862 lines
37 KiB
Python
862 lines
37 KiB
Python
import logging
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import operator
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import os
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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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import tensorflow as tf
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from keras.callbacks import CSVLogger, EarlyStopping, ModelCheckpoint
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from sklearn.metrics import confusion_matrix
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import arguments
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import dataset
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import hyperband
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import models
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# create logger
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import visualize
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from arguments import get_model_args
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from utils import exists_or_make_path, get_custom_class_weights, get_custom_sample_weights, load_model
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logger = logging.getLogger('cisco_logger')
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logger.setLevel(logging.DEBUG)
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logger.propagate = False
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# create console handler and set level to debug
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ch = logging.StreamHandler()
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ch.setLevel(logging.DEBUG)
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# create formatter
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formatter1 = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
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# add formatter to ch
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ch.setFormatter(formatter1)
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# add ch to logger
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logger.addHandler(ch)
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# ch = logging.FileHandler("info.log")
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# ch.setLevel(logging.DEBUG)
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#
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# # create formatter
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# formatter2 = logging.Formatter('!! %(asctime)s - %(name)s - %(levelname)s - %(message)s')
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#
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# # add formatter to ch
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# ch.setFormatter(formatter2)
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#
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# # add ch to logger
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# logger.addHandler(ch)
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args = arguments.parse()
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if args.gpu:
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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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# default parameter
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PARAMS = {
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"type": args.model_type,
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"embedding_type": args.embedding_type,
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# "depth": args.model_depth,
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"batch_size": args.batch_size,
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"window_size": args.window,
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"domain_length": args.domain_length,
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"flow_features": 3,
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#
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'dropout': 0.5, # currently fix
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'embedding': args.embedding,
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'flow_features': 3,
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'filter_embedding': args.filter_embedding,
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'dense_embedding': args.dense_embedding,
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'kernel_embedding': args.kernel_embedding,
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'filter_main': args.filter_main,
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'dense_main': args.dense_main,
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'kernel_main': args.kernel_main,
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'model_output': args.model_output
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}
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# TODO: remove inner global params
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def get_param_dist(dist_size="small"):
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if dist_size == "small":
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return {
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# static params
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"type": [args.model_type],
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"embedding_type": [args.embedding_type],
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# "depth": [args.model_depth],
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"model_output": [args.model_output],
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"batch_size": [args.batch_size],
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"window_size": [args.window],
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"flow_features": [3],
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"domain_length": [args.domain_length],
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# model params
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"embedding": [2 ** x for x in range(3, 6)],
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"filter_embedding": [2 ** x for x in range(1, 8)],
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"kernel_embedding": [1, 3, 5],
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"dense_embedding": [2 ** x for x in range(4, 8)],
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"dropout": [0.5],
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"filter_main": [2 ** x for x in range(1, 8)],
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"kernel_main": [1, 3, 5],
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"dense_main": [2 ** x for x in range(1, 8)],
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}
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else:
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return {
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# static params
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"type": [args.model_type],
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"embedding_type": [args.embedding_type],
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# "depth": [args.model_depth],
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"model_output": [args.model_output],
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"batch_size": [args.batch_size],
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"window_size": [args.window],
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"flow_features": [3],
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"domain_length": [args.domain_length],
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# model params
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"embedding": [2 ** x for x in range(3, 7)],
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"filter_embedding": [2 ** x for x in range(1, 10)],
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"kernel_embedding": [1, 3, 5, 7, 9],
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"dense_embedding": [2 ** x for x in range(4, 10)],
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"dropout": [0.5],
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"filter_main": [2 ** x for x in range(1, 10)],
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"kernel_main": [1, 3, 5, 7, 9],
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"dense_main": [2 ** x for x in range(1, 12)],
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}
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def shuffle_training_data(domain, flow, client, server):
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idx = np.random.permutation(len(domain))
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domain = domain[idx]
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flow = flow[idx]
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client = client[idx]
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server = server[idx]
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return domain, flow, client, server
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def main_paul_best():
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pauls_best_params = {
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"type": "paul",
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"batch_size": 64,
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"window_size": 10,
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"domain_length": 40,
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"flow_features": 3,
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#
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'dropout': 0.5,
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'domain_features': 32,
