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6f2ff70 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 | import numpy as np
import scipy.stats as stats
import seaborn as sns
import matplotlib.pyplot as plt
import pickle
import time
import torch
from tqdm import tqdm
from torch.utils.data import DataLoader
from bert_pytorch.dataset import WordVocab
from bert_pytorch.dataset import LogDataset
from bert_pytorch.dataset.sample import fixed_window
def compute_anomaly(results, params, seq_threshold=0.5):
is_logkey = params["is_logkey"]
is_time = params["is_time"]
total_errors = 0
for seq_res in results:
# label pairs as anomaly when over half of masked tokens are undetected
if (is_logkey and seq_res["undetected_tokens"] > seq_res["masked_tokens"] * seq_threshold) or \
(is_time and seq_res["num_error"]> seq_res["masked_tokens"] * seq_threshold) or \
(params["hypersphere_loss_test"] and seq_res["deepSVDD_label"]):
total_errors += 1
return total_errors
def find_best_threshold(test_normal_results, test_abnormal_results, params, th_range, seq_range):
best_result = [0] * 9
for seq_th in seq_range:
FP = compute_anomaly(test_normal_results, params, seq_th)
TP = compute_anomaly(test_abnormal_results, params, seq_th)
if TP == 0:
continue
TN = len(test_normal_results) - FP
FN = len(test_abnormal_results) - TP
P = 100 * TP / (TP + FP)
R = 100 * TP / (TP + FN)
F1 = 2 * P * R / (P + R)
if F1 > best_result[-1]:
best_result = [0, seq_th, FP, TP, TN, FN, P, R, F1]
return best_result
class Predictor():
def __init__(self, options):
self.model_path = options["model_path"]
self.vocab_path = options["vocab_path"]
self.device = options["device"]
self.window_size = options["window_size"]
self.adaptive_window = options["adaptive_window"]
self.seq_len = options["seq_len"]
self.corpus_lines = options["corpus_lines"]
self.on_memory = options["on_memory"]
self.batch_size = options["batch_size"]
self.num_workers = options["num_workers"]
self.num_candidates = options["num_candidates"]
self.output_dir = options["output_dir"]
self.model_dir = options["model_dir"]
self.gaussian_mean = options["gaussian_mean"]
self.gaussian_std = options["gaussian_std"]
self.is_logkey = options["is_logkey"]
self.is_time = options["is_time"]
self.scale_path = options["scale_path"]
self.hypersphere_loss = options["hypersphere_loss"]
self.hypersphere_loss_test = options["hypersphere_loss_test"]
self.lower_bound = self.gaussian_mean - 3 * self.gaussian_std
self.upper_bound = self.gaussian_mean + 3 * self.gaussian_std
self.center = None
self.radius = None
self.test_ratio = options["test_ratio"]
self.mask_ratio = options["mask_ratio"]
self.min_len=options["min_len"]
def detect_logkey_anomaly(self, masked_output, masked_label):
num_undetected_tokens = 0
output_maskes = []
for i, token in enumerate(masked_label):
# output_maskes.append(torch.argsort(-masked_output[i])[:30].cpu().numpy()) # extract top 30 candidates for mask labels
if token not in torch.argsort(-masked_output[i])[:self.num_candidates]:
num_undetected_tokens += 1
return num_undetected_tokens, [output_maskes, masked_label.cpu().numpy()]
@staticmethod
def generate_test(output_dir, file_name, window_size, adaptive_window, seq_len, scale, min_len):
"""
:return: log_seqs: num_samples x session(seq)_length, tim_seqs: num_samples x session_length
"""
log_seqs = []
tim_seqs = []
with open(output_dir + file_name, "r") as f:
for idx, line in tqdm(enumerate(f.readlines())):
#if idx > 40: break
log_seq, tim_seq = fixed_window(line, window_size,
adaptive_window=adaptive_window,
seq_len=seq_len, min_len=min_len)
if len(log_seq) == 0:
continue
# if scale is not None:
# times = tim_seq
# for i, tn in enumerate(times):
# tn = np.array(tn).reshape(-1, 1)
# times[i] = scale.transform(tn).reshape(-1).tolist()
# tim_seq = times
log_seqs += log_seq
tim_seqs += tim_seq
# sort seq_pairs by seq len
log_seqs = np.array(log_seqs, dtype=object)
tim_seqs = np.array(tim_seqs, dtype=object)
test_len = list(map(len, log_seqs))
test_sort_index = np.argsort(-1 * np.array(test_len))
log_seqs = log_seqs[test_sort_index]
tim_seqs = tim_seqs[test_sort_index]
print(f"{file_name} size: {len(log_seqs)}")
return log_seqs, tim_seqs
def helper(self, model, output_dir, file_name, vocab, scale=None, error_dict=None):
total_results = []
total_errors = []
output_results = []
total_dist = []
output_cls = []
logkey_test, time_test = self.generate_test(output_dir, file_name, self.window_size, self.adaptive_window, self.seq_len, scale, self.min_len)
# use 1/10 test data
if self.test_ratio != 1:
num_test = len(logkey_test)
rand_index = torch.randperm(num_test)
rand_index = rand_index[:int(num_test * self.test_ratio)] if isinstance(self.test_ratio, float) else rand_index[:self.test_ratio]
