Update classification_evolvement.py
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DroidEvolver
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@ -1,3 +1,9 @@
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'''
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Use the model pool initialized with 2011 apps to detect malware from apps developed in 2012, 2013, 2014, 2015, 2016
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Model pool and feature set (i.e., feature_set.pkl) are evolved during detection.
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'''
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import numpy as np
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import scipy
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from scipy.stats import logistic
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@ -23,6 +29,56 @@ class app(object):
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self.y = y
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self.pl = pl
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def extract_benign(filedir):
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app_feature = pkl.load(open(filedir + '.feature','rb'))
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result = []
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result.append('-1 ')
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new = []
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for i in range(len(features)):
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if features[i] in app_feature:
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result.append(str(i+1) + ':1 ')
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for item in app_feature:
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if item not in features: # this is a new feature, store new features in advance to save time
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p = 1
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# append the new feature to the data
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# the model won't process this new feature unless update
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# the model will only process the first |len(features)| features
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result.append(str(len(features) + p) + ':1 ')
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new.append(item)
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p += 1
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return result, new
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def extract_malicious(filedir):
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app_feature = pkl.load(open(filedir + '.feature','rb'))
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result = []
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result.append('1 ')
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new = []
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for i in range(len(features)):
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if features[i] in app_feature:
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result.append(str(i+1) + ':1 ')
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for item in app_feature:
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if item not in features: # this is a new feature
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p = 1
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# append the new feature to the data
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# the model won't process this new feature unless update
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# the model will only process the first |len(features)| features
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# if this app is a drifting app, the new identified feature will be added into feature_set.pkl
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result.append(str(len(features) + p) + ':1 ')
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new.append(item)
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p += 1
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return result, new
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def evaluation(Y_test, instances):
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n = p = tp = fn = tn = fp = right = 0
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@ -90,7 +146,7 @@ def all_model_label(i, age_threshold_low, age_threshold_up):
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for j in xrange(len(clfs)):
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if age_threshold_low <= p_values[i][j] <= age_threshold_up: # not an aged model, can vote
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young += confidences[i][j]
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y_marker += 1
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y_marker += 1 # number of young model
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else: # this is an aged model, need to be updated
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aged += confidences[i][j]
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@ -127,7 +183,7 @@ def main():
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parser = argparse.ArgumentParser()
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parser.add_argument('--past', type=int, help='past year')
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parser.add_argument('--current', type=int, help='current year')
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parser.add_argument('--starting', type=int, help='starting year')
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parser.add_argument('--starting', type=int, help='starting year') # initialization year = 2011
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parser.add_argument('--low', type=float, help='low threshold value')
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parser.add_argument('--high', type=float, help='high threshold value')
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parser.add_argument('--buffer', type=int, help = 'buffer size value')
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@ -139,6 +195,8 @@ def main():
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age_threshold_up = args.high
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global features
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features = pkl.load(open('feature_set.pkl','rb'))
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whole_directory = './'+ str(args.starting) + 'train/'
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current_directory = str(age_threshold_low) + '_' + str(age_threshold_up) + '_' + str(buffer_size) + '/'
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@ -146,39 +204,22 @@ def main():
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if not os.path.exists(checkpoint_dir):
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os.makedirs(checkpoint_dir)
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# record all evolving result to a file
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Result = open(whole_directory + str(args.starting) + '.evolving_result','a')
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evolving_result = []
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evolving_result.append(str(args.past) + ' ' + str(args.current) + ' ' + str(age_threshold_low) + ' ' + str(age_threshold_up) + ' ' + str(buffer_size) + ' ' + str(model_number) + ' ')
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print 'Loading data from ', args.past # old dataset
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X_train,Y_train=load_svmlight_file( str(args.past))
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print 'X_train data shape' , type(X_train), X_train.shape
