1 ]
# Create objects
data_helpers = DataHelpers( paths, labels, max_length, n_gram, number_samples)
w2v = W2V()
sentence_stream = data_helpers.sentence_stream(number_samples_w2v)
params = w2v.train_word2vec_stream(sentence_stream,
num_features=embedding_dim,
min_word_count=min_word_count, context=context,
num_epochs=num_epochs)
svm_w2v = svmW2v(seed, model_variation, sequence_length, embedding_dim,
filter_sizes, num_filters, dropout_prob, hidden_dims, number_samples,
num_epochs, val_split, min_word_count, context, action, weights_file,
sentence, paths, labels, data_helpers)
svm_w2v.get_data(params[0], params[1], params[2])
svm_w2v.build_model()
svm_w2v.train_model()
def analyze_network():
clf = SGDClassifier(loss='hinge', penalty="l2")
# Word2Vec parameters, see train_word2vec
min_word_count = 1 # Minimum word count
context = 4 # Context window size
paths = [
"/var/lib/arhuaco/data/dns_normal.log",
"/var/lib/arhuaco/data/dns_malicious.log"
]
labels = [0, 1]
number_samples = 10
num_epochs = 100
embedding_dim = 5
# Model Hyperparameters
max_length = 5
n_gram = 1
# Create objects
data_helpers = data_helpers = DataHelpers(paths, labels, max_length,
n_gram, number_samples)
w2v = W2V()
# Load data
print("Loading data...")
x, y, vocabulary, vocabulary_inv = data_helpers.load_data()
embedding_weights, vocabulary = w2v.train_word2vec(x, embedding_dim,
min_word_count, context)
classes = np.array([0, 1])
# Data load
data_generator = data_helpers.get_data_chunk(vocabulary)
# Training the model
train_accuracy = []
train_loss = []
test_accuracy = []
test_loss = []
for batch in range(num_epochs):
x_train, y_train = next(data_generator)
x_test, y_test = next(data_generator)
clf.partial_fit(x_train, y_train, classes=classes)
print("Batch: %d" % batch)
print('Train Accuracy: %.3f' % clf.score(x_train, y_train))
print('Test Accuracy: %.3f' % clf.score(x_test, y_test))
train_accuracy.append(clf.score(x_train, y_train))
test_accuracy.append(clf.score(x_test, y_test))
# Plot the results
plot = Plot()
plot.history2plot([train_accuracy, test_accuracy], "Model accuracy",
"Epoch", "Accuracy")
def optimize_cnn_hyperparameters(self,
tokens_per_line,
number_lines,
type="syscall"):
# Load configuration
config_object = Configuration()
if type == "syscall":
config_object.load_configuration("host")
configuration = config_object.default_config
# Training parameters
configuration['verbose'] = 2
configuration['samples_per_batch'] = 5
configuration['samples_per_epoch'] = 10000
configuration['num_epochs'] = 100
configuration['val_split'] = 0.1
configuration['weights_file_conv'] = "/var/lib/arhuaco/data/models/sys_W_conv-%s"\
% time.strftime("%Y%m%d-%H%M%S")
configuration['model_file_conv'] = "/var/lib/arhuaco/data/models/sys_model_conv-%s.json"\
% time.strftime("%Y%m%d-%H%M%S")
# Training dataset
configuration['paths'] = [
"/var/lib/arhuaco/data/normal_clean_filtered.csv",
"/var/lib/arhuaco/data/malicious_clean_filtered.csv"
]
elif type == "network":
# Load configuration
config_object = Configuration()
config_object.load_configuration("network")
configuration = config_object.default_config
# Training parameters
configuration['verbose'] = 2
configuration['samples_per_batch'] = 5
configuration['samples_per_epoch'] = 1000
configuration['num_epochs'] = 100
