178 lines
6.9 KiB
Python
178 lines
6.9 KiB
Python
# -*- coding: utf-8 -*-
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import string
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import numpy as np
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import pandas as pd
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from tqdm import tqdm
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# config = tf.ConfigProto(log_device_placement=True)
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# config.gpu_options.per_process_gpu_memory_fraction = 0.5
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# config.gpu_options.allow_growth = True
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# session = tf.Session(config=config)
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def get_character_dict():
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return dict((char, idx) for (idx, char) in
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enumerate(string.ascii_lowercase + string.punctuation))
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def get_user_chunks(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 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]).astype(float)
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except:
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pass
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return curFeature
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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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# log transform
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ciscoFeatures = np.log1p(ciscoFeatures).astype(float)
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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 create_dataset_from_flows(user_flow_df, char_dict, maxLen, threshold=3, windowSize=10):
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domainLists = []
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dfLists = []
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print("get chunks from user data frames")
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for i, user_flow in enumerate(get_flow_per_user(user_flow_df)):
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(domainListsTmp, dfListsTmp) = get_user_chunks(user_flow, windowSize=windowSize,
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overlapping=False, maxLengthInSeconds=-1)
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domainLists += domainListsTmp
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dfLists += dfListsTmp
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if i >= 10:
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break
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print("create training dataset")
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return create_dataset_from_lists(
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domainLists=domainLists, dfLists=dfLists, charachterDict=char_dict,
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maxLen=maxLen, threshold=threshold,
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flagUseCiscoFeatures=False, urlSIPDIct=dict(),
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windowSize=windowSize)
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def create_dataset_from_lists(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 get_user_flow_data():
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# load train and test data from joblib
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# created with createTrainDataMultipleTaskLearning.py
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# rk: changed to csv file
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trainDFs = pd.read_csv("data/rk_data.csv.gz")
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trainDFs.drop("Unnamed: 0", 1, inplace=True)
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trainDFs.set_index(keys=['user_hash'], drop=False, inplace=True)
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users = trainDFs['user_hash'].unique().tolist()
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u0 = trainDFs.loc[trainDFs.user_hash == users[0]]
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return trainDFs
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def get_flow_per_user(df):
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users = df['user_hash'].unique().tolist()
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for user in users:
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yield df.loc[df.user_hash == user]
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