rene
/
ma_cisco_malware
1
0
Fork 0
Code Issues Pull Requests Releases Wiki Activity

refactor dataset creation, split up functions

This commit is contained in:
René Knaebel 2017-09-08 17:11:13 +02:00
parent 528829bb33
commit edc75f4f44
3 changed files with 72 additions and 57 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

66
Makefile
View File

@ -1,37 +1,69 @@
run:
python3 main.py --mode train --train data/rk_mini.csv.gz --model results/test1 --epochs 2 --depth small \
--dense_embd 16 --domain_embd 8 --batch 64 --balanced_weights --type final
python3 main.py --mode train --train data/rk_mini.csv.gz --model results/test/test_both_1 --epochs 2 --depth small \
--filter_embd 32 --kernel_embd 3 --filter_main 16 --kernel_main 3 --dense_main 128 \
--dense_embd 16 --domain_embd 8 --batch 64 --balanced_weights --type final --model_output both
python3 main.py --mode train --train data/rk_mini.csv.gz --model results/test2 --epochs 2 --depth small \
--dense_embd 16 --domain_embd 8 --batch 64 --balanced_weights --type inter
python3 main.py --mode train --train data/rk_mini.csv.gz --model results/test/test_both_2 --epochs 2 --depth small \
--filter_embd 32 --kernel_embd 3 --filter_main 16 --kernel_main 3 --dense_main 128 \
--dense_embd 16 --domain_embd 8 --batch 64 --balanced_weights --type inter --model_output both
python3 main.py --mode train --train data/rk_mini.csv.gz --model results/test3 --epochs 2 --depth medium \
--dense_embd 16 --domain_embd 8 --batch 64 --balanced_weights --type final
python3 main.py --mode train --train data/rk_mini.csv.gz --model results/test/test_both_3 --epochs 2 --depth medium \
--filter_embd 32 --kernel_embd 3 --filter_main 16 --kernel_main 3 --dense_main 128 \
--dense_embd 16 --domain_embd 8 --batch 64 --balanced_weights --type final --model_output both
python3 main.py --mode train --train data/rk_mini.csv.gz --model results/test4 --epochs 2 --depth medium \
--dense_embd 16 --domain_embd 8 --batch 64 --balanced_weights --type inter
python3 main.py --mode train --train data/rk_mini.csv.gz --model results/test/test_both_4 --epochs 2 --depth medium \
--filter_embd 32 --kernel_embd 3 --filter_main 16 --kernel_main 3 --dense_main 128 \
--dense_embd 16 --domain_embd 8 --batch 64 --balanced_weights --type inter --model_output both
python3 main.py --mode train --train data/rk_mini.csv.gz --model results/test5 --epochs 2 --depth small \
--dense_embd 16 --domain_embd 8 --batch 64 --balanced_weights --type staggered
python3 main.py --mode train --train data/rk_mini.csv.gz --model results/test/test_both_5 --epochs 2 --depth small \
--filter_embd 32 --kernel_embd 3 --filter_main 16 --kernel_main 3 --dense_main 128 \
--dense_embd 16 --domain_embd 8 --batch 64 --balanced_weights --type staggered --model_output both
python3 main.py --mode train --train data/rk_mini.csv.gz --model results/test/test_client_1 --epochs 2 --depth small \
--filter_embd 32 --kernel_embd 3 --filter_main 16 --kernel_main 3 --dense_main 128 \
--dense_embd 16 --domain_embd 8 --batch 64 --balanced_weights --type final --model_output client
python3 main.py --mode train --train data/rk_mini.csv.gz --model results/test/test_client_2 --epochs 2 --depth small \
--filter_embd 32 --kernel_embd 3 --filter_main 16 --kernel_main 3 --dense_main 128 \
--dense_embd 16 --domain_embd 8 --batch 64 --balanced_weights --type inter --model_output client
python3 main.py --mode train --train data/rk_mini.csv.gz --model results/test/test_client_3 --epochs 2 --depth medium \
--filter_embd 32 --kernel_embd 3 --filter_main 16 --kernel_main 3 --dense_main 128 \
--dense_embd 16 --domain_embd 8 --batch 64 --balanced_weights --type final --model_output client
python3 main.py --mode train --train data/rk_mini.csv.gz --model results/test/test_client_4 --epochs 2 --depth medium \
--filter_embd 32 --kernel_embd 3 --filter_main 16 --kernel_main 3 --dense_main 128 \
--dense_embd 16 --domain_embd 8 --batch 64 --balanced_weights --type inter --model_output client
test:
python3 main.py --mode test --batch 128 --models results/test* --test data/rk_mini.csv.gz
python3 main.py --mode test --batch 128 --models results/test/test_both_* --test data/rk_mini.csv.gz --model_output both
python3 main.py --mode test --batch 128 --models results/test/test_client_* --test data/rk_mini.csv.gz --model_output client
fancy:
python3 main.py --mode fancy --batch 128 --model results/test1 --test data/rk_mini.csv.gz
python3 main.py --mode fancy --batch 128 --model results/test/test_both_1 --test data/rk_mini.csv.gz
python3 main.py --mode fancy --batch 128 --model results/test2 --test data/rk_mini.csv.gz
python3 main.py --mode fancy --batch 128 --model results/test/test_both_2 --test data/rk_mini.csv.gz
python3 main.py --mode fancy --batch 128 --model results/test3 --test data/rk_mini.csv.gz
python3 main.py --mode fancy --batch 128 --model results/test/test_both_3 --test data/rk_mini.csv.gz
python3 main.py --mode fancy --batch 128 --model results/test4 --test data/rk_mini.csv.gz
python3 main.py --mode fancy --batch 128 --model results/test/test_both_4 --test data/rk_mini.csv.gz
python3 main.py --mode fancy --batch 128 --model results/test/test_both_5 --test data/rk_mini.csv.gz
python3 main.py --mode fancy --batch 128 --model results/test/test_client_1 --test data/rk_mini.csv.gz
python3 main.py --mode fancy --batch 128 --model results/test/test_client_2 --test data/rk_mini.csv.gz
python3 main.py --mode fancy --batch 128 --model results/test/test_client_3 --test data/rk_mini.csv.gz
python3 main.py --mode fancy --batch 128 --model results/test/test_client_4 --test data/rk_mini.csv.gz
all-fancy:
python3 main.py --mode all_fancy --batch 128 --models results/test* --test data/rk_mini.csv.gz
python3 main.py --mode all_fancy --batch 128 --models results/test/test* --test data/rk_mini.csv.gz
hyper:
python3 main.py --mode hyperband --batch 64 --train data/rk_data.csv.gz
clean:
rm -r results/test*
rm -r results/test/test*
rm data/rk_mini.csv.gz.h5