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'drop_out': 0.5,
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'embedding_size': 64,
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'filter_main': 512,
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'flow_features': 3,
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'dense_main': 32,
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'filter_embedding': 32,
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'hidden_embedding': 32,
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'kernel_embedding': 8,
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'kernels_main': 8,
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'input_length': 40
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}
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main_train(pauls_best_params)
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def main_hyperband(data, domain_length, window_size, model_type, result_file, max_iter, dist_size="small"):
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logger.info("create training dataset")
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domain_tr, flow_tr, client_tr, server_tr = load_data(data, domain_length, window_size, model_type, shuffled=True)
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return run_hyperband(dist_size, domain_tr, flow_tr, client_tr, server_tr, max_iter, result_file)
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def run_hyperband(dist_size, features, labels, max_iter, savefile):
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param_dist = get_param_dist(dist_size)
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hp = hyperband.Hyperband(param_dist, features, labels,
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max_iter=max_iter,
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savefile=savefile)
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results = hp.run()
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return results
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def train(parameters, features, labels):
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pass
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def load_data(data, domain_length, window_size, model_type, shuffled=False):
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# data preparation
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domain_tr, flow_tr, name_tr, client_tr, server_windows_tr = dataset.load_or_generate_h5data(data, domain_length,
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window_size)
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server_tr = np.max(server_windows_tr, axis=1)
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if model_type in ("inter", "staggered"):
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server_tr = np.expand_dims(server_windows_tr, 2)
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if shuffled:
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domain_tr, flow_tr, client_tr, server_tr = shuffle_training_data(domain_tr, flow_tr, client_tr, server_tr)
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return domain_tr, flow_tr, client_tr, server_tr
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def load_training_data(data, model_output, domain_length, window_size, model_type, shuffled=False):
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domain_tr, flow_tr, client_tr, server_tr = load_data(data, domain_length,
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window_size, model_type, shuffled)
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features = {"ipt_domains": domain_tr.value, "ipt_flows": flow_tr.value}
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if model_output == "both":
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labels = {"client": client_tr.value, "server": server_tr}
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loss_weights = {"client": 1.0, "server": 1.0}
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elif model_output == "client":
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labels = {"client": client_tr.value}
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loss_weights = {"client": 1.0}
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elif model_output == "server":
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labels = {"server": server_tr}
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loss_weights = {"server": 1.0}
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else:
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raise ValueError("unknown model output")
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return features, labels, loss_weights
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def get_weighting(class_weights, sample_weights, labels):
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return None, None
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client, server = labels["client"], labels["server"]
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if class_weights:
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logger.info("class weights: compute custom weights")
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custom_class_weights = get_custom_class_weights(client, server)
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logger.info(custom_class_weights)
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else:
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logger.info("class weights: set default")
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custom_class_weights = None
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if sample_weights:
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logger.info("class weights: compute custom weights")
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custom_sample_weights = get_custom_sample_weights(client, server)
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logger.info(custom_sample_weights)
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else:
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logger.info("class weights: set default")
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custom_sample_weights = None
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return custom_class_weights, custom_sample_weights
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def main_train(param=None):
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logger.info(f"Create model path {args.model_path}")
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exists_or_make_path(args.model_path)
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logger.info(f"Use command line arguments: {args}")
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# data preparation
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features, labels, loss_weights = load_training_data(args.data, args.model_output, args.domain_length,
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args.window, args.model_type)
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# call hyperband if results are not accessible
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if args.hyperband_results:
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try:
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hyper_results = joblib.load(args.hyperband_results)
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except Exception:
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logger.info("start hyperband parameter search")
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hyper_results = run_hyperband("small", features, labels, args.hyper_max_iter,
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args.hyperband_results)
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param = sorted(hyper_results, key=operator.itemgetter("loss"))[0]["params"]
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param["type"] = args.model_type
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logger.info(f"select params from result: {param}")
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if not param:
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param = PARAMS
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# custom class or sample weights
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# TODO: should throw an error when using weights with only the client labels
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custom_class_weights, custom_sample_weights = get_weighting(args.class_weights, args.sample_weights, labels)
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for i in range(args.runs):
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model_path = os.path.join(args.model_path, f"clf_{i}.h5")
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train_log_path = os.path.join(args.model_path, f"train_{i}.log.csv")
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# define training call backs
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logger.info("define callbacks")
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callbacks = []
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callbacks.append(ModelCheckpoint(filepath=model_path,
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monitor='loss',
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verbose=False,
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save_best_only=True))
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callbacks.append(CSVLogger(train_log_path))
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logger.info(f"Use early stopping: {args.stop_early}")
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if args.stop_early:
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callbacks.append(EarlyStopping(monitor='val_loss',
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patience=5,
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verbose=False))
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custom_metrics = models.get_metric_functions()