configuration['val_split'] = 0.1
configuration['weights_file_conv'] = "/var/lib/arhuaco/data/models/net_W_conv-%s"\
% time.strftime("%Y%m%d-%H%M%S")
configuration['model_file_conv'] = "/var/lib/arhuaco/data/models/net_model_conv-%s.json"\
% time.strftime("%Y%m%d-%H%M%S")
# Training dataset
paths = [
"/var/lib/arhuaco/data/dns_normal.log",
"/var/lib/arhuaco/data/dns_malicious.log"
]
# Create objects
# First create the sources of data
data_helpers = DataHelpers(
data_source=configuration['paths'],
label=None,
tokens_per_line=tokens_per_line,
number_lines=number_lines,
samples_per_batch=configuration['samples_per_batch'],
seed=configuration['seed'])
# Apply the word2vec processing
w2v = W2V()
sentence_stream = data_helpers.sentence_stream(
configuration['samples_per_epoch'])
params = w2v.train_word2vec_stream(
sentence_stream,
num_features=configuration['embedding_dim'],
min_word_count=configuration['min_word_count'],
context=configuration['context'],
num_epochs=configuration['num_epochs'])
embedding_weights = params[0]
vocabulary = params[1]
vocabulary_index = params[2]
# Create the Convolutional network object
cnn_w2v = CnnW2v(seed=configuration['seed'],
samples_per_batch=configuration['samples_per_batch'],
min_word_count=configuration['min_word_count'],
context=configuration['context'],
weights_file=configuration['weights_file_conv'],
model_file=configuration['model_file_conv'],
labels=None,
verbose=configuration['verbose'])
cnn_w2v.set_w2v_params(embedding_weights=params[0],
vocabulary=params[1],
vocabulary_index=params[2])
print("Convolutional optimization")
# Get the data sources
training_generator = data_helpers.get_data_chunk(
vocabulary, configuration['labels_conv'])
validation_generator = data_helpers.get_data_chunk(
vocabulary, configuration['labels_conv'])
test_generator = data_helpers.get_data_chunk(
vocabulary, configuration['labels_conv'])
# Create model for grid search
model = KerasClassifier(build_fn=cnn_w2v.build_model,
epochs=8,
batch_size=10,
verbose=3)
# Define the grid search parameters
learn_rate = [0.001, 0.01, 0.1]
momentum = [0.0, 0.2, 0.4, 0.6, 0.8, 0.9]
decay = [0.0, 1e-5, 1e-6, 1e-7]
nesterov = [True, False]
regularizer_param = [0.1, 0.01, 0.001]
hidden_neurons = [5, 10, 20, 30]
num_filters = [5, 10, 20, 30]
filter_sizes = [(1, 2, 3, 4), (3, 4, 5), (5, 6)]
dropout_rate = [0.0, 0.5, 0.1, 0.01]
embedding_dim = [configuration['embedding_dim']]
pool_size = [2, 3, 4]
sequence_length = [tokens_per_line * number_lines]
print("Starting grid search for %d tokens per line and %d lines" %
(tokens_per_line, number_lines))
param_grid = dict(learn_rate=learn_rate,
momentum=momentum,
decay=decay,
nesterov=nesterov,
regularizer_param=regularizer_param,
hidden_neurons=hidden_neurons,
num_filters=num_filters,
filter_sizes=filter_sizes,
dropout_rate=dropout_rate,
embedding_dim=embedding_dim,
pool_size=pool_size,
sequence_length=sequence_length)
grid = GridSearchCV(estimator=model,
param_grid=param_grid,
n_jobs=-1,
verbose=3)
print("Extracting data from source...")
X, Y = next(training_generator)
# TODO: fix this! It is too slow...
for i in range(configuration['samples_per_epoch']):
X_i, Y_i = next(training_generator)
X = np.append(X, X_i, axis=0)
Y = np.append(Y, Y_i, axis=0)
print("Starting grid search trainings...")
