ma_cisco_malware/dataset.py

# -*- coding: utf-8 -*-
import string

import numpy as np
import pandas as pd
from tqdm import tqdm

chars = dict((char, idx + 1) for (idx, char) in
             enumerate(string.ascii_lowercase + string.punctuation + string.digits))


def get_character_dict():
    return chars


def encode_char(c):
    if c in chars:
        return chars[c]
    else:
        return 0


encode_char = np.vectorize(encode_char)


def get_user_chunks(dataFrame, windowSize=10, overlapping=False,
                    maxLengthInSeconds=300):
    maxMilliSeconds = maxLengthInSeconds * 1000
    outDomainLists = []
    outDFFrames = []
    if overlapping == False:
        numBlocks = int(np.ceil(float(len(dataFrame)) / float(windowSize)))
        userIDs = np.arange(len(dataFrame))
        for blockID in np.arange(numBlocks):
            curIDs = userIDs[(blockID * windowSize):((blockID + 1) * windowSize)]
            # print(curIDs)
            useData = dataFrame.iloc[curIDs]
            curDomains = useData['domain']
            if maxLengthInSeconds != -1:
                curMinMilliSeconds = np.min(useData['timeStamp']) + maxMilliSeconds
                underTimeOutIDs = np.where(np.array(useData['timeStamp']) <= curMinMilliSeconds)
                if len(underTimeOutIDs) != len(curIDs):
                    curIDs = curIDs[underTimeOutIDs]
                    useData = dataFrame.iloc[curIDs]
                    curDomains = useData['domain']
            outDomainLists.append(list(curDomains))
            outDFFrames.append(useData)
    else:
        numBlocks = len(dataFrame) + 1 - windowSize
        userIDs = np.arange(len(dataFrame))
        for blockID in np.arange(numBlocks):
            curIDs = userIDs[blockID:blockID + windowSize]
            useData = dataFrame.iloc[curIDs]
            curDomains = useData['domain']
            if maxLengthInSeconds != -1:
                curMinMilliSeconds = np.min(useData['timeStamp']) + maxMilliSeconds
                underTimeOutIDs = np.where(np.array(useData['timeStamp']) <= curMinMilliSeconds)
                if len(underTimeOutIDs) != len(curIDs):
                    curIDs = curIDs[underTimeOutIDs]
                    useData = dataFrame.iloc[curIDs]
                    curDomains = useData['domain']
            outDomainLists.append(list(curDomains))
            outDFFrames.append(useData)
    if len(outDomainLists[-1]) != windowSize:
        outDomainLists.pop(-1)
        outDFFrames.pop(-1)
    return (outDomainLists, outDFFrames)


def get_domain_features(domain, vocab, max_length=40):
    encoding = np.zeros((max_length,))
    for j in range(np.min([len(domain), max_length])):
        curCharacter = domain[-j]
        if curCharacter in vocab:
            encoding[j] = vocab[curCharacter]
    return encoding


def get_flow_features(flow):
    keys = ['duration', 'bytes_down', 'bytes_up']
    features = np.zeros([len(keys), ])
    for i, key in enumerate(keys):
        # TODO: does it still works after exceptions occur -- default: zero!
        # i wonder whether something brokes
        # if there are exceptions regarding to inconsistent feature length
        try:
            features[i] = np.log1p(flow[key]).astype(float)
        except:
            pass
    return features


def get_cisco_features(curDataLine, urlSIPDict):
    numCiscoFeatures = 30
    try:
        ciscoFeatures = urlSIPDict[str(curDataLine['domain']) + str(curDataLine['server_ip'])]
        # log transform
        ciscoFeatures = np.log1p(ciscoFeatures).astype(float)
        return ciscoFeatures.ravel()
    except:
        return np.zeros([numCiscoFeatures, ]).ravel()