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logger.info(f"Generator model with params: {param}")
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model = models.get_models_by_params(param)
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logger.info(f"select model: {args.model_type}")
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if args.model_type == "staggered":
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logger.info("compile and pre-train server model")
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logger.info(model.get_config())
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model.compile(optimizer='adam',
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loss='binary_crossentropy',
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loss_weights={"client": 0.0, "server": 1.0},
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metrics=['accuracy'] + custom_metrics)
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model.summary()
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model.fit(features, labels,
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batch_size=args.batch_size,
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epochs=args.epochs,
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class_weight=custom_class_weights,
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sample_weight=custom_sample_weights)
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logger.info("fix server model")
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model.get_layer("domain_cnn").trainable = False
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model.get_layer("domain_cnn").layer.trainable = False
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model.get_layer("dense_server").trainable = False
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model.get_layer("server").trainable = False
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loss_weights = {"client": 1.0, "server": 0.0}
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logger.info("compile and train model")
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logger.info(model.get_config())
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model.compile(optimizer='adam',
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loss='binary_crossentropy',
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loss_weights=loss_weights,
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metrics=['accuracy'] + custom_metrics)
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model.summary()
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model.fit(features, labels,
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batch_size=args.batch_size,
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epochs=args.epochs,
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callbacks=callbacks,
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class_weight=custom_class_weights,
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sample_weight=custom_sample_weights)
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def main_retrain():
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source = os.path.join(args.model_source, "clf.h5")
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destination = os.path.join(args.model_destination, "clf.h5")
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logger.info(f"Use command line arguments: {args}")
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exists_or_make_path(args.model_destination)
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domain_tr, flow_tr, name_tr, client_tr, server_windows_tr = dataset.load_or_generate_h5data(args.data,
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args.domain_length,
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args.window)
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logger.info("define callbacks")
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callbacks = []
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callbacks.append(ModelCheckpoint(filepath=destination,
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monitor='loss',
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verbose=False,
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save_best_only=True))
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callbacks.append(CSVLogger(args.train_log))
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logger.info(f"Use early stopping: {args.stop_early}")
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if args.stop_early:
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callbacks.append(EarlyStopping(monitor='val_loss',
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patience=5,
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verbose=False))
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server_tr = np.max(server_windows_tr, axis=1)
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if args.class_weights:
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logger.info("class weights: compute custom weights")
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custom_class_weights = get_custom_class_weights(client_tr.value, server_tr)
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logger.info(custom_class_weights)
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else:
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logger.info("class weights: set default")
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custom_class_weights = None
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logger.info(f"Load pretrained model")
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embedding, model = load_model(source, custom_objects=models.get_custom_objects())
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if args.model_type in ("inter", "staggered"):
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server_tr = np.expand_dims(server_windows_tr, 2)
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features = {"ipt_domains": domain_tr.value, "ipt_flows": flow_tr.value}
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if args.model_output == "both":
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labels = {"client": client_tr.value, "server": server_tr}
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elif args.model_output == "client":
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labels = {"client": client_tr.value}
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elif args.model_output == "server":
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labels = {"server": server_tr}
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else:
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raise ValueError("unknown model output")
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logger.info("re-train model")
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embedding.summary()
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model.summary()
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model.fit(features, labels,
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batch_size=args.batch_size,
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epochs=args.epochs,
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callbacks=callbacks,
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class_weight=custom_class_weights,
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initial_epoch=args.initial_epoch)
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def main_test():
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logger.info("load test data")
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domain_val, flow_val, _, _, _, _ = dataset.load_or_generate_raw_h5data(args.data, args.domain_length, args.window)
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logger.info("load test domains")
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domain_encs, _, _ = dataset.load_or_generate_domains(args.data, args.domain_length)
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def get_dir(path):
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return os.path.split(os.path.normpath(path))
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results = {}
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for model_path in args.model_paths:
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file = get_dir(model_path)[1]
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results[file] = {}
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logger.info(f"process model {model_path}")
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embd_model, clf_model = load_model(model_path, custom_objects=models.get_custom_objects())
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pred = clf_model.predict([domain_val, flow_val],
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batch_size=args.batch_size,
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verbose=1)
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if args.model_output == "both":
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c_pred, s_pred = pred
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results[file]["client_pred"] = c_pred
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results[file]["server_pred"] = s_pred
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elif args.model_output == "client":
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results[file]["client_pred"] = pred
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else:
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results[file]["server_pred"] = pred
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domain_embeddings = embd_model.predict(domain_encs, batch_size=args.batch_size, verbose=1)
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results["domain_embds"] = domain_embeddings
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# store results every round - safety first!
