412 lines
17 KiB
Python
412 lines
17 KiB
Python
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# -*- coding: utf-8 -*-
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from keras.models import Sequential
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from keras.layers import Dense, Activation,LSTM,Embedding,Dropout,Conv1D, GlobalMaxPooling1D, Merge, Reshape, Lambda
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from keras.layers import Convolution1D
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import ciscoProcessing as ciscoProcessing
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import numpy as np
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import matplotlib.pyplot as plt
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import pandas as pd
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import joblib
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import csv
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import keras
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from keras.layers import Input
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from keras.models import Model
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from keras.utils import np_utils
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from sklearn.metrics import precision_recall_curve
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from sklearn.metrics import auc, roc_curve
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from tqdm import tqdm
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import os
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def getCiscoFeatures(curDataLine,urlSIPDict):
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numCiscoFeatures = 30
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try:
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ciscoFeatures = urlSIPDict[str(curDataLine['domain']) + str(curDataLine['server_ip'])]
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#print('cisco features: ' + str(ciscoFeatures))
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# log transform
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ciscoFeatures = np.log1p(ciscoFeatures,dtype='float32')
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#print('log transformed: ' + str(ciscoFeatures))
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return ciscoFeatures.ravel()
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except:
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return np.zeros([numCiscoFeatures,]).ravel()
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def getCNNWithoutLastLayer(vocabSize,embeddingSize,input_length,filters,kernel_size,
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hidden_dims,drop_out):
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model = Sequential()
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model.add(Embedding(input_dim=vocabSize, output_dim=embeddingSize,
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input_length=input_length))
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model.add(Conv1D(filters,
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kernel_size,
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activation='relu'))
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# we use max pooling:
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model.add(GlobalMaxPooling1D())
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# We add a vanilla hidden layer:
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model.add(Dense(hidden_dims))
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model.add(Dropout(drop_out))
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model.add(Activation('relu'))
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return model
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def getCNNWitoutLastLayerFunctional(vocabSize,embeddingSize,input_length,filters,kernel_size,
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hidden_dims,drop_out):
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a = Input(shape=(input_length,))
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embedding = Embedding(input_dim=vocabSize,output_dim=embeddingSize)(a)
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conv1 = Conv1D(filters,kernel_size,activation='relu')(embedding)
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glob = GlobalMaxPooling1D()(conv1)
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dense = Dense(hidden_dims)(glob)
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drop = Dropout(drop_out)(dense)
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model = Activation('relu')(drop)
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model = Model(a, model)
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return model
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def getFlowFeatureLayer(numFeatures):
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model = Sequential()
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#slpModel.add(Dense(1, input_shape=(1,)))
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model.add(Lambda(lambda x: x + 0.0, input_shape=(numFeatures,)))
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return model
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def createCNNDataSet(domains,label,characterDict,maxLen=40):
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# process domains in reverse order
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outFeature = np.zeros([len(domains),maxLen])
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outLabel = np.zeros([len(domains),])
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for i in range(len(domains)):
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domain = domains[i]
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curLabel = label[i]
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curFeature = np.zeros([maxLen,])
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# print(domain + ' ' + str(len(domain)))
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for j in range(np.min([len(domain),maxLen])):