grid_result = grid.fit(X, Y, verbose=3)
# summarize results
print("Best: %f using %s" %
(grid_result.best_score_, grid_result.best_params_))
means = grid_result.cv_results_['mean_test_score']
stds = grid_result.cv_results_['std_test_score']
params = grid_result.cv_results_['params']
for mean, stdev, param in zip(means, stds, params):
print("%f (%f) with: %r" % (mean, stdev, param))
def analyze_syscalls():
# Parameters
seed = 5
verbose = 2
# Model Hyperparameters
# Max lenght of one sentence
max_length = 7
# Number of lines included in the
# series
n_gram = 6
# Total lenght of the classification
# object
sequence_length = max_length * n_gram
# Size of the vector representing each word
embedding_dim = 20
dropout_prob = (0.0, 0.0)
# Number of neurons in the hidden layer
hidden_dims = 20
# Training parameters
number_samples = 5
samples_per_epoch = 10000
num_epochs = 100
val_split = 0.1
# Word2Vec parameters, see train_word2vec
# Minimum word count
min_word_count = 6
# Number of words that make sense in the context
context = 10
weights_file_svm = "/var/lib/arhuaco/data/models/sys_W_svm-%s"\
% time.strftime("%Y%m%d-%H%M%S")
model_file_svm = "/var/lib/arhuaco/data/models/sys_model_svm-%s.json"\
% time.strftime("%Y%m%d-%H%M%S")
# Training dataset
paths = [
"/var/lib/arhuaco/data/normal_clean.csv",
"/var/lib/arhuaco/data/malicious_clean.csv"
]
# Training labels
labels_svm = [-1, 1]
# Create objects
data_helpers = DataHelpers(paths, None, max_length, n_gram, number_samples,
seed)
w2v = W2V()
sentence_stream = data_helpers.sentence_stream(samples_per_epoch)
params = w2v.train_word2vec_stream(sentence_stream,
num_features=embedding_dim,
min_word_count=min_word_count,
context=context,
num_epochs=num_epochs)
svm = SVM(seed, sequence_length, embedding_dim, dropout_prob, hidden_dims,
number_samples, num_epochs, val_split, min_word_count, context,
weights_file_svm, model_file_svm, paths, None, data_helpers,
verbose)
svm.get_data(params[0], params[1], params[2])
svm.build_model()
print("SVM syscall training")
history_svm = svm.train_model(samples_per_epoch, labels_svm)
result = svm.test_model(10000, labels_svm, max_length, n_gram)
# Graphically plot the results
plot = Plot()
# Training vs validation
plot.history2plot([
history_svm.history['real_accuracy'],
history_svm.history['val_real_accuracy']
], ['Training', 'Validation'],
"SVM accuracy",
"Epoch",
"Accuracy",
"/var/lib/arhuaco/data/models/sys_svm_accuracy-%s.pdf" %
time.strftime("%Y%m%d-%H%M%S"),
location='lower right')
# Trainning vs validation fpr
plot.history2plot([history_svm.history['false_pos_rate'],
history_svm.history['val_false_pos_rate']],
['Training', 'Validation'],
"SVM false positive rate", "Epoch",
"False positive rate",
"/var/lib/arhuaco/data/models/sys_svm_fpr-%s.pdf"\
% time.strftime("%Y%m%d-%H%M%S"),
location='upper right')
def build_model(self,type="syscall"):
# Load configuration
config_object = Configuration()
if type == "syscall":
# Load configuration
config_object.load_configuration("host")
configuration = config_object.default_config
configuration['input_queue'] = self.input_queue_dict["syscall_sensor"]
# Training parameters
configuration['verbose'] = 2
configuration['samples_per_batch'] = 5
configuration['samples_per_epoch'] = 100000
configuration['num_epochs'] = 10
configuration['val_split'] = 0.1
configuration['weights_file_conv'] = "/var/lib/arhuaco/data/models/sys_W_conv.bin"
configuration['model_file_conv'] = "/var/lib/arhuaco/data/models/sys_model_conv.json"
elif type == "network":
# Load configuration
config_object = Configuration()
config_object.load_configuration("network")
configuration = config_object.default_config
configuration['input_queue'] = self.input_queue_dict["network_sensor"]
# Training parameters
configuration['verbose'] = 2
configuration['samples_per_batch'] = 5
configuration['samples_per_epoch'] = 10000
configuration['num_epochs'] = 10
configuration['val_split'] = 0.1
configuration['weights_file_conv'] = "/var/lib/arhuaco/data/models/net_W_conv.bin"
configuration['model_file_conv'] = "/var/lib/arhuaco/data/models/net_model_conv.json"
w2v_model_name = "{:d}features_{:d}minwords_{:d}context".format(
configuration['num_features'],
configuration['min_word_count'],
configuration['context'])
# Create objects
# Apply the word2vec processing
w2v = W2V()
params = w2v.load_word2vec_model(w2v_model_name)
embedding_weights=params[0]
configuration['vocabulary']=params[1]
configuration['vocabulary_index']=params[2]
# Create the Convolutional network object
cnn_w2v = CnnW2v(seed=configuration['seed'],
samples_per_batch=configuration['samples_per_batch'],
min_word_count=configuration['min_word_count'],
context=configuration['context'],
weights_file=configuration['weights_file_conv'],
model_file=configuration['model_file_conv'],
labels=None,
verbose=configuration['verbose'])
cnn_w2v.set_w2v_params(embedding_weights=embedding_weights,
vocabulary=configuration['vocabulary'],
vocabulary_index=configuration['vocabulary_index'])