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dataset.save_predictions(get_dir(model_path)[0], results)
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def main_visualization():
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def plot_model(clf_model, path):
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embd, model = load_model(clf_model, custom_objects=models.get_custom_objects())
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visualize.plot_model_as(embd, os.path.join(path, "model_embd.pdf"), shapes=False)
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visualize.plot_model_as(model, os.path.join(path, "model_clf.pdf"), shapes=False)
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def vis(model_name, model_path, df, df_paul, aggregation, curve):
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visualize.plot_clf()
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if aggregation == "user":
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df = df.groupby(df.names).max()
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df_paul = df_paul.groupby(df_paul.names).max()
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if curve == "prc":
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visualize.plot_precision_recall(df.client_val.as_matrix(), df.client_pred.as_matrix(), model_name)
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visualize.plot_precision_recall(df_paul.client_val.as_matrix(), df_paul.client_pred.as_matrix(), "paul")
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elif curve == "roc":
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visualize.plot_roc_curve(df.client_val.as_matrix(), df.client_pred.as_matrix(), model_name)
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visualize.plot_roc_curve(df_paul.client_val.as_matrix(), df_paul.client_pred.as_matrix(), "paul")
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visualize.plot_legend()
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visualize.plot_save("{}/{}_{}.pdf".format(model_path, aggregation, curve))
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_, _, name_val, hits_vt, hits_trusted, server_val = dataset.load_or_generate_raw_h5data(args.data,
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args.domain_length,
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args.window)
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results = dataset.load_predictions(args.model_path)
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df = pd.DataFrame(data={
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"names": name_val, "client_pred": results["client_pred"].flatten(),
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"hits_vt": hits_vt, "hits_trusted": hits_trusted
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})
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df["client_val"] = np.logical_or(df.hits_vt == 1.0, df.hits_trusted >= 3)
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df_user = df.groupby(df.names).max()
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paul = dataset.load_predictions("results/paul/")
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df_paul = pd.DataFrame(data={
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"names": paul["testNames"].flatten(), "client_pred": paul["testScores"].flatten(),
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"hits_vt": paul["testLabel"].flatten(), "hits_trusted": paul["testHits"].flatten()
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})
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df_paul["client_val"] = np.logical_or(df_paul.hits_vt == 1.0, df_paul.hits_trusted >= 3)
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logger.info("plot model")
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plot_model(args.clf_model, args.model_path)
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# logger.info("plot training curve")
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# logs = pd.read_csv(args.train_log)
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# if "acc" in logs.keys():
