298 lines
10 KiB
Python
298 lines
10 KiB
Python
# -*- coding: utf-8 -*-
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import logging
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import string
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from multiprocessing import Pool
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import h5py
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import joblib
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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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logger = logging.getLogger('cisco_logger')
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char2idx = dict((char, idx + 1) for (idx, char) in
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enumerate(string.ascii_lowercase + string.punctuation + string.digits + " "))
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idx2char = {v: k for k, v in char2idx.items()}
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def get_character_dict():
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return char2idx
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def get_vocab_size():
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return len(char2idx) + 1
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def encode_char(c):
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return char2idx.get(c, 0)
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def decode_char(i):
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return idx2char.get(i, "")
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encode_char = np.vectorize(encode_char)
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decode_char = np.vectorize(decode_char)
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def encode_domain(domain: string):
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return encode_char(list(domain))
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def decode_domain(domain):
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return "".join(decode_char(domain))
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def get_user_chunks(user_flow, window=10):
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result = []
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chunk_size = (len(user_flow) // window)
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for i in range(chunk_size):
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result.append(user_flow.iloc[i * window:(i + 1) * window])
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if result and len(result[-1]) != window:
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result.pop()
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return result
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def get_domain_features(domain: string, 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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c = domain[len(domain) - 1 - j]
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encoding[max_length - 1 - j] = encode_char(c)
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return encoding
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def get_all_flow_features(features):
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flows = np.stack(
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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 filter_window_dataset_by_hits(domain, flow, name, hits, trusted_hits, server):
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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 == 1.0, trusted_hits >= 1.0))[0]
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neg_idx = np.where(hits == 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 = domain[idx]
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flow = flow[idx]
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client = np.zeros_like(idx, float)
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client[:pos_idx.shape[-1]] = 1.0
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server = server[idx]
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name = name[idx]
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return domain, flow, name, client, server
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def create_raw_dataset_from_flows(user_flow_df, max_len, window_size=10):
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logger.info("get chunks from user data frames")
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with Pool() as pool:
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results = []
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for user_flow in tqdm(get_flow_per_user(user_flow_df), total=len(user_flow_df['user_hash'].unique().tolist())):
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results.append(pool.apply_async(get_user_chunks, (user_flow, window_size)))
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windows = [window for res in results for window in res.get()]
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logger.info("create training dataset")
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domain, flow, hits, name, server, trusted_hits = create_dataset_from_windows(chunks=windows,
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max_len=max_len)
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# make client labels discrete with 4 different values
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hits = np.apply_along_axis(lambda x: make_label_discrete(x, 3), 0, np.atleast_2d(hits))
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return domain, flow, name, hits, trusted_hits, server
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def store_h5dataset(path, data: dict):
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f = h5py.File(path + ".h5", "w")
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for key, val in data.items():
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f.create_dataset(key, data=val)
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f.close()
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def check_h5dataset(path):
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return open(path + ".h5", "r")
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def load_h5dataset(path):
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f = h5py.File(path + ".h5", "r")
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data = {}
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for k in f.keys():
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data[k] = f[k]
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return data
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def create_dataset_from_windows(chunks, max_len):
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"""
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combines domain and feature windows to sequential training data
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:param chunks: list of flow feature windows
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:param vocab:
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:param max_len:
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:return:
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"""
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def get_domain_features_reduced(d):
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return get_domain_features(d[0], max_len)
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logger.info(" compute domain features")
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domain_features = []
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for ds in tqdm(map(lambda f: f.domain, chunks)):
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domain_features.append(np.apply_along_axis(get_domain_features_reduced, 2, np.atleast_3d(ds)))
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domain_features = np.concatenate(domain_features, 0)
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logger.info(" compute flow features")
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flow_features = get_all_flow_features(chunks)
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logger.info(" select hits")
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hits = np.max(np.stack(map(lambda f: f.virusTotalHits, chunks)), axis=1)
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logger.info(" select names")
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names = np.stack(map(lambda f: f.user_hash, chunks))
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assert (names[:, :1].repeat(10, axis=1) == names).all()
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names = names[:, 0]
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logger.info(" select servers")
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servers = np.stack(map(lambda f: f.serverLabel, chunks))
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logger.info(" select trusted hits")