# Buid the model
# Do I really need to build the model again?
# What is in the the model.json file then?
cnn_w2v.build_model(learn_rate=configuration['learn_rate'],
momentum=configuration['momentum'],
decay=configuration['decay'],
nesterov=configuration['nesterov'],
regularizer_param=configuration['regularizer_param'],
hidden_neurons=configuration['hidden_dims'],
num_filters=configuration['num_filters'],
filter_sizes=configuration['filter_sizes'],
dropout_rate=configuration['dropout_prob'],
embedding_dim=configuration['embedding_dim'],
pool_size=configuration['pool_size'],
sequence_length=configuration['sequence_length']
)
cnn_w2v.load_model_weights(configuration['weights_file_conv'])
return cnn_w2v, configuration
def train(self, type="syscall"):
# Load configuration
config_object = Configuration()
if type == "syscall":
config_object.load_configuration("host")
configuration = config_object.default_config
# Training parameters
configuration['verbose'] = 2
configuration['samples_per_batch'] = 5
configuration['samples_per_epoch'] = 100000
configuration['num_epochs'] = 10
configuration['val_split'] = 0.1
configuration['weights_file_svm'] = "/var/lib/arhuaco/data/models/sys_W_svm-%s"\
% time.strftime("%Y%m%d-%H%M%S")
configuration['model_file_svm'] = "/var/lib/arhuaco/data/models/sys_model_svm-%s.json"\
% time.strftime("%Y%m%d-%H%M%S")
# Training dataset
configuration['paths'] = [
"/var/lib/arhuaco/data/normal_clean_filtered.csv",
"/var/lib/arhuaco/data/malicious_clean_filtered.csv"
]
configuration['pdf_paths'] = ["/var/lib/arhuaco/data/models/sys_svm_accuracy-%s.pdf"
% time.strftime("%Y%m%d-%H%M%S"),
"/var/lib/arhuaco/data/models/sys_svm_fpr-%s.pdf"\
% time.strftime("%Y%m%d-%H%M%S")]
elif type == "network":
# Load configuration
config_object = Configuration()
config_object.load_configuration("network")
configuration = config_object.default_config
# Training parameters
configuration['verbose'] = 2
configuration['samples_per_batch'] = 5
configuration['samples_per_epoch'] = 1000
configuration['num_epochs'] = 10
configuration['val_split'] = 0.1
configuration['weights_file_svm'] = "/var/lib/arhuaco/data/models/net_W_svm-%s"\
% time.strftime("%Y%m%d-%H%M%S")
configuration['model_file_svm'] = "/var/lib/arhuaco/data/models/net_model_svm-%s.json"\
% time.strftime("%Y%m%d-%H%M%S")
# Training dataset
configuration['paths'] = [
"/var/lib/arhuaco/data/dns_normal.log",
"/var/lib/arhuaco/data/dns_malicious.log"
]
# "/var/lib/arhuaco/data/dns_malicious_generated.log"]
configuration['pdf_paths'] = ["/var/lib/arhuaco/data/models/net_svm_accuracy-%s.pdf"
% time.strftime("%Y%m%d-%H%M%S"),
"/var/lib/arhuaco/data/models/net_svm_fpr-%s.pdf"\
% time.strftime("%Y%m%d-%H%M%S")]
# Create objects
# First create the sources of data
data_helper = DataHelpers(
data_source=configuration['paths'],
label=None,
tokens_per_line=configuration['tokens_per_line'],
number_lines=configuration['number_lines'],
samples_per_batch=configuration['samples_per_batch'],
seed=configuration['seed'])
# Apply the word2vec processing
w2v = W2V()
sentence_stream = data_helper.sentence_stream(
configuration['samples_per_epoch'])
params = w2v.train_word2vec_stream(
sentence_stream,
num_features=configuration['embedding_dim'],
min_word_count=configuration['min_word_count'],
context=configuration['context'],
num_epochs=configuration['num_epochs'])
embedding_weights = params[0]