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# visualize.plot_training_curve(logs, "", "{}/client_train.png".format(args.model_path))
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# elif "client_acc" in logs.keys() and "server_acc" in logs.keys():
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# visualize.plot_training_curve(logs, "client_", "{}/client_train.png".format(args.model_path))
|
|
# visualize.plot_training_curve(logs, "server_", "{}/server_train.png".format(args.model_path))
|
|
# else:
|
|
# logger.warning("Error while plotting training curves")
|
|
|
|
logger.info("plot window prc")
|
|
vis(args.model_name, args.model_path, df, df_paul, "window", "prc")
|
|
logger.info("plot window roc")
|
|
vis(args.model_name, args.model_path, df, df_paul, "window", "roc")
|
|
logger.info("plot user prc")
|
|
vis(args.model_name, args.model_path, df, df_paul, "user", "prc")
|
|
logger.info("plot user roc")
|
|
vis(args.model_name, args.model_path, df, df_paul, "user", "roc")
|
|
|
|
# absolute values
|
|
visualize.plot_confusion_matrix(df.client_val.as_matrix(), df.client_pred.as_matrix().round(),
|
|
"{}/client_cov.pdf".format(args.model_path),
|
|
normalize=False, title="Client Confusion Matrix")
|
|
visualize.plot_confusion_matrix(df_user.client_val.as_matrix(), df_user.client_pred.as_matrix().round(),
|
|
"{}/user_cov.pdf".format(args.model_path),
|
|
normalize=False, title="User Confusion Matrix")
|
|
# normalized
|
|
visualize.plot_confusion_matrix(df.client_val.as_matrix(), df.client_pred.as_matrix().round(),
|
|
"{}/client_cov_norm.pdf".format(args.model_path),
|
|
normalize=True, title="Client Confusion Matrix")
|
|
visualize.plot_confusion_matrix(df_user.client_val.as_matrix(), df_user.client_pred.as_matrix().round(),
|
|
"{}/user_cov_norm.pdf".format(args.model_path),
|
|
normalize=True, title="User Confusion Matrix")
|
|
|
|
|
|
def main_visualize_all():
|
|
_, _, name_val, hits_vt, hits_trusted, server_val = dataset.load_or_generate_raw_h5data(args.data,
|
|
args.domain_length,
|
|
args.window)
|
|
|
|
def load_df(path):
|
|
res = dataset.load_predictions(path)
|
|
res = pd.DataFrame(data={
|
|
"names": name_val, "client_pred": res["client_pred"].flatten(),
|
|
"hits_vt": hits_vt, "hits_trusted": hits_trusted
|
|
})
|
|
res["client_val"] = np.logical_or(res.hits_vt == 1.0, res.hits_trusted >= 3)
|
|
return res
|
|
|
|
dfs = [(model_args["model_name"], load_df(model_args["model_path"])) for model_args in get_model_args(args)]
|
|
|
|
paul = dataset.load_predictions("results/paul/")
|
|
df_paul = pd.DataFrame(data={
|
|
"names": paul["testNames"].flatten(), "client_pred": paul["testScores"].flatten(),
|
|
"hits_vt": paul["testLabel"].flatten(), "hits_trusted": paul["testHits"].flatten()
|
|
})
|
|
df_paul["client_val"] = np.logical_or(df_paul.hits_vt == 1.0, df_paul.hits_trusted >= 3)
|
|
|
|
def vis(output_prefix, dfs, df_paul, aggregation, curve):
|
|
visualize.plot_clf()
|
|
if curve == "prc":
|
|
for model_name, df in dfs:
|
|
if aggregation == "user":
|
|
df = df.groupby(df.names).max()
|
|
visualize.plot_precision_recall(df.client_val.as_matrix(), df.client_pred.as_matrix(), model_name)
|
|
if aggregation == "user":
|
|
df_paul = df_paul.groupby(df_paul.names).max()
|
|
visualize.plot_precision_recall(df_paul.client_val.as_matrix(), df_paul.client_pred.as_matrix(), "paul")
|
|
elif curve == "roc":
|
|
for model_name, df in dfs:
|
|
if aggregation == "user":
|
|
df = df.groupby(df.names).max()
|
|
visualize.plot_roc_curve(df.client_val.as_matrix(), df.client_pred.as_matrix(), model_name)
|
|
if aggregation == "user":
|
|
df_paul = df_paul.groupby(df_paul.names).max()
|
|
visualize.plot_roc_curve(df_paul.client_val.as_matrix(), df_paul.client_pred.as_matrix(), "paul")
|
|
visualize.plot_legend()
|
|
visualize.plot_save("{}_{}_{}.pdf".format(output_prefix, aggregation, curve))
|
|
|
|
logger.info("plot pr curves")
|
|
vis(args.output_prefix, dfs, df_paul, "window", "prc")
|
|
logger.info("plot roc curves")
|
|
vis(args.output_prefix, dfs, df_paul, "window", "roc")
|
|
|
|
logger.info("plot user pr curves")
|
|
vis(args.output_prefix, dfs, df_paul, "user", "prc")
|
|
