def search(table_name):
if os.path.exists(databases_root_folder + table_name):
db = ScaDatabase(databases_root_folder + table_name)
analysis_all = db.select_all(Analysis)
analyses = []
hp = []
for analysis in analysis_all:
final_key_ranks = db.select_final_key_rank_json_from_analysis(KeyRank, analysis.id)
if len(final_key_ranks) > 0:
hyper_parameters = db.select_from_analysis(HyperParameter, analysis.id)
training_hyper_parameters = hyper_parameters.hyper_parameters
training_hyper_parameters[0]['guessing_entropy'] = final_key_ranks[0][0]['key_rank']
hp.append(training_hyper_parameters[0])
exp = hip.Experiment().from_iterable(hp)
exp.display_data(hip.Displays.PARALLEL_PLOT).update({
'hide': ['uid', 'key_rank', 'key'], # Hide some columns
'order': ['guessing_entropy'], # Put column time first on the left
})
exp.validate()
exp.to_html("webapp/templates/hiplot.html")
return render_template("dashboard/search.html", analyses=analyses)
return render_template("dashboard/search.html", analyses=[])
def table():
db_files = []
# r=root, d=directories, f = files
for r, d, f in os.walk(databases_root_folder):
for file in f:
if file.endswith(".sqlite"):
db_files.append(file)
all_tables = []
all_tables_names = []
for db_file in db_files:
if os.path.exists(databases_root_folder + db_file):
db = ScaDatabase(databases_root_folder + db_file)
analysis_all = db.select_all(Analysis)
analyses = []
for analysis in analysis_all:
if not analysis.deleted:
localtimezone = pytz.timezone(os.getenv("TIME_ZONE"))
analysis_datetime = datetime.strptime(str(analysis.datetime), "%Y-%m-%d %H:%M:%S.%f").astimezone(
localtimezone).__format__(
"%b %d, %Y %H:%M:%S")
final_key_ranks = db.select_final_key_rank_json_from_analysis(KeyRank, analysis.id)
final_success_rates = db.select_final_success_rate_from_analysis(SuccessRate, analysis.id)
neural_network = db.select_from_analysis(NeuralNetwork, analysis.id)
analyses.append({
"id": analysis.id,
"datetime": analysis_datetime,
"dataset": analysis.dataset,
"settings": analysis.settings,
"elapsed_time": time.strftime('%H:%M:%S', time.gmtime(analysis.elapsed_time)),
"key_ranks": final_key_ranks,
"success_rates": final_success_rates,
"neural_network_name": "not ready" if neural_network is None else neural_network.model_name
})
all_tables.append(analyses)
all_tables_names.append(db_file)
return render_template("tables.html", all_tables=all_tables, all_tables_names=all_tables_names)
def generate_script(script_filename, databases_root_folder, table_name,
analysis_id):
script_py_file = open(
"scripts/{}_{}.py".format(script_filename,
table_name.replace(".sqlite", "")), "w+")
script_py_file.write("from tensorflow.keras.optimizers import *\n")
script_py_file.write("from tensorflow.keras.layers import *\n")
script_py_file.write("from tensorflow.keras.models import *\n")
script_py_file.write("from aisy.sca_deep_learning_aes import AisyAes\n")
db = ScaDatabase(databases_root_folder + table_name)
analysis = db.select_analysis(Analysis, analysis_id)
neural_network_model = db.select_from_analysis(NeuralNetwork, analysis_id)
# get training hyper-parameters information from database
hyper_parameters = db.select_all_from_analysis(HyperParameter, analysis_id)
hyper_parameters_single = hyper_parameters[0].hyper_parameters[0]
hyper_parameter_search = None
if len(hyper_parameters) > 1:
hyper_parameter_search = db.select_from_analysis(
HyperParameterSearch, analysis.id)
leakage_models = db.select_from_analysis(LeakageModel, analysis_id)
leakage_model_parameters = leakage_models.leakage_model[0]
script_py_file.write('\naisy = AisyAes()')
script_py_file.write('\naisy.set_dataset("{}")'.format(analysis.dataset))
script_py_file.write('\naisy.set_database_name("{}")'.format(table_name))
script_py_file.write('\naisy.set_aes_leakage_model(')
for index, key in enumerate(leakage_model_parameters):
if isinstance(leakage_model_parameters[key], str):
script_py_file.write('{}="{}"'.format(
key, leakage_model_parameters[key]))
else:
script_py_file.write('{}={}'.format(key,
leakage_model_parameters[key]))
if index < len(leakage_model_parameters) - 1:
script_py_file.write(', ')
script_py_file.write(')')
script_py_file.write('\naisy.set_key("{}")'.format(
hyper_parameters_single['key']))
script_py_file.write('\naisy.set_number_of_profiling_traces({})'.format(
hyper_parameters_single['profiling_traces']))
script_py_file.write('\naisy.set_number_of_attack_traces({})'.format(