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#print(j)
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curCharacter = domain[-j]
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if curCharacter in characterDict:
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curFeature[j] = characterDict[curCharacter]
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outFeature[i] = curFeature
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outLabel[i] = curLabel
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return (outFeature,outLabel)
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def getFeatureVecForDomain(domain,characterDict,maxLen=40):
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curFeature = np.zeros([maxLen,])
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for j in range(np.min([len(domain),maxLen])):
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#print(j)
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curCharacter = domain[-j]
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if curCharacter in characterDict:
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curFeature[j] = characterDict[curCharacter]
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return curFeature
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def getFlowFeatures(curDataLine):
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useKeys = ['duration','bytes_down','bytes_up']
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curFeature = np.zeros([len(useKeys),])
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for i in range(len(useKeys)):
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curKey = useKeys[i]
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try:
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curFeature[i] = np.log1p(curDataLine[curKey],dtype='float32')
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except:
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pass
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return curFeature
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def getChunksFromUserDataFrame(dataFrame,windowSize=10,overlapping=False,
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maxLengthInSeconds=300):
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#print('maxLength: ' + str(maxLengthInSeconds))
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maxMilliSeconds = maxLengthInSeconds * 1000
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outDomainLists = []
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outDFFrames = []
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if overlapping == False:
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numBlocks = int(np.ceil(float(len(dataFrame)) / float(windowSize)))
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userIDs = np.arange(len(dataFrame))
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for blockID in np.arange(numBlocks):
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curIDs = userIDs[(blockID * windowSize):((blockID+1)*windowSize)]
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#print(curIDs)
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useData = dataFrame.iloc[curIDs]
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curDomains = useData['domain']
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if maxLengthInSeconds != -1:
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curMinMilliSeconds = np.min(useData['timeStamp']) + maxMilliSeconds
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underTimeOutIDs = np.where(np.array(useData['timeStamp']) <= curMinMilliSeconds)
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if len(underTimeOutIDs) != len(curIDs):
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curIDs = curIDs[underTimeOutIDs]
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useData = dataFrame.iloc[curIDs]
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curDomains = useData['domain']
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outDomainLists.append(list(curDomains))
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outDFFrames.append(useData)
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else:
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numBlocks = len(dataFrame) + 1 - windowSize
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userIDs = np.arange(len(dataFrame))
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for blockID in np.arange(numBlocks):
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curIDs = userIDs[blockID:blockID+windowSize]
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#print(curIDs)
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useData = dataFrame.iloc[curIDs]
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curDomains = useData['domain']
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if maxLengthInSeconds != -1:
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curMinMilliSeconds = np.min(useData['timeStamp']) + maxMilliSeconds
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underTimeOutIDs = np.where(np.array(useData['timeStamp']) <= curMinMilliSeconds)
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if len(underTimeOutIDs) != len(curIDs):
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curIDs = curIDs[underTimeOutIDs]
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useData = dataFrame.iloc[curIDs]
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curDomains = useData['domain']
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outDomainLists.append(list(curDomains))
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outDFFrames.append(useData)
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return (outDomainLists,outDFFrames)
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def createTrainData(domainLists,dfLists,charachterDict,maxLen,threshold = 3,
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flagUseCiscoFeatures=False,urlSIPDIct=dict,
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windowSize=10):
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if 'hits' in dfLists[0].keys():