logger.info("plot user roc curves")
|
|
vis(args.output_prefix, dfs, df_paul, "user", "roc")
|
|
|
|
|
|
def main_visualize_all_embds():
|
|
def load_df(path):
|
|
res = dataset.load_predictions(path)
|
|
return res["domain_embds"]
|
|
|
|
dfs = [(model_args["model_name"], load_df(model_args["model_path"])) for model_args in get_model_args(args)]
|
|
|
|
from sklearn.decomposition import TruncatedSVD
|
|
|
|
def vis2(domain_embedding, labels):
|
|
n_levels = 7
|
|
logger.info(f"reduction for {len(domain_embedding)} points")
|
|
red = TruncatedSVD(n_components=2, algorithm="arpack")
|
|
domains = red.fit_transform(domain_embedding)
|
|
logger.info("plot kde")
|
|
benign = domains[labels.sum(axis=1) == 0]
|
|
# print(domains.shape)
|
|
# print(benign.shape)
|
|
# benign_idx
|
|
# sns.kdeplot(domains[labels.sum(axis=1) == 0, 0], domains[labels.sum(axis=1) == 0, 1],
|
|
# cmap="Blues", label="benign", n_levels=9, alpha=0.35, shade=True, shade_lowest=False)
|
|
# sns.kdeplot(domains[labels[:, 1], 0], domains[labels[:, 1], 1],
|
|
# cmap="Greens", label="server", n_levels=5, alpha=0.35, shade=True, shade_lowest=False)
|
|
# sns.kdeplot(domains[labels[:, 0], 0], domains[labels[:, 0], 1],
|
|
# cmap="Reds", label="client", n_levels=5, alpha=0.35, shade=True, shade_lowest=False)
|
|
plt.scatter(benign[benign_idx, 0], benign[benign_idx, 1],
|
|
cmap="Blues", label="benign", alpha=0.35, s=10)
|
|
plt.scatter(domains[labels[:, 1], 0], domains[labels[:, 1], 1],
|
|
cmap="Greens", label="server", alpha=0.35, s=10)
|
|
plt.scatter(domains[labels[:, 0], 0], domains[labels[:, 0], 1],
|
|
cmap="Reds", label="client", alpha=0.35, s=10)
|
|
|
|
return np.concatenate((domains[:1000], domains[1000:2000], domains[2000:3000]), axis=0)
|
|
|
|
domain_encs, _, labels = dataset.load_or_generate_domains(args.data, args.domain_length)
|
|
|
|
idx = np.arange(len(labels))
|
|
client = labels[:, 0]
|
|
server = labels[:, 1]
|
|
benign = np.logical_not(np.logical_or(client, server))
|
|
print(client.sum(), server.sum(), benign.sum())
|
|
|
|
idx = np.concatenate((
|
|
np.random.choice(idx[client], 1000),
|
|
np.random.choice(idx[server], 1000),
|
|
np.random.choice(idx[benign], 6000)), axis=0)
|
|
benign_idx = np.random.choice(np.arange(6000), 1000)
|
|
|
|
print(idx.shape)
|
|
lls = labels[idx]
|
|
|
|
for model_name, embd in dfs:
|
|
logger.info(f"plot embedding for {model_name}")
|
|
visualize.plot_clf()
|
|
embd = embd[idx]
|
|
points = vis2(embd, lls)
|
|
# np.savetxt("{}_{}.csv".format(args.output_prefix, model_name), points, delimiter=",")
|
|
visualize.plot_save("{}_{}.pdf".format(args.output_prefix, model_name))
|
|
|
|
|
|
def main_beta():
|
|
domain_val, _, name_val, hits_vt, hits_trusted, server_val = dataset.load_or_generate_raw_h5data(args.data,
|
|
args.domain_length,
|
|
args.window)
|
|
path, model_prefix = os.path.split(os.path.normpath(args.model_path))
|
|
curves = {
|
|
model_prefix: {"all": {}}
|
|
}
|
|
|
|
# domains = domain_val.value.reshape(-1, 40)
|
|
# domains = np.apply_along_axis(lambda d: dataset.decode_domain(d), 1, domains)
|
|
|
|
def load_df(res):
|
|
df_server = None
|
|
data = {
|
|
"names": name_val, "client_pred": res["client_pred"].flatten(),
|
|
"hits_vt": hits_vt, "hits_trusted": hits_trusted,
|
|
}
|
|
if "server_pred" in res:
|
|
server = res["server_pred"] if len(res["server_pred"].shape) == 2 else res["server_pred"].max(axis=1)
|
|
val = server_val.value.max(axis=1)
|
|
data["server_pred"] = server.flatten()
|
|
data["server_val"] = val.flatten()
|
|
|
|
# if res["server_pred"].flatten().shape == server_val.value.flatten().shape:
|
|
# df_server = pd.DataFrame(data={
|
|
# "server_pred": res["server_pred"].flatten(),
|
|
# "domain": domains,
|
|
# "server_val": server_val.value.flatten()
|
|
# })
|
|
|
|
res = pd.DataFrame(data=data)
|
|
res["client_val"] = np.logical_or(res.hits_vt == 1.0, res.hits_trusted >= 3)
|
|
|
|
return res, df_server
|
|
|
|
logger.info(f"load results from {args.model_path}")
|
|
res = dataset.load_predictions(args.model_path)
|
|
model_keys = sorted(filter(lambda x: x.startswith("clf"), res.keys()), key=lambda x: int(x[4:-3]))
|
|
|
|
client_preds = []
|
|
server_preds = []