vocabulary = params[1]
vocabulary_index = params[2]
# Create the svm network object
svm_bow = SVM(seed=configuration['seed'],
samples_per_batch=configuration['samples_per_batch'],
min_word_count=configuration['min_word_count'],
context=configuration['context'],
weights_file=configuration['weights_file_svm'],
model_file=configuration['model_file_svm'],
labels=None,
verbose=configuration['verbose'])
svm_bow.set_bow_params(embedding_weights=params[0],
vocabulary=params[1],
vocabulary_index=params[2])
# Buid the model
svm_bow.build_model(
learn_rate=configuration['learn_rate'],
momentum=configuration['momentum'],
decay=configuration['decay'],
nesterov=configuration['nesterov'],
regularizer_param=configuration['regularizer_param'],
dropout_rate=configuration['dropout_prob'],
embedding_dim=configuration['embedding_dim'],
)
print("svm training")
# Get the data sources
training_generator = data_helper.get_data_BoW_chunk(
vocabulary, configuration['labels_svm'])
validation_generator = data_helper.get_data_BoW_chunk(
vocabulary, configuration['labels_svm'])
# Train and validate the model
history_object = svm_bow.train_model(training_source=training_generator,
validation_source=validation_generator,
samples_per_epoch\
=configuration['samples_per_epoch'],
number_epochs=configuration['num_epochs'],
val_split=configuration['val_split'])
# Test the model with new data
# Create a new data source for validation with generated data
configuration['paths'][1] = '/var/lib/arhuaco/data/dns_malicious.log'
configuration['samples_per_epoch'] = 1000
validation_data_helper = DataHelpers(
data_source=configuration['paths'],
label=None,
tokens_per_line=configuration['tokens_per_line'],
number_lines=configuration['number_lines'],
samples_per_batch=configuration['samples_per_batch'],
seed=configuration['seed'] + 3)
test_generator = validation_data_helper.get_data_BoW_chunk(
vocabulary, configuration['labels_svm'])
result = svm_bow.test_model(
test_data_source=test_generator,
samples_to_test=configuration['samples_per_epoch'])
# Graphically plot the results
plot = Plot()
# Training vs validation accuracy
plot.history2plot([
history_object.history['real_accuracy'],
history_object.history['val_real_accuracy']
], ['Training', 'Validation'], "svm accuracy", "Epoch", "Accuracy",
configuration['pdf_paths'][0], 'lower right', [0, 9],
[0.8, 1.0])
# Trainning vs validation fpr
plot.history2plot([
history_object.history['false_pos_rate'],
history_object.history['val_false_pos_rate']
], ['Training', 'Validation'], "svm false positive rate", "Epoch",
"False positive rate", configuration['pdf_paths'][1],
'upper right', [0, 9], [0, 0.2])
def analyze_network():
# Parameters
seed = 5
model_variation = 'CNN-non-static'
# Model Hyperparameters
# Max lenght of one sentence
max_length = 5
# Number of lines included in the
# series
n_gram = 1
# Total lenght of the classification
# object
sequence_length = max_length * n_gram
# Size of the vector representing each word
embedding_dim = 10
# Conv. Filters applied to the text
filter_sizes = (2, 3)
# Total filters used
num_filters = 3
dropout_prob = (0.0, 0.0)
# Number of neurons in the hidden layer
hidden_dims = 10
# Training parameters
number_samples = 5
samples_per_epoch = 1000
num_epochs = 100
val_split = 0.1
verbose = 2
# Word2Vec parameters, see train_word2vec
# Minimum word count
min_word_count = 1
