print("{0} malicious instances are being detected initially".format(
len(ls)))
return ls, [x.label for x in ls]
dataset = EmailDataset(path='./data_reader/data/raw/trec05p-1/test-400',
binary=False,
raw=True)
training_, testing_ = dataset.split({'train': 60, 'test': 40})
training_data = load_dataset(training_)
testing_data = load_dataset(testing_)
test_true_label = [x.label for x in testing_data]
#test simple learner svm
learning_model = svm.SVC(probability=True, kernel='linear')
learner1 = SimpleLearner(learning_model, training_data)
learner1.train()
predictions = learner1.predict(testing_data)
print("======== initial prediction =========")
print(summary(predictions, test_true_label))
#test Restrained_attack
attacker = CoordinateGreedy(lambda_val=0, max_change=100, step_size=1000)
attacker.set_adversarial_params(learner1, testing_data)
new_testing_data = attacker.attack(testing_data)
predictions2 = learner1.predict(new_testing_data)
print("========= post-attack prediction =========")
print(summary(predictions2, test_true_label))
def learner(data):
learning_model = svm.SVC(probability=True, kernel='linear')
learner = SimpleLearner(learning_model, data['training_data'])
learner.train()
return learner