|
|
server_flow_preds = []
|
|
client_user_preds = []
|
|
server_user_preds = []
|
|
server_domain_preds = []
|
|
server_domain_avg_preds = []
|
|
for model_name in model_keys:
|
|
logger.info(f"load model {model_name}")
|
|
df, df_server = load_df(res[model_name])
|
|
client_preds.append(df.client_pred.as_matrix())
|
|
if "server_val" in df.columns:
|
|
server_preds.append(df.server_pred.as_matrix())
|
|
if df_server is not None:
|
|
logger.info(f" group servers")
|
|
server_flow_preds.append(df_server.server_pred.as_matrix())
|
|
df_domain = df_server.groupby(df_server.domain).max()
|
|
server_domain_preds.append(df_domain.server_pred.as_matrix())
|
|
df_domain_avg = df_server.groupby(df_server.domain).rolling(10).mean()
|
|
server_domain_avg_preds.append(df_domain_avg.server_pred.as_matrix())
|
|
|
|
curves[model_prefix][model_name] = confusion_matrix(df.client_val.as_matrix(),
|
|
df.client_pred.as_matrix().round())
|
|
logger.info(f" group users")
|
|
df_user = df.groupby(df.names).max()
|
|
client_user_preds.append(df_user.client_pred.as_matrix())
|
|
if "server_val" in df.columns:
|
|
server_user_preds.append(df_user.server_pred.as_matrix())
|
|
|
|
logger.info("compute client curves")
|
|
curves[model_prefix]["all"]["client_window_prc"] = visualize.calc_pr_mean(df.client_val.as_matrix(), client_preds)
|
|
curves[model_prefix]["all"]["client_window_roc"] = visualize.calc_roc_mean(df.client_val.as_matrix(), client_preds)
|
|
curves[model_prefix]["all"]["client_user_prc"] = visualize.calc_pr_mean(df_user.client_val.as_matrix(),
|
|
client_user_preds)
|
|
curves[model_prefix]["all"]["client_user_roc"] = visualize.calc_roc_mean(df_user.client_val.as_matrix(),
|
|
client_user_preds)
|
|
|
|
if "server_val" in df.columns:
|
|
logger.info("compute server curves")
|
|
curves[model_prefix]["all"]["server_window_prc"] = visualize.calc_pr_mean(df.server_val.as_matrix(),
|
|
server_preds)
|
|
curves[model_prefix]["all"]["server_window_roc"] = visualize.calc_roc_mean(df.server_val.as_matrix(),
|
|
server_preds)
|
|
curves[model_prefix]["all"]["server_user_prc"] = visualize.calc_pr_mean(df_user.server_val.as_matrix(),
|
|
server_user_preds)
|
|
|
|
curves[model_prefix]["all"]["server_user_roc"] = visualize.calc_roc_mean(df_user.server_val.as_matrix(),
|
|
server_user_preds)
|
|
|
|
if df_server is not None:
|
|
logger.info("compute server flow curves")
|
|
curves[model_prefix]["all"]["server_flow_prc"] = visualize.calc_pr_mean(df_server.server_val.as_matrix(),
|
|
server_flow_preds)
|
|
curves[model_prefix]["all"]["server_flow_roc"] = visualize.calc_roc_mean(df_server.server_val.as_matrix(),
|
|
server_flow_preds)
|
|
curves[model_prefix]["all"]["server_domain_prc"] = visualize.calc_pr_mean(df_domain.server_val.as_matrix(),
|
|
server_domain_preds)
|
|
curves[model_prefix]["all"]["server_domain_roc"] = visualize.calc_roc_mean(df_domain.server_val.as_matrix(),
|
|
server_domain_preds)
|
|
curves[model_prefix]["all"]["server_domain_avg_prc"] = visualize.calc_pr_mean(
|
|
df_domain_avg.server_val.as_matrix(),
|
|
server_domain_avg_preds)
|
|
curves[model_prefix]["all"]["server_domain_avg_roc"] = visualize.calc_roc_mean(
|
|
df_domain_avg.server_val.as_matrix(),
|
|
server_domain_avg_preds)
|
|
|
|
joblib.dump(curves, f"{args.model_path}_curves.joblib")
|
|
try:
|
|
curves_all: dict = joblib.load(f"{path}/curves.joblib")
|
|
logger.info(f"load file {path}/curves.joblib successfully")
|
|
curves_all[model_prefix] = curves[model_prefix]
|
|
except Exception:
|
|
curves_all = curves
|
|
logger.info(f"currently {len(curves_all)} models in file: {curves_all.keys()}")
|
|
joblib.dump(curves_all, f"{path}/curves.joblib")
|
|
|
|
import matplotlib
|
|
|
|
matplotlib.use("agg")
|
|
import matplotlib.pyplot as plt
|
|
|
|
|
|
def plot_overall_result():
|
|
path, model_prefix = os.path.split(os.path.normpath(args.model_path))
|
|
exists_or_make_path(f"{path}/figs/curves/")
|
|
try:
|
|
results = joblib.load(f"{path}/curves.joblib")
|
|
logger.info("curves successfully loaded")
|
|
except Exception:
|
|
results = {}
|
|
|
|
x = np.linspace(0, 1, 10000)
|
|
for vis in ["client_window_prc", "client_window_roc", "client_user_prc", "client_user_roc",