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trusted_hits = np.max(np.stack(map(lambda f: f.trustedHits, chunks)), 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 make_label_discrete(values, threshold):
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max_val = np.max(values)
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if max_val >= threshold:
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return 1.0
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elif max_val == -1:
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return -1.0
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elif 0 < max_val < 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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"http_method": object,
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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, index_col=False)
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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].dropna(axis=0, how="any")
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def load_or_generate_h5data(train_data, domain_length, window_size):
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logger.info(f"check for h5data {train_data}")
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try:
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check_h5dataset(train_data)
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except FileNotFoundError:
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logger.info("load raw training dataset")
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domain, flow, name, hits, trusted_hits, server = load_or_generate_raw_h5data(train_data, domain_length,
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window_size)
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logger.info("filter training dataset")
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domain, flow, name, client, server = filter_window_dataset_by_hits(domain.value, flow.value,
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name.value, hits.value,
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trusted_hits.value, server.value)
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logger.info("store training dataset as h5 file")
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data = {
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"domain": domain.astype(np.int8),
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"flow": flow,
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"name": name,
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"client": client.astype(np.bool),
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"server": server.astype(np.bool)
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}
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store_h5dataset(train_data, data)
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logger.info("load h5 dataset")
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data = load_h5dataset(train_data)
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return data["domain"], data["flow"], data["name"], data["client"], data["server"]
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def load_or_generate_raw_h5data(train_data, domain_length, window_size):
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h5data = train_data + "_raw"
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logger.info(f"check for h5data {h5data}")
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try:
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check_h5dataset(h5data)
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except FileNotFoundError:
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logger.info("h5 data not found - load csv file")
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user_flow_df = get_user_flow_data(train_data)
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logger.info("create raw training dataset")
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domain, flow, name, hits, trusted_hits, server = create_raw_dataset_from_flows(user_flow_df, domain_length,
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window_size)
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logger.info("store raw training dataset as h5 file")
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data = {
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"domain": domain.astype(np.int8),
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"flow": flow,
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"name": name,
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"hits_vt": hits.astype(np.int8),
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"hits_trusted": hits.astype(np.int8),
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"server": server.astype(np.bool)
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}
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store_h5dataset(h5data, data)
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logger.info("load h5 dataset")
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data = load_h5dataset(h5data)
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return data["domain"], data["flow"], data["name"], data["hits_vt"], data["hits_trusted"], data["server"]
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def generate_names(train_data, window_size):
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user_flow_df = get_user_flow_data(train_data)
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with Pool() as pool:
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results = []
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for user_flow in tqdm(get_flow_per_user(user_flow_df),
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total=len(user_flow_df['user_hash'].unique().tolist())):
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results.append(pool.apply_async(get_user_chunks, (user_flow, window_size)))
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windows = [window for res in results for window in res.get()]
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names = np.stack(map(lambda f: f.user_hash, windows))
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names = names[:, 0]
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return names
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def load_or_generate_domains(train_data, domain_length):
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fn = f"{train_data}_domains.gz"
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try:
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logger.info(f"Load file {fn}.")
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user_flow_df = pd.read_csv(fn)
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logger.info(f"File successfully loaded.")
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except FileNotFoundError:
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logger.info(f"File {fn} not found, recreate.")
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user_flow_df = get_user_flow_data(train_data)
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# user_flow_df.reset_index(inplace=True)
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user_flow_df = user_flow_df[["domain", "serverLabel", "trustedHits", "virusTotalHits"]].dropna(axis=0,
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how="any")
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user_flow_df = user_flow_df.groupby(user_flow_df.domain).mean()
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user_flow_df.reset_index(inplace=True)
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user_flow_df["clientLabel"] = np.where(
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np.logical_or(user_flow_df.trustedHits > 0, user_flow_df.virusTotalHits >= 3), True, False)
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user_flow_df[["serverLabel", "clientLabel"]] = user_flow_df[["serverLabel", "clientLabel"]].astype(bool)
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user_flow_df = user_flow_df[["domain", "serverLabel", "clientLabel"]]
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user_flow_df.to_csv(fn, compression="gzip")
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logger.info(f"Extract features from domains")
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domain_encs = user_flow_df.domain.apply(lambda d: get_domain_features(d, domain_length))
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domain_encs = np.stack(domain_encs)
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return domain_encs, user_flow_df.domain, user_flow_df[["clientLabel", "serverLabel"]].as_matrix().astype(bool)
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def save_predictions(path, results):
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joblib.dump(results, path + "/results.joblib", compress=3)
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def load_predictions(path):
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return joblib.load(path + "/results.joblib")
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