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print 'Loading test data from ', args.current # new dataset
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X_test,Y_test=load_svmlight_file(str(args.current))
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X_testt,Y_testt=load_svmlight_file( str(args.current))
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print 'X_test data shape', type(X_test), X_test.shape
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xtest_dense = scipy.sparse.csr_matrix(X_testt).todense()
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print 'X_test', xtest_dense.shape
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global clfs
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clfs = [PA1(), OGD(), AROW(), RDA(), ADA_FOBOS()]
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print 'model pool size: ', len(clfs)
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ori_train_acc, ori_test_acc, weights, pool_acc, pool_fscore, pool_precision, pool_tpr, pool_fnr, pool_fpr, pool_difference = ([] for i in range(10))
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ori_train_acc, ori_test_acc, weights, pool_acc, pool_fscore, pool_precision, pool_tpr, pool_fnr, pool_fpr, pool_difference = ([] for list_number in range(10))
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print 'Loading trained model from ', args.past
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if args.starting == args.past: # copy the original detection model into checkpoint_dir
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if args.starting == args.past: # copy the initial detection model into checkpoint_dir
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for i in xrange(len(clfs)):
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shutil.copy2( whole_directory + str(args.past) + '_' + str(i) + '.model' , checkpoint_dir )
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for i in xrange(len(clfs)):
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for i in xrange(len(clfs)): # for each model in the model pool
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clfs[i].load( checkpoint_dir + str(args.past) + '_' + str(i) + '.model')
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# get original model weight
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@ -189,8 +230,6 @@ def main():
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weight.append(w[w_num])
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weights.append(weight)
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test_accuracy,auc,tpr_fig,fpr_fig=clfs[i].score(X_test,Y_test,False)
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ori_test_acc.append(test_accuracy)
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print 'original weight size'
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for c in xrange(len(weights)):
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@ -198,10 +237,14 @@ def main():
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print 'App buffer generation'
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global app_buffer
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app_buffer = []
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app_buffer = []
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if '2011' in str(args.past): # buffer is not exist
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print 'App buffer not exists'
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print 'App buffer initialization'
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print 'Loading data from ', args.past, ' to initialize app buffer ...' # load the 2011 data to initialized app buffer
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X_train,Y_train=load_svmlight_file( str(args.past) + '.libsvm')
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train_size, _ = X_train.shape
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random_app_index = np.random.randint(train_size, size = buffer_size)
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@ -212,6 +255,7 @@ def main():
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for j in xrange(len(clfs)):
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app_buffer_temp.append(clfs[j].decision_function(X_train_temp[i])[0])
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app_buffer.append(app_buffer_temp)
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else: # load buffer from str(args.past).buffer
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print 'App buffer exists'
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app_buffer = pkl.load(open( checkpoint_dir + str(args.past) + '_buffer.pkl', 'rb'))
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@ -219,18 +263,54 @@ def main():
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print 'Start evolving'
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global confidences, new_confidences, p_values, instances, model_credits, model_confidences
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confidences, new_confidences, p_values, instances, model_credits, model_confidences = ([] for i in range(6))
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confidences, new_confidences, p_values, instances, model_credits, model_confidences = ([] for list_number in range(6))
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all_fail = 0 # a special case, all model are aged
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num_of_update = num_of_update_model = 0
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wrong_update = 0
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wrong_update_benign = wrong_update_malicious = right_update_benign = right_update_malicious = 0
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for i in xrange(len(Y_test)): # i = every app
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Y_test = [] # save ground truth of test data ; for final evaluation only
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pre, conf, new_conf, app_b, p_value = ([] for i in range(5))
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names = ['---list of test app names -----'] # names of apps developed in the current_year, e.g., names of apps developed in 2012
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for i in xrange(len(names)):
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# generate test data
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app_name = names[i] # for each test app
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# according to the ground truth to get the true label
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# the true label is for evaluation only, won't be processed by the model
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data = []
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if 'malicious' in app_name:
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d, new_feature = extract_malicious(app_name)
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data.append(d)
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else:
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d, new_feature = extract_benign(app_name)
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data.append(d)
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# skip if do not need to save test data
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save_data = open(app_name + '.libsvm', 'w')
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for item in data:
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save_data.writelines(item)
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save_data.writelines('\n')
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data_file.close()
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X_test, y_t=load_svmlight_file(app_name + '.libsvm')