|
|
"server_window_prc", "server_window_roc", "server_user_prc", "server_user_roc",
|
|
"server_flow_prc", "server_flow_roc", "server_domain_prc", "server_domain_roc"]:
|
|
logger.info(f"plot {vis}")
|
|
visualize.plot_clf()
|
|
for model_key in results.keys():
|
|
if vis not in results[model_key]["all"]:
|
|
continue
|
|
if "final" in model_key and vis.startswith("server_flow"):
|
|
continue
|
|
ys_mean, ys_std, ys = results[model_key]["all"][vis]
|
|
plt.plot(x, ys_mean, label=f"{model_key} - {np.mean(ys_mean):5.4} ({np.mean(ys_std):4.3})")
|
|
plt.fill_between(x, ys_mean - ys_std, ys_mean + ys_std, alpha=0.2)
|
|
if vis.endswith("prc"):
|
|
plt.xlabel('Recall')
|
|
plt.ylabel('Precision')
|
|
else:
|
|
plt.plot(x, x, label="random classifier", ls="--", c=".3", alpha=0.4)
|
|
plt.xlabel('False Positive Rate')
|
|
plt.ylabel('True Positive Rate')
|
|
plt.xscale('log')
|
|
plt.ylim([0.0, 1.0])
|
|
plt.xlim([0.0, 1.0])
|
|
visualize.plot_legend()
|
|
visualize.plot_save(f"{path}/figs/curves/{vis}_all.pdf")
|
|
return
|
|
|
|
for vis in ["client_window_prc", "client_window_roc", "client_user_prc", "client_user_roc",
|
|
"server_window_prc", "server_window_roc", "server_user_prc", "server_user_roc",
|
|
"server_flow_prc", "server_flow_roc", "server_domain_prc", "server_domain_roc"]:
|
|
logger.info(f"plot {vis}")
|
|
visualize.plot_clf()
|
|
for model_key in results.keys():
|
|
if vis not in results[model_key]["all"]:
|
|
continue
|
|
if "final" in model_key and vis.startswith("server_flow"):
|
|
continue
|
|
_, _, ys = results[model_key]["all"][vis]
|
|
for y in ys:
|
|
plt.plot(x, y, label=f"{model_key} - {np.mean(y):5.4}")
|
|
if vis.endswith("prc"):
|
|
plt.xlabel('Recall')
|
|
plt.ylabel('Precision')
|
|
else:
|
|
plt.xlabel('False Positive Rate')
|
|
plt.ylabel('True Positive Rate')
|
|
plt.xscale('log')
|
|
plt.ylim([0.0, 1.0])
|
|
plt.xlim([0.0, 1.0])
|
|
visualize.plot_legend()
|
|
visualize.plot_save(f"{path}/figs/Appendices/{model_key}_{vis}.pdf")
|
|
|
|
|
|
def main_stats():
|
|
path, model_prefix = os.path.split(os.path.normpath(args.output_prefix))
|
|
|
|
for time in ("current", "future"):
|
|
df = dataset.get_user_flow_data(f"data/{time}Data.csv.gz")
|
|
df["clientlabel"] = np.logical_or(df.virusTotalHits > 3, df.trustedHits > 0)
|
|
# df_user = df.groupby(df.user_hash).max()
|
|
# df_server = df.groupby(df.domain).max()
|
|
|
|
# len(df)
|
|
# df.clientlabel.sum()
|
|
# df.serverLabel.sum()
|
|
|
|
for col in ["duration", "bytes_down", "bytes_up"]:
|
|
# visualize.plot_clf()
|
|
plt.clf()
|
|
plt.hist(df[col])
|
|
visualize.plot_save(f"{path}/figs/hist_{time}_{col}.pdf")
|
|
print(".")
|
|
# visualize.plot_clf()
|
|
plt.clf()
|
|
plt.hist(np.log1p(df[col]))
|
|
visualize.plot_save(f"{path}/figs/hist_{time}_norm_{col}.pdf")
|
|
print("-")
|
|
|
|
|
|
def main_stats2():
|
|
import joblib
|
|
res = joblib.load("results/variance_test_hyper/curves.joblib")
|
|
|
|
for vis in ["client_window_prc", "client_window_roc", "client_user_prc", "client_user_roc",
|
|
"server_window_prc", "server_window_roc", "server_user_prc", "server_user_roc",
|
|
"server_flow_prc", "server_flow_roc", "server_domain_prc", "server_domain_roc",
|
|
"server_domain_avg_prc", "server_domain_avg_roc"]:
|
|
tab = []
|
|
for m, r in res.items():
|
|
if vis not in r: continue
|
|
tab.append(r["all"][vis][2].mean(axis=1))
|
|
if not tab: continue
|
|
|
|
df = pd.DataFrame(data=np.vstack(tab).T, columns=list(res.keys()),
|
|
index=range(1, 21))
|
|
df.to_csv(f"{vis}.csv")
|
|
|
|
print(f"% {vis}")
|
|
print(df.round(4).to_latex())
|
|
print()
|
|
|
|
|
|
def main():
|
|
if "train" == args.mode:
|
|
main_train()
|
|
if "retrain" == args.mode:
|
|
main_retrain()
|
|
if "hyperband" == args.mode:
|
|
main_hyperband(args.data, args.domain_length, args.window, args.model_type, args.hyperband_results,
|
|
args.hyper_max_iter)
|
|
if "test" == args.mode:
|
|
main_test()
|
|
if "beta" == args.mode:
|
|
main_beta()
|
|
if "all_beta" == args.mode:
|
|
plot_overall_result()
|
|
if "embedding" == args.mode:
|
|
main_visualize_all_embds()
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main()
|