# Number of words that make sense in the context
context = 4
weights_file_conv = "/var/lib/arhuaco/data/models/net_W_conv-%s"\
% time.strftime("%Y%m%d-%H%M%S")
model_file_conv = "/var/lib/arhuaco/data/models/net_model_conv-%s.json"\
% time.strftime("%Y%m%d-%H%M%S")
# Training dataset
paths = [
"/var/lib/arhuaco/data/dns_normal.log",
#"/var/lib/arhuaco/data/dns_malicious.log"]
"/var/lib/arhuaco/data/dns_malicious_generated.log"
]
# Training labels
labels_conv = [0, 1]
# Create objects
data_helpers = DataHelpers(paths, None, max_length, n_gram, number_samples,
seed)
w2v = W2V()
sentence_stream = data_helpers.sentence_stream(samples_per_epoch)
params = w2v.train_word2vec_stream(sentence_stream,
num_features=embedding_dim,
min_word_count=min_word_count,
context=context,
num_epochs=num_epochs)
cnn_w2v = CnnW2v(seed, model_variation, sequence_length, embedding_dim,
filter_sizes, num_filters, dropout_prob, hidden_dims,
number_samples, num_epochs, val_split, min_word_count,
context, weights_file_conv, model_file_conv, paths, None,
data_helpers, verbose)
cnn_w2v.get_data(params[0], params[1], params[2])
cnn_w2v.build_model()
print("Convolutional network training")
history_conv = cnn_w2v.train_model(samples_per_epoch, labels_conv)
cnn_w2v.paths[1] = "/var/lib/arhuaco/data/dns_malicious.log"
result = cnn_w2v.test_model(1000, labels_conv, max_length, n_gram)
# Graphically plot the results
plot = Plot()
# Training vs validation
plot.history2plot([history_conv.history['real_accuracy'],
history_conv.history['val_real_accuracy']],
['Training', 'Validation'],
"CNN accuracy", "Epoch", "Accuracy",
"/var/lib/arhuaco/data/models/net_cnn_accuracy-%s.pdf"\
% time.strftime("%Y%m%d-%H%M%S"),
location='lower right')
# Trainning vs validation fpr
plot.history2plot([history_conv.history['false_pos_rate'],
history_conv.history['val_false_pos_rate']],
['Training', 'Validation'],
"CNN false positive rate", "Epoch",
"False positive rate",
"/var/lib/arhuaco/data/models/net_cnn_fpr-%s.pdf"\
% time.strftime("%Y%m%d-%H%M%S"),
location='upper right')
def train(self,
type="syscall"
):
# Load configuration
config_object = Configuration()
if type == "syscall":
config_object.load_configuration("host")
configuration = config_object.default_config
# Training parameters
configuration['verbose'] = 1
configuration['samples_per_batch'] = 5
# Thesis configuration
configuration['samples_per_epoch'] = 100000
# Test configuration to show concept
# configuration['samples_per_epoch'] = 1000
configuration['num_epochs'] = 10
configuration['val_split'] = 0.1
configuration['weights_file_conv'] = "/var/lib/arhuaco/data/models/sys_W_conv-%s"\
% time.strftime("%Y%m%d-%H%M%S")
configuration['model_file_conv'] = "/var/lib/arhuaco/data/models/sys_model_conv-%s.json"\
% time.strftime("%Y%m%d-%H%M%S")
# Training dataset
configuration['paths'] = [ "/var/lib/arhuaco/data/normal_clean_filtered.csv",
"/var/lib/arhuaco/data/malicious_clean_filtered.csv"]
configuration['pdf_paths'] = ["/var/lib/arhuaco/data/models/sys_cnn_accuracy-%s.pdf"
% time.strftime("%Y%m%d-%H%M%S"),
"/var/lib/arhuaco/data/models/sys_cnn_fpr-%s.pdf"\
% time.strftime("%Y%m%d-%H%M%S")]
elif type == "network":
# Load configuration
config_object = Configuration()
config_object.load_configuration("network")
configuration = config_object.default_config
# Training parameters
configuration['verbose'] = 1