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hitName = 'hits'
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elif 'virusTotalHits' in dfLists[0].keys():
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hitName = 'virusTotalHits'
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numFlowFeatures = 3
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numCiscoFeatures = 30
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numFeatures = numFlowFeatures
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if flagUseCiscoFeatures:
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numFeatures += numCiscoFeatures
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outputFeatures = []
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label = []
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hits = []
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trainNames = []
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for i in range(windowSize):
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outputFeatures.append(np.zeros([len(domainLists),maxLen]))
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outputFeatures.append(np.zeros([len(domainLists),numFeatures]))
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for i in tqdm(np.arange(len(domainLists)), miniters=10):
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curCounter = 0
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#print('len domainList: ' + str(len(domainLists[i])))
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#print('len df: ' + str(len(dfLists[i])))
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for j in range(np.min([windowSize,len(domainLists[i])])):
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outputFeatures[curCounter][i,:] = getFeatureVecForDomain(domainLists[i][j],charachterDict,maxLen)
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curCounter += 1
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if flagUseCiscoFeatures:
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outputFeatures[curCounter][i,0:numFlowFeatures] = getFlowFeatures(dfLists[i].iloc[j])
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outputFeatures[curCounter][i,numFlowFeatures:] = getCiscoFeatures(dfLists[i].iloc[j],urlSIPDIct)
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else:
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outputFeatures[curCounter][i,:] = getFlowFeatures(dfLists[i].iloc[j])
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curCounter += 1
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curLabel = 0.0
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if np.max(dfLists[i][hitName]) >= threshold:
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curLabel = 1.0
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elif np.max(dfLists[i][hitName]) == -1:
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curLabel = -1.0
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elif np.max(dfLists[i][hitName]) > 0 and np.max(dfLists[i][hitName]) < threshold:
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curLabel = -2.0
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label.append(curLabel)
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hits.append(np.max(dfLists[i][hitName]))
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trainNames.append(np.unique(dfLists[i]['user_hash']))
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return (outputFeatures, np.array(label), np.array(hits), np.array(trainNames))
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def transformStringListToNumpyArray(listString):
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listString = listString.replace('[','').replace(']','')
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return np.array(listString.split(','),dtype='float32')
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def getCiscoFeatureDict(csvPathList):
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outDict = dict()
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for path in tqdm(csvPathList, miniters=1):
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fobj = open(path,'r')
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csvReader = csv.DictReader(fobj,delimiter=',')
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for row in csvReader:
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urlSIPString = row['Domain'] + row['ServerIP']
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ciscoFeatures = row['CiscoFeature']
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outDict[urlSIPString] = transformStringListToNumpyArray(ciscoFeatures)
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#if len(outDict) % 10000 == 0:
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# print('numbers in dict: ' + str(len(outDict)))
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return outDict
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if __name__ == "__main__":
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# get data
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trainDirsUserLevel = ['trainData/joblib2016-07-annomalous-stg-new/10/',
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'trainData/joblib2016-07-annomalous-stg-new/09/',
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'trainData/joblib2016-07-annomalous-stg-new/08/',
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'trainData/joblib2016-07-annomalous-stg-new/07/',
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'trainData/joblib2016-07-annomalous-stg-new/06/']
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testDirsUserLevel = ['trainData/joblib2016-09-annomalous-stg-new/07/',\
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'trainData/joblib2016-09-annomalous-stg-new/08/',\
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'trainData/joblib2016-09-annomalous-stg-new/09/',\
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'trainData/joblib2016-09-annomalous-stg-new/10/',\
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'trainData/joblib2016-09-annomalous-stg-new/11/',\