def create_dataset_from_flows(user_flow_df, char_dict, max_len, window_size=10, use_cisco_features=False):
    domains = []
    features = []
    print("get chunks from user data frames")
    for i, user_flow in enumerate(get_flow_per_user(user_flow_df)):
        (domain_windows, feature_windows) = get_user_chunks(user_flow,
                                                            windowSize=window_size,
                                                            overlapping=False,
                                                            maxLengthInSeconds=-1)
        domains += domain_windows
        features += feature_windows
        # TODO: remove later
        if i >= 50:
            break

    print("create training dataset")
    domain_tr, flow_tr, hits_tr, names_tr, server_tr, trusted_hits_tr = create_dataset_from_lists(
        domains=domains, features=features, vocab=char_dict,
        max_len=max_len,
        use_cisco_features=use_cisco_features, urlSIPDIct=dict(),
        window_size=window_size)

    # make client labels discrete with 4 different values
    # TODO: use trusted_hits_tr for client classification too
    client_labels = np.apply_along_axis(lambda x: discretize_label(x, 3), 0, np.atleast_2d(hits_tr))
    # select only 1.0 and 0.0 from training data
    pos_idx = np.where(client_labels == 1.0)[0]
    neg_idx = np.where(client_labels == 0.0)[0]
    idx = np.concatenate((pos_idx, neg_idx))
    # choose selected sample to train on
    domain_tr = domain_tr[idx]
    flow_tr = flow_tr[idx]
    client_labels = client_labels[idx]
    server_labels = server_tr[idx]

    return domain_tr, flow_tr, client_labels, server_labels


def create_dataset_from_lists(domains, features, vocab, max_len,
                              use_cisco_features=False, urlSIPDIct=dict(),
                              window_size=10):
    """
    combines domain and feature windows to sequential training data
    :param domains: list of domain windows
    :param features: list of feature windows
    :param vocab: 
    :param max_len: 
    :param use_cisco_features: idk
    :param urlSIPDIct: idk
    :param window_size: size of the flow window
    :return: 
    """
    # TODO: check for hits vs vth consistency
    # if 'hits' in dfs[0].keys():
    #     hits_col = 'hits'
    # elif 'virusTotalHits' in dfs[0].keys():
    #     hits_col = 'virusTotalHits'
    hits_col = "virusTotalHits"

    numFlowFeatures = 3
    numCiscoFeatures = 30
    numFeatures = numFlowFeatures
    if use_cisco_features:
        numFeatures += numCiscoFeatures
    sample_size = len(domains)
    hits = []
    names = []
    servers = []
    trusted_hits = []

    domain_features = np.zeros((sample_size, window_size, max_len))
    flow_features = np.zeros((sample_size, window_size, numFeatures))

    for i in tqdm(np.arange(sample_size), miniters=10):
        for j in range(window_size):
            domain_features[i, j] = get_domain_features(domains[i][j], vocab, max_len)
            flow_features[i, j] = get_flow_features(features[i].iloc[j])
            # TODO: cisco features?

        hits.append(np.max(features[i][hits_col]))
        names.append(np.unique(features[i]['user_hash']))
        servers.append(np.max(features[i]['serverLabel']))
        trusted_hits.append(np.max(features[i]['trustedHits']))
    return (domain_features, flow_features,
            np.array(hits), np.array(names), np.array(servers), np.array(trusted_hits))


def discretize_label(values, threshold):
    maxVal = np.max(values)
    if maxVal >= threshold:
        return 1.0
    elif maxVal == -1:
        return -1.0
    elif 0 < maxVal < threshold:
        return -2.0
    else:
        return 0.0


def get_user_flow_data(csv_file):
    df = pd.read_csv(csv_file)
    keys = ["duration", "bytes_down", "bytes_up", "domain", "timeStamp", "server_ip", "user_hash", "virusTotalHits",
            "serverLabel", "trustedHits"]
    df = df[keys]
    df.set_index(keys=['user_hash'], drop=False, inplace=True)
    return df


def get_flow_per_user(df):
    users = df['user_hash'].unique().tolist()
    for user in users:
        yield df.loc[df.user_hash == user]