314 lines
11 KiB
Python
314 lines
11 KiB
Python
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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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from scipy.special import expit
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from numpy import dot
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import sklearn
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from sklearn.datasets import load_svmlight_file
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import os
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import sys
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import string
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from decimal import *
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import collections
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from classifiers import *
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import time
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import random
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import pickle as pkl
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import argparse
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import shutil
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class app(object):
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def __init__(self, a, y, pl):
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self.a = a
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self.y = y
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self.pl = pl
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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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print 'evaluating predictions'
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for e in xrange(len(Y_test)):
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if Y_test[e] != 1 and instances[e].pl != 1: # true label, prediction label
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n += 1
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tn += 1
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if Y_test[e] != 1 and instances[e].pl == 1:
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n += 1
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fp +=1
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if Y_test[e] == 1 and instances[e].pl == 1:
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p += 1
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tp += 1
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if Y_test[e] == 1 and instances[e].pl != 1:
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p += 1
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fn += 1
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if Y_test[e] == instances[e].pl:
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right += 1
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print type(Y_test), len(Y_test)
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print 'all', n+p, 'right', right ,'n', n , 'p:', p, 'tn', tn, 'tp',tp, 'fn',fn, 'fp',fp
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accu = (Decimal(tp) + Decimal(tn))*Decimal(100) / (Decimal(n) + Decimal(p))
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tpr = Decimal(tp)*Decimal(100)/Decimal(p)
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fpr = Decimal(fp)*Decimal(100)/Decimal(n)
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f1 = Decimal(200)*Decimal(tp)/(Decimal(2)*Decimal(tp) + Decimal(fp) + Decimal(fn))
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precision = Decimal(tp)*Decimal(100)/(Decimal(tp) + Decimal(fp))
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print 'model pool f measure: ', float(format(f1, '.2f')), 'precision: ', float(format(precision, '.2f')), 'recall: ', float(format(tpr, '.2f'))
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return float(format(accu, '.2f')), float(format(f1, '.2f')), float(format(precision, '.2f')), float(format(tpr, '.2f')), float(format(fpr, '.2f'))
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def metric_calculation(i, j, buffer_size):
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larger = 0
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if len(app_buffer) <=buffer_size:
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app_temp = [item[j] for item in app_buffer]
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positive = sum(app_tt > 0 for app_tt in app_temp)
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negative = sum(app_tt <= 0 for app_tt in app_temp)
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if confidences[i][j] > 0: # prediction label = 1 = malicious
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larger = sum(confidences[i][j] >= app_t and app_t > 0 for app_t in app_temp)
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p_ratio = float(Decimal(larger)/Decimal(positive))
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else: # <= 0 = benign
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larger = sum(confidences[i][j] <= app_t and app_t <= 0 for app_t in app_temp)
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p_ratio = float(Decimal(larger)/Decimal(negative))
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else:
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app_temp = [item[j] for item in app_buffer[len(app_buffer)-buffer_size:]]
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positive = sum(app_tt > 0 for app_tt in app_temp)
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negative = sum(app_tt <= 0 for app_tt in app_temp)
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if confidences[i][j] > 0: # prediction label = 1 = malicious
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larger = sum(confidences[i][j] >= app_t and app_t > 0 for app_t in app_temp)
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p_ratio = float(Decimal(larger)/Decimal(positive))
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else:
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larger = sum(confidences[i][j] <= app_t and app_t <= 0 for app_t in app_temp)
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p_ratio = float(Decimal(larger)/Decimal(negative))
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return p_ratio
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def all_model_label(i, age_threshold_low, age_threshold_up):
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young = aged = a_marker = y_marker = 0
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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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else: # this is an aged model, need to be updated
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aged += confidences[i][j]
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aged_model.append(j) # record aged model index
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a_marker += 1 # num of aged model for this drifting app
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return young, aged, a_marker, y_marker
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def generate_pseudo_label(aged_marker, young_marker, aged_value, young_value):
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if young_marker == 0: # young models are not available; weighted voting using aged model
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if aged_value > 0:
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temp = app(aged_marker, young_marker, 1.)
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else:
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temp = app(aged_marker, young_marker, -1.)
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fail += 1
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else: # young models are available; weighted voting using young model
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if young_value > 0:
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temp = app(aged_marker, young_marker, 1.)
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else:
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temp = app(aged_marker, aged_marker, -1.)
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instances.append(temp)
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def save_model(current_year, checkpoint_dir):
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for m in xrange(len(clfs)):
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print m, clfs[m]
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clfs[m].save( checkpoint_dir + str(current_year) + '_' + str(m) + '.model')
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def main():
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# set argument for past year and current year
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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('--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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args = parser.parse_args()
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buffer_size = args.buffer
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age_threshold_low = args.low
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age_threshold_up = args.high
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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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checkpoint_dir = whole_directory + current_directory
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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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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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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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clfs[i].load( checkpoint_dir + str(args.past) + '_' + str(i) + '.model')
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# get original model weight
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w = clfs[i].coef_[1:]
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weight = [] # [i][j]: i = model index, j = feature index
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for w_num in xrange(len(w)):
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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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print c, len(weights[c])
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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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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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train_size, _ = X_train.shape
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random_app_index = np.random.randint(train_size, size = buffer_size)
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X_train_temp = X_train[random_app_index, :]
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for i in xrange(buffer_size):
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app_buffer_temp = []
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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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print 'Load app buffer from ', args.past, '_buffer.pkl'
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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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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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pre, conf, new_conf, app_b, p_value = ([] for i 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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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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new_conf.append(abs(score[0,0]))
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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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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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global aged_model
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aged_model = [] # store the index of aged model for current app i
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young_value = aged_value = aged_marker = young_marker = 0
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young_value, aged_value, aged_marker, young_marker = all_model_label(i, age_threshold_low, age_threshold_up)
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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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# 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 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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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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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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pool_fscore.append(f)
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pool_precision.append(preci)
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pool_tpr.append(tprr)
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pool_fnr.append(100-tprr)
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pool_fpr.append(fprr)
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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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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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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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