configuration['samples_per_batch'] = 5
# Thesis configuration
# configuration['samples_per_epoch'] = 10000
# Test configuration to show concept
configuration['samples_per_epoch'] = 1000
configuration['num_epochs'] = 10
configuration['val_split'] = 0.1
configuration['weights_file_conv'] = "/var/lib/arhuaco/data/models/net_W_conv-%s"\
% time.strftime("%Y%m%d-%H%M%S")
configuration['model_file_conv'] = "/var/lib/arhuaco/data/models/net_model_conv-%s.json"\
% time.strftime("%Y%m%d-%H%M%S")
# Training dataset
paths = [ "/var/lib/arhuaco/data/dns_normal.log",
"/var/lib/arhuaco/data/dns_malicious.log"]
configuration['pdf_paths'] = ["/var/lib/arhuaco/data/models/net_cnn_accuracy-%s.pdf"
% time.strftime("%Y%m%d-%H%M%S"),
"/var/lib/arhuaco/data/models/net_cnn_fpr-%s.pdf"\
% time.strftime("%Y%m%d-%H%M%S")]
# Create objects
# First create the sources of data
data_helpers = DataHelpers(data_source=configuration['paths'],
label=None,
tokens_per_line=configuration['tokens_per_line'],
number_lines=configuration['number_lines'],
samples_per_batch=configuration['samples_per_batch'],
seed=configuration['seed'])
# Apply the word2vec processing
w2v = W2V()
sentence_stream = data_helpers.sentence_stream(
configuration['samples_per_epoch'])
params = w2v.train_word2vec_stream(sentence_stream,
num_features=configuration['embedding_dim'],
min_word_count=configuration['min_word_count'],
context=configuration['context'],
num_epochs=configuration['num_epochs'])
embedding_weights=params[0]
vocabulary=params[1]
vocabulary_index=params[2]
# Create the Convolutional network object
cnn_w2v = CnnW2v(seed=configuration['seed'],
samples_per_batch=configuration['samples_per_batch'],
min_word_count=configuration['min_word_count'],
context=configuration['context'],
weights_file=configuration['weights_file_conv'],
model_file=configuration['model_file_conv'],
labels=None,
verbose=configuration['verbose'])
cnn_w2v.set_w2v_params(embedding_weights=params[0],
vocabulary=params[1],
vocabulary_index=params[2])
# Buid the model
cnn_w2v.build_model(learn_rate=configuration['learn_rate'],
momentum=configuration['momentum'],
decay=configuration['decay'],
nesterov=configuration['nesterov'],
regularizer_param=configuration['regularizer_param'],
hidden_neurons=configuration['hidden_dims'],
num_filters=configuration['num_filters'],
filter_sizes=configuration['filter_sizes'],
dropout_rate=configuration['dropout_prob'],
embedding_dim=configuration['embedding_dim'],
pool_size=configuration['pool_size'],
sequence_length=configuration['sequence_length']
)
print("Convolutional training")
# Get the data sources
training_generator = data_helpers.get_data_chunk(vocabulary,
configuration['labels_conv'])
validation_generator = data_helpers.get_data_chunk(vocabulary,
configuration['labels_conv'])
test_generator = data_helpers.get_data_chunk(vocabulary,
configuration['labels_conv'])
# Train and validate the model
history_object = cnn_w2v.train_model(training_source=training_generator,
validation_source=validation_generator,
samples_per_epoch\
=configuration['samples_per_epoch'],
number_epochs=configuration['num_epochs'],
val_split=configuration['val_split'])
# Test the model with new data
result = cnn_w2v.test_model(test_data_source=test_generator,
samples_to_test=configuration['samples_per_epoch'])
# Graphically plot the results (skip for the time being)
''' plot = Plot()