204 lines
7.2 KiB
Python
204 lines
7.2 KiB
Python
# -*- coding: utf-8 -*-
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import string
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import h5py
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import numpy as np
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import pandas as pd
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from keras.utils import np_utils
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from tqdm import tqdm
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chars = dict((char, idx + 1) for (idx, char) in
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enumerate(string.ascii_lowercase + string.punctuation + string.digits))
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def get_character_dict():
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return chars
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def encode_char(c):
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if c in chars:
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return chars[c]
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else:
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return 0
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encode_char = np.vectorize(encode_char)
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# TODO: refactor
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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 not overlapping:
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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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if len(outDomainLists[-1]) != windowSize:
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outDomainLists.pop(-1)
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outDFFrames.pop(-1)
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return outDomainLists, outDFFrames
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def get_domain_features(domain, vocab: dict, max_length=40):
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encoding = np.zeros((max_length,))
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for j in range(min(len(domain), max_length)):
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char = domain[-j] # TODO: why -j -> order reversed for domain url?
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encoding[j] = vocab.get(char, 0)
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return encoding
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def get_all_flow_features(features):
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flows = np.stack(list(
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map(lambda f: f[["duration", "bytes_up", "bytes_down"]], features))
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)
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return np.log1p(flows)
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def create_dataset_from_flows(user_flow_df, char_dict, max_len, window_size=10):
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domains = []
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features = []
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print("get chunks from user data frames")
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for i, user_flow in tqdm(list(enumerate(get_flow_per_user(user_flow_df)))):
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(domain_windows, feature_windows) = get_user_chunks(user_flow,
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windowSize=window_size,
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overlapping=False,
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maxLengthInSeconds=-1)
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domains += domain_windows
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features += feature_windows
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print("create training dataset")
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domain_tr, flow_tr, hits_tr, _, server_tr, trusted_hits_tr = create_dataset_from_lists(domains=domains,
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flows=features,
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vocab=char_dict,
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max_len=max_len,
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window_size=window_size)
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# make client labels discrete with 4 different values
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hits_tr = np.apply_along_axis(lambda x: discretize_label(x, 3), 0, np.atleast_2d(hits_tr))
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# select only 1.0 and 0.0 from training data
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pos_idx = np.where(np.logical_or(hits_tr == 1.0, trusted_hits_tr >= 1.0))[0]
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neg_idx = np.where(hits_tr == 0.0)[0]
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idx = np.concatenate((pos_idx, neg_idx))
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# choose selected sample to train on
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domain_tr = domain_tr[idx]
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flow_tr = flow_tr[idx]
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client_tr = np.zeros_like(idx, float)
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client_tr[:pos_idx.shape[-1]] = 1.0
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server_tr = server_tr[idx]
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client_tr = np_utils.to_categorical(client_tr, 2)
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server_tr = np_utils.to_categorical(server_tr, 2)
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return domain_tr, flow_tr, client_tr, server_tr
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def store_h5dataset(path, domain_tr, flow_tr, client_tr, server_tr):
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f = h5py.File(path, "w")
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domain_tr = domain_tr.astype(np.int8)
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f.create_dataset("domain", data=domain_tr)
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f.create_dataset("flow", data=flow_tr)
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server_tr = server_tr.astype(np.bool)
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client_tr = client_tr.astype(np.bool)
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f.create_dataset("client", data=client_tr)
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f.create_dataset("server", data=server_tr)
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f.close()
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def load_h5dataset(path):
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data = h5py.File(path, "r")
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return data["domain"], data["flow"], data["client"], data["server"]
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def create_dataset_from_lists(domains, flows, vocab, max_len, window_size=10):
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"""
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combines domain and feature windows to sequential training data
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:param domains: list of domain windows
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:param flows: list of flow feature windows
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:param vocab:
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:param max_len:
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:param window_size: size of the flow window
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:return:
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"""
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# sample_size = len(domains)
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# domain_features = np.zeros((sample_size, window_size, max_len))
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flow_features = get_all_flow_features(flows)
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domain_features = np.array([[get_domain_features(d, vocab, max_len) for d in x] for x in domains])
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hits = np.max(np.stack(map(lambda f: f.virusTotalHits, flows)), axis=1)
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names = np.unique(np.stack(map(lambda f: f.user_hash, flows)), axis=1)
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servers = np.max(np.stack(map(lambda f: f.serverLabel, flows)), axis=1)
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trusted_hits = np.max(np.stack(map(lambda f: f.trustedHits, flows)), axis=1)
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return (domain_features, flow_features,
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hits, names, servers, 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(csv_file):
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types = {
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"duration": int,
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"bytes_down": int,
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"bytes_up": int,
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"domain": object,
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"timeStamp": float,
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"server_ip": object,
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"user_hash": float,
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"virusTotalHits": int,
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"serverLabel": int,
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"trustedHits": int
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}
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df = pd.read_csv(csv_file)
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df = df[list(types.keys())]
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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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