hyper_parameters_single['attack_traces']))
script_py_file.write('\naisy.set_first_sample({})'.format(
hyper_parameters_single['first_sample']))
script_py_file.write('\naisy.set_number_of_samples({})'.format(
hyper_parameters_single['number_of_samples']))
script_py_file.write('\naisy.set_batch_size({})'.format(
hyper_parameters_single['batch_size']))
script_py_file.write('\naisy.set_epochs({})'.format(
hyper_parameters_single['epochs']))
if len(hyper_parameters) == 1:
script_py_file.write('\n\n\n{}'.format(
neural_network_model.description))
script_py_file.write('\n\naisy.set_neural_network({})'.format(
neural_network_model.model_name))
else:
if "grid_search" in analysis.settings:
script_py_file.write('\ngrid_search = {}'.format(
analysis.settings["grid_search"]))
if "random_search" in analysis.settings:
script_py_file.write('\nrandom_search = {}'.format(
analysis.settings["random_search"]))
if "early_stopping" in analysis.settings:
script_py_file.write('\nearly_stopping = {}'.format(
analysis.settings["early_stopping"]))
script_py_file.write('\n\naisy.run(')
script_py_file.write('\n key_rank_executions={},'.format(
analysis.settings["key_rank_executions"]))
script_py_file.write('\n key_rank_report_interval={},'.format(
analysis.settings["key_rank_report_interval"]))
script_py_file.write('\n key_rank_attack_traces={},'.format(
analysis.settings["key_rank_attack_traces"]))
if "early_stopping" in analysis.settings:
script_py_file.write('\n early_stopping=early_stopping,')
if "ensemble" in analysis.settings:
script_py_file.write('\n ensemble=[{}],'.format(
analysis.settings["ensemble"]))
if len(hyper_parameters) == 1:
script_py_file.write('\n)\n')
else:
if hyper_parameter_search.search_type == "Grid Search":
script_py_file.write('\n grid_search=grid_search')
else:
script_py_file.write('\n random_search=random_search')
script_py_file.write('\n)\n')
script_py_file.close()
def result(analysis_id, table_name):
db = ScaDatabase(databases_root_folder + table_name)
# get neural network information from database
analysis = db.select_analysis(Analysis, analysis_id)
sca_views = ScaViews(analysis_id, db)
all_metric_plots = sca_views.metric_plots()
all_accuracy_plots = sca_views.accuracy_plots()
dash_app_accuracy.layout = html.Div(children=[all_accuracy_plots])
all_loss_plots = sca_views.loss_plots()
dash_app_loss.layout = html.Div(children=[all_loss_plots])
if "ensemble" in analysis.settings:
all_key_rank_plots = sca_views.ensemble_plots_key_rank()
else:
all_key_rank_plots = sca_views.key_rank_plots()
dash_app_key_ranks.layout = html.Div(children=[all_key_rank_plots])
if "ensemble" in analysis.settings:
all_success_rate_plots = sca_views.ensemble_plots_success_rate()
else:
all_success_rate_plots = sca_views.success_rate_plots()
dash_app_success_rates.layout = html.Div(children=[all_success_rate_plots])
# get neural network information from database
neural_network_model = db.select_from_analysis(NeuralNetwork, analysis_id)
# get training hyper-parameters information from database
hyper_parameters = db.select_all_from_analysis(HyperParameter, analysis_id)
training_hyper_parameters = []
for hp in hyper_parameters:
training_hyper_parameters.append(hp.hyper_parameters)
hyper_parameter_search = []
if len(hyper_parameters) > 1:
hyper_parameter_search = db.select_from_analysis(HyperParameterSearch, analysis.id)
# get leakage model information from database
leakage_models = db.select_from_analysis(LeakageModel, analysis_id)
leakage_model_parameters = leakage_models.leakage_model
# get visualization plots
all_visualization_plots = sca_views.visualization_plots()
all_visualization_heatmap_plots = sca_views.visualization_plots_heatmap()
# confusion matrix plot
all_confusion_matrix_plots = sca_views.confusion_matrix_plots()
return render_template("dashboard/result.html",
all_plots=all_metric_plots,
all_key_rank_plots=all_key_rank_plots,
all_success_rate_plots=all_success_rate_plots,
neural_network_description=neural_network_model.description,
training_hyper_parameters=training_hyper_parameters,
hyper_parameters=hyper_parameters,
hyper_parameter_search=hyper_parameter_search,
leakage_model_parameters=leakage_model_parameters,
all_visualization_plots=all_visualization_plots,
all_visualization_heatmap_plots=all_visualization_heatmap_plots,
all_confusion_matrix_plots=all_confusion_matrix_plots,
analysis=analysis)