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'trainData/joblib2016-09-annomalous-stg-new/12/',\
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'trainData/joblib2016-09-annomalous-stg-new/13/',\
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'trainData/joblib2016-09-annomalous-stg-new/14/']
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trainCiscoFeatureCSVPaths = ['trainData/ciscoDomainFeatueres_joblib2016-07-annomalous-stg-new_07.csv',
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'trainData/ciscoDomainFeatueres_joblib2016-07-annomalous-stg-new_06.csv',
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'trainData/ciscoDomainFeatueres_joblib2016-07-annomalous-stg-new_08.csv',
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'trainData/ciscoDomainFeatueres_joblib2016-07-annomalous-stg-new_10.csv',
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'trainData/ciscoDomainFeatueres_joblib2016-07-annomalous-stg-new_09.csv']
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testCiscoFeatureCSVPaths = ['trainData/ciscoDomainFeatueres_joblib2016-09-annomalous-stg-new_12.csv',
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'trainData/ciscoDomainFeatueres_joblib2016-09-annomalous-stg-new_08.csv',
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'trainData/ciscoDomainFeatueres_joblib2016-09-annomalous-stg-new_07.csv',
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'trainData/ciscoDomainFeatueres_joblib2016-09-annomalous-stg-new_09.csv',
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'trainData/ciscoDomainFeatueres_joblib2016-09-annomalous-stg-new_13.csv',
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'trainData/ciscoDomainFeatueres_joblib2016-09-annomalous-stg-new_14.csv',
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'trainData/ciscoDomainFeatueres_joblib2016-09-annomalous-stg-new_10.csv',
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'trainData/ciscoDomainFeatueres_joblib2016-09-annomalous-stg-new_11.csv']
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# parameter
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numNegPerDay = 5000
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numEpochs = 10
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domainFeatures = 512
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flowFeatures = 3
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numCiscoFeatures= 30
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windowSize = 10
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maxLen = 40
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lstmUnits = 32
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lstmDenseSize = 128
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embeddingSize = 100
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kernel_size = 2
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drop_out = 0.5
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filters = 2
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hidden_dims = 100
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vocabSize = 40
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flagUseCiscoFeatures = True
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threshold = 3
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resultStoreDir = 'results/201705/'
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if flagUseCiscoFeatures:
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resultStorePath = resultStoreDir + 'cnn_plus_cisco_plus_lstm_numNegPerDay' + str(numNegPerDay) + '.joblib'
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resultModelPath = resultStoreDir + 'cnn_plus_cisco_plus_lstm_numNegPerDay' + str(numNegPerDay)
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else:
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resultStorePath = resultStoreDir + 'cnn_plus_lstm_numNegPerDay' + str(numNegPerDay) + '.joblib'
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resultModelPath = resultStoreDir + 'cnn_plus_lstm_numNegPerDay' + str(numNegPerDay)
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flagRedo = True
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if flagUseCiscoFeatures:
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if 'trainCiscoFeatureDict' not in locals():
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trainCiscoFeatureDict = getCiscoFeatureDict(trainCiscoFeatureCSVPaths)
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if 'testCiscoFeatureDict' not in locals():
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testCiscoFeatureDict = getCiscoFeatureDict(testCiscoFeatureCSVPaths)
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else:
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trainCiscoFeatureDict = dict()
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testCiscoFeatureDict = dict()
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if flagRedo or not os.path.exists(resultStorePath):
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if 'characterDict' not in locals():
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characterDictPath = 'trainData/characterIDDict.joblib'
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characterDict = joblib.load(characterDictPath)['characterIDDict']
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print('create train data')
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if 'dataFrameList' not in locals():
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(dataFrameList) = ciscoProcessing.loadRawDataSetFromJoblibPerUser(\
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trainDirsUserLevel,numNegPerDay = numNegPerDay)
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maxHits = []
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for i in range(len(dataFrameList)):
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maxHits.append(np.max(dataFrameList[i]['hits']))
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print('create test data')
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# validation error