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X_testt,y_testt=load_svmlight_file(app_name + '.libsvm')
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Y_test.append(y_t)
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print 'X_test data shape', type(X_test), X_test.shape
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xtest_dense = scipy.sparse.csr_matrix(X_testt).todense()
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print 'X_test', xtest_dense.shape
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# calculate JI value
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pre, conf, new_conf, app_b, p_value = ([] for list_number in range(5))
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for j in xrange(len(clfs)):
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xtest_current = xtest_dense[i, :len(weights[j])]
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xtest_current = xtest_dense[ ,:len(weights[j])]
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score = xtest_current.dot(weights[j])
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conf.append(score[0,0])
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app_b.append(score[0,0])
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@ -238,13 +318,13 @@ def main():
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confidences.append(conf)
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new_confidences.append(new_conf)
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app_buffer[random.randint(0, buffer_size-1)] = app_b # randomly replace a processed app with new app
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app_buffer[random.randint(0, buffer_size-1)] = app_b # randomly replace a processed app with the new app
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for j in xrange(len(clfs)):
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pv = metric_calculation(i, j, buffer_size)
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p_value.append(pv)
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p_values.append(p_value)
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p_values.append(p_value)
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global aged_model
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@ -255,26 +335,25 @@ def main():
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# generate pseudo label
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generate_pseudo_label(aged_marker, young_marker, aged_value, young_value)
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# drifting app is identified and young model exists
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if (aged_marker != 0) and (young_marker >= 1):
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# update aged models
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if (aged_marker != 0) and (young_marker >= 1): # drifting app is identified and young model exists
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update_label = np.array([instances[i].pl])
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update_with_pseudo_label += 1
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for model_index in aged_model: # update clfs[a] with X_test[i], temp.pl; a is the aged model index
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num_of_update_model += 1
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update_label = np.array([instances[i].pl]) # update label = pseudo label of the drifting app
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# update aged models
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for model_index in aged_model: # update clfs[a] with X_test, update_label; a is the aged model index
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# update with drifting app and corresponding pseudo label
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train_accuracy,data,err,fit_time=clfs[model_index].fit(X_test[i],update_label, False)
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train_accuracy,data,err,fit_time=clfs[model_index].fit(X_test,update_label, False)
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w = clfs[model_index].coef_[1:]
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updated_w = []
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for w_num in xrange(len(w)):
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updated_w.append(w[w_num])
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weights[model_index] = updated_w
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weights[model_index] = updated_w # update weight matrix in the weight matrix list for the next new app
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# updat feature set
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for new_identified_feature in new_feature:
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features.append(new_identified_feature)
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print 'update with pseudo label ', update_with_pseudo_label
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a, f, preci, tprr, fprr = evaluation(Y_test, instances)
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pool_acc.append(a)
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@ -286,29 +365,30 @@ def main():
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print buffer_size, len(app_buffer)
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print 'original test accuracy',ori_test_acc # without evolving
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print 'pool accuracy', pool_acc
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print 'pool fscore', pool_fscore
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print 'pool precision', pool_precision
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print 'pool tpr', pool_tpr
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print 'pool fnr', pool_fnr
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print 'pool fpr', pool_fpr
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print 'pool_fnr - pool_fpr', pool_difference
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print 'evolved weight length'
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for c in xrange(len(weights)):
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print c, len(weights[c])
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# save evolved model for each year
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print 'Save model evolved in Year ', args.current, 'into directory /', checkpoint_dir
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current_year = args.current
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save_model(current_year, checkpoint_dir)
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# save feature set
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with open('feature_set.pkl','wb') as feature_result:
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pkl.dump(features, feature_result)
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print 'Save app buffer evolved in Year', args.current
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pkl.dump(app_buffer, open( checkpoint_dir + str(args.current) + '_buffer.pkl', 'wb'))
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evolving_result.append(str(pool_acc) + ' ' + str(pool_fscore) + ' ' + str(pool_precision) + ' ' + str(pool_tpr) + ' ' + str(pool_fnr))
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Result.writelines(evolving_result)
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Result.writelines('\n')
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if __name__ == "__main__":
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main()
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main()
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