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dataset.py
83
dataset.py
@ -6,12 +6,6 @@ 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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@ -60,23 +54,21 @@ def get_user_chunks(dataFrame, windowSize=10, overlapping=False,
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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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def get_domain_features(domain, vocab, max_length=40):
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curFeature = np.zeros([max_length, ])
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for j in range(np.min([len(domain), max_length])):
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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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if curCharacter in vocab:
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curFeature[j] = vocab[curCharacter]
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return curFeature
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def getFlowFeatures(curDataLine):
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def get_flow_features(flow):
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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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for i, curKey in enumerate(useKeys):
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try:
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curFeature[i] = np.log1p(curDataLine[curKey]).astype(float)
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curFeature[i] = np.log1p(flow[curKey]).astype(float)
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except:
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pass
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return curFeature
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@ -93,13 +85,13 @@ def getCiscoFeatures(curDataLine, urlSIPDict):
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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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def create_dataset_from_flows(user_flow_df, char_dict, maxLen, threshold=3, windowSize=10, use_cisco_features=False):
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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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overlapping=True, 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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@ -107,68 +99,63 @@ def create_dataset_from_flows(user_flow_df, char_dict, maxLen, threshold=3, wind
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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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domains=domainLists, dfs=dfLists, charachterDict=char_dict,
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maxLen=maxLen, threshold=threshold,
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flagUseCiscoFeatures=False, urlSIPDIct=dict(),
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use_cisco_features=use_cisco_features, 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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def create_dataset_from_lists(domains, dfs, charachterDict, maxLen, threshold=3,
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use_cisco_features=False, urlSIPDIct=dict(),
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windowSize=10):
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if 'hits' in dfLists[0].keys():
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if 'hits' in dfs[0].keys():
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hitName = 'hits'
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elif 'virusTotalHits' in dfLists[0].keys():
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elif 'virusTotalHits' in dfs[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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if use_cisco_features:
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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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outputFeatures.append(np.zeros([len(domains), maxLen]))
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outputFeatures.append(np.zeros([len(domains), numFeatures]))
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for i in tqdm(np.arange(len(domainLists)), miniters=10):
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for i in tqdm(np.arange(len(domains)), 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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for j in range(np.min([windowSize, len(domains[i])])):
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outputFeatures[curCounter][i, :] = get_domain_features(domains[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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if use_cisco_features:
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outputFeatures[curCounter][i, 0:numFlowFeatures] = get_flow_features(dfs[i].iloc[j])
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outputFeatures[curCounter][i, numFlowFeatures:] = get_cisco_features(dfs[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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outputFeatures[curCounter][i, :] = get_flow_features(dfs[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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if np.max(dfs[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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elif np.max(dfs[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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elif np.max(dfs[i][hitName]) > 0 and np.max(dfs[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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hits.append(np.max(dfs[i][hitName]))
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trainNames.append(np.unique(dfs[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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df = pd.read_csv("data/rk_data.csv.gz")
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df.drop("Unnamed: 0", 1, inplace=True)
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df.set_index(keys=['user_hash'], drop=False, inplace=True)
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return df
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def get_flow_per_user(df):
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6
main.py
6
main.py
@ -5,6 +5,12 @@ import dataset
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import models
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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 main():
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# parameter
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innerCNNFilters = 512
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