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if 'testDataFrameList' not in locals():
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(testDataFrameList) = ciscoProcessing.loadRawDataSetFromJoblibPerUser(\
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[testDirsUserLevel[0]],numNegPerDay = numNegPerDay)
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maxHits = []
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for i in range(len(testDataFrameList)):
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maxHits.append(np.max(testDataFrameList[i]['hits']))
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sharedCNNFun = getCNNWitoutLastLayerFunctional(len(characterDict)+1,embeddingSize,maxLen,domainFeatures,kernel_size,domainFeatures,0.5)
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inputList = []
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encodedList = []
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numFeatures = flowFeatures
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if flagUseCiscoFeatures:
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numFeatures += numCiscoFeatures
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for i in range(windowSize):
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inputList.append(Input(shape=(maxLen,)))
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encodedList.append(sharedCNNFun(inputList[-1])) # add shared domain model
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inputList.append(Input(shape=(numFeatures,)))
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merge_layer_input = []
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for i in range(windowSize):
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merge_layer_input.append(encodedList[i])
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merge_layer_input.append(inputList[(2*i)+1])
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# We can then concatenate the two vectors:
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merged_vector = keras.layers.concatenate(merge_layer_input, axis=-1)
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reshape = Reshape((windowSize, domainFeatures+numFeatures))(merged_vector)
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lstm = LSTM(lstmUnits, input_shape=(windowSize,domainFeatures+numFeatures))(reshape)
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dense = Dense(lstmDenseSize, activation='relu')(lstm)
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dropout = Dropout(0.5)(dense)
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# And add a logistic regression on top
|
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predictions = Dense(2, activation='softmax')(dropout)
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# We define a trainable model linking the
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# tweet inputs to the predictions
|
||
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model = Model(inputs=inputList, outputs=predictions)
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|
||
|
|
model.compile(optimizer='adam',
|
||
|
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loss='binary_crossentropy',
|
||
|
|
metrics=['accuracy'])
|
||
|
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|
||
|
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|
||
|
|
# get train data
|
||
|
|
domainLists = []
|
||
|
|
dfLists = []
|
||
|
|
for i in tqdm(np.arange(len(dataFrameList)), miniters=10):
|
||
|
|
(domainListsTmp,dfListsTmp) = getChunksFromUserDataFrame(dataFrameList[i],windowSize=windowSize,overlapping=False)
|
||
|
|
domainLists += domainListsTmp
|
||
|
|
dfLists += dfListsTmp
|
||
|
|
|
||
|
|
(trainData,trainLabel,trainHits,trainNames) = createTrainData(domainLists,dfLists,characterDict,
|
||
|
|
maxLen,threshold = threshold,
|
||
|
|
flagUseCiscoFeatures=flagUseCiscoFeatures,urlSIPDIct=trainCiscoFeatureDict)
|
||
|
|
useIDs = np.where(trainHits == 0)[0]
|
||
|
|
useIDs = np.concatenate([useIDs,np.where(trainHits >= threshold)[0]])
|
||
|
|
for i in range(len(trainData)):
|
||
|
|
trainData[i] = np.array(trainData[i])[useIDs]
|
||
|
|
trainLabel = trainLabel[useIDs]
|
||
|
|
trainHits = trainHits[useIDs]
|
||
|
|
trainNames = trainNames[useIDs]
|
||
|
|
|
||
|
|
# get test data
|
||
|
|
domainLists = []
|
||
|
|
dfLists = []
|
||
|
|
for i in tqdm(np.arange(len(testDataFrameList)), miniters=10):
|
||
|
|
(domainListsTmp,dfListsTmp) = getChunksFromUserDataFrame(testDataFrameList[i],windowSize=windowSize,overlapping=False)
|
||
|
|
domainLists += domainListsTmp
|
||
|
|
dfLists += dfListsTmp
|
||
|
|
|
||
|
|
(testData,testLabel,testHits,testNames) = createTrainData(domainLists,dfLists,characterDict,
|
||
|
|
maxLen,threshold = threshold,
|
||
|
|
flagUseCiscoFeatures=flagUseCiscoFeatures,urlSIPDIct=testCiscoFeatureDict)
|
||
|
|
useIDs = np.where(testHits == 0)[0]
|
||
|
|
useIDs = np.concatenate([useIDs,np.where(testHits >= threshold)[0]])
|
||
|
|
for i in range(len(testData)):
|
||
|
|
testData[i] = np.array(testData[i])[useIDs]
|
||
|
|
testLabel = testLabel[useIDs]
|
||
|
|
testHits = testHits[useIDs]
|
||
|
|
testNames = testNames[useIDs]
|
||
|
|
|
||
|
|
numPos = len(np.where(trainLabel == 1.0)[0])
|
||
|
|
numNeg = len(np.where(trainLabel == 0.0)[0])
|
||
|
|
print('major class: ' + str(float(numNeg) / float(numNeg + numPos)))
|
||
|
|
lstmLabel = np_utils.to_categorical(trainLabel, 2)
|
||
|
|
lstmTestLabel = np_utils.to_categorical(testLabel, 2)
|
||
|
|
trainHist = model.fit(trainData,lstmLabel,epochs=numEpochs,batch_size=128, validation_data=(testData,lstmTestLabel))
|
||
|
|
|
||
|
|
|
||
|
|
# save lstm model
|
||
|
|
ciscoProcessing.save_model(model,resultModelPath+'.json',
|
||
|
|
resultModelPath + '.h5')
|
||
|
|
|
||
|
|
# classify train and test
|
||
|
|
trainScores = model.predict(trainData)[:,1]
|
||
|
|
testScores = model.predict(testData)[:,1]
|
||
|
|
|
||
|
|
joblib.dump({'testLabel':testLabel,
|
||
|
|
'testHits':testHits,
|
||
|
|
'testNames':testNames,
|
||
|
|
'testScores':testScores,
|
||
|
|
'trainLabel':trainLabel,
|
||
|
|
'trainScores':trainScores},resultStorePath,compress=3)
|
||
|
|
|