174 lines
6.2 KiB
Python
174 lines
6.2 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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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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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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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 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 vocab:
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curFeature[j] = vocab[curCharacter]
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return curFeature
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def get_flow_features(flow):
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keys = ['duration', 'bytes_down', 'bytes_up']
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features = np.zeros([len(keys), ])
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for i, key in enumerate(keys):
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# TODO: does it still works after exceptions occur -- default: zero!
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# i wonder whether something brokes
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# if there are exceptions regarding to inconsistent feature length
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try:
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features[i] = np.log1p(flow[key]).astype(float)
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except:
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pass
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return features
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def get_cisco_features(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, 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=True, maxLengthInSeconds=-1)
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domainLists += domainListsTmp
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dfLists += dfListsTmp
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# TODO: remove later
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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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domains=domainLists, dfs=dfLists, vocab=char_dict,
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maxLen=maxLen,
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use_cisco_features=use_cisco_features, urlSIPDIct=dict(),
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window_size=windowSize)
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def create_dataset_from_lists(domains, dfs, vocab, maxLen,
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use_cisco_features=False, urlSIPDIct=dict(),
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window_size=10):
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# TODO: check for hits vs vth consistency
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if 'hits' in dfs[0].keys():
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hitName = 'hits'
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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 use_cisco_features:
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numFeatures += numCiscoFeatures
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Xs = []
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hits = []
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names = []
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servers = []
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trusted_hits = []
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for i in range(window_size):
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Xs.append(np.zeros([len(domains), maxLen]))
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Xs.append(np.zeros([len(domains), numFeatures]))
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for i in tqdm(np.arange(len(domains)), miniters=10):
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ctr = 0
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for j in range(np.min([window_size, len(domains[i])])):
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Xs[ctr][i, :] = get_domain_features(domains[i][j], vocab, maxLen)
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ctr += 1
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if use_cisco_features:
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Xs[ctr][i, 0:numFlowFeatures] = get_flow_features(dfs[i].iloc[j])
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Xs[ctr][i, numFlowFeatures:] = get_cisco_features(dfs[i].iloc[j], urlSIPDIct)
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else:
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Xs[ctr][i, :] = get_flow_features(dfs[i].iloc[j])
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ctr += 1
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hits.append(np.max(dfs[i][hitName]))
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names.append(np.unique(dfs[i]['user_hash']))
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servers.append(np.max(dfs[i]['serverLabel']))
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trusted_hits.append(np.max(dfs[i]['trustedHits']))
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return Xs, np.array(hits), np.array(names), np.array(servers), np.array(trusted_hits)
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def discretize_label(values, threshold):
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maxVal = np.max(values)
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if maxVal >= threshold:
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return 1.0
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elif maxVal == -1:
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return -1.0
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elif 0 < maxVal < threshold:
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return -2.0
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else:
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return 0.0
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def get_user_flow_data():
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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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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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