logkey_test, time_test = logkey_test[rand_index], time_test[rand_index]
seq_dataset = LogDataset(logkey_test, time_test, vocab, seq_len=self.seq_len,
corpus_lines=self.corpus_lines, on_memory=self.on_memory, predict_mode=True, mask_ratio=self.mask_ratio)
# use large batch size in test data
data_loader = DataLoader(seq_dataset, batch_size=self.batch_size, num_workers=self.num_workers,
collate_fn=seq_dataset.collate_fn)
for idx, data in enumerate(data_loader):
data = {key: value.to(self.device) for key, value in data.items()}
result = model(data["bert_input"], data["time_input"])
# mask_lm_output, mask_tm_output: batch_size x session_size x vocab_size
# cls_output: batch_size x hidden_size
# bert_label, time_label: batch_size x session_size
# in session, some logkeys are masked
mask_lm_output, mask_tm_output = result["logkey_output"], result["time_output"]
output_cls += result["cls_output"].tolist()
# dist = torch.sum((result["cls_output"] - self.hyper_center) ** 2, dim=1)
# when visualization no mask
# continue
# loop though each session in batch
for i in range(len(data["bert_label"])):
seq_results = {"num_error": 0,
"undetected_tokens": 0,
"masked_tokens": 0,
"total_logkey": torch.sum(data["bert_input"][i] > 0).item(),
"deepSVDD_label": 0
}
mask_index = data["bert_label"][i] > 0
num_masked = torch.sum(mask_index).tolist()
seq_results["masked_tokens"] = num_masked
if self.is_logkey:
num_undetected, output_seq = self.detect_logkey_anomaly(
mask_lm_output[i][mask_index], data["bert_label"][i][mask_index])
seq_results["undetected_tokens"] = num_undetected
output_results.append(output_seq)
if self.hypersphere_loss_test:
# detect by deepSVDD distance
assert result["cls_output"][i].size() == self.center.size()
# dist = torch.sum((result["cls_fnn_output"][i] - self.center) ** 2)
dist = torch.sqrt(torch.sum((result["cls_output"][i] - self.center) ** 2))
total_dist.append(dist.item())
# user defined threshold for deepSVDD_label
seq_results["deepSVDD_label"] = int(dist.item() > self.radius)
#
# if dist > 0.25:
# pass
if idx < 10 or idx % 1000 == 0:
print(
"{}, #time anomaly: {} # of undetected_tokens: {}, # of masked_tokens: {} , "
"# of total logkey {}, deepSVDD_label: {} \n".format(
file_name,
seq_results["num_error"],
seq_results["undetected_tokens"],
seq_results["masked_tokens"],
seq_results["total_logkey"],
seq_results['deepSVDD_label']
)
)
total_results.append(seq_results)
# for time
# return total_results, total_errors
#for logkey
# return total_results, output_results
# for hypersphere distance
return total_results, output_cls
def predict(self):
model = torch.load(self.model_path, weights_only=False)
model.to(self.device)
model.eval()
print('model_path: {}'.format(self.model_path))
start_time = time.time()
vocab = WordVocab.load_vocab(self.vocab_path)
scale = None
error_dict = None
if self.is_time:
with open(self.scale_path, "rb") as f:
scale = pickle.load(f)
with open(self.model_dir + "error_dict.pkl", 'rb') as f:
error_dict = pickle.load(f)
if self.hypersphere_loss:
center_dict = torch.load(self.model_dir + "best_center.pt", weights_only=False)
self.center = center_dict["center"]
self.radius = center_dict["radius"]
# self.center = self.center.view(1,-1)
print("test normal predicting")
test_normal_results, test_normal_errors = self.helper(model, self.output_dir, "test_normal", vocab, scale, error_dict)
print("test abnormal predicting")
test_abnormal_results, test_abnormal_errors = self.helper(model, self.output_dir, "test_abnormal", vocab, scale, error_dict)
print("Saving test normal results")
with open(self.model_dir + "test_normal_results", "wb") as f:
pickle.dump(test_normal_results, f)
print("Saving test abnormal results")
with open(self.model_dir + "test_abnormal_results", "wb") as f:
pickle.dump(test_abnormal_results, f)
print("Saving test normal errors")
with open(self.model_dir + "test_normal_errors.pkl", "wb") as f:
pickle.dump(test_normal_errors, f)
print("Saving test abnormal results")
with open(self.model_dir + "test_abnormal_errors.pkl", "wb") as f:
pickle.dump(test_abnormal_errors, f)
params = {"is_logkey": self.is_logkey, "is_time": self.is_time, "hypersphere_loss": self.hypersphere_loss,
"hypersphere_loss_test": self.hypersphere_loss_test}
best_th, best_seq_th, FP, TP, TN, FN, P, R, F1 = find_best_threshold(test_normal_results,
test_abnormal_results,
params=params,
th_range=np.arange(10),
seq_range=np.arange(0,1,0.1))
print("best threshold: {}, best threshold ratio: {}".format(best_th, best_seq_th))
print("TP: {}, TN: {}, FP: {}, FN: {}".format(TP, TN, FP, FN))
print('Precision: {:.2f}%, Recall: {:.2f}%, F1-measure: {:.2f}%'.format(P, R, F1))
elapsed_time = time.time() - start_time
print('elapsed_time: {}'.format(elapsed_time))
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