61
dataset.py
View File

@ -99,18 +99,13 @@ def get_all_flow_features(features):
def create_dataset_from_flows(user_flow_df, char_dict, max_len, window_size=10):
logger.info("get chunks from user data frames")
with Pool() as pool:
results = []
for user_flow in tqdm(get_flow_per_user(user_flow_df), total=len(user_flow_df['user_hash'].unique().tolist())):
results.append(pool.apply_async(get_user_chunks, (user_flow, window_size)))
windows = [window for res in results for window in res.get()]
logger.info("create training dataset")
domain, flow, hits, names, server, trusted_hits = create_dataset_from_lists(chunks=windows,
vocab=char_dict,
max_len=max_len)
# make client labels discrete with 4 different values
hits = np.apply_along_axis(lambda x: discretize_label(x, 3), 0, np.atleast_2d(hits))
domain, flow, name, hits, trusted_hits, server = create_raw_dataset_from_flows(user_flow_df, char_dict,
max_len, window_size)
domain, flow, name, client, server = filter_window_dataset_by_hits(domain, flow, name, hits, trusted_hits, server)
return domain, flow, name, client, server
def filter_window_dataset_by_hits(domain, flow, name, hits, trusted_hits, server):
# select only 1.0 and 0.0 from training data
pos_idx = np.where(np.logical_or(hits == 1.0, trusted_hits >= 1.0))[0]
neg_idx = np.where(hits == 0.0)[0]
@ -118,15 +113,15 @@ def create_dataset_from_flows(user_flow_df, char_dict, max_len, window_size=10):
# choose selected sample to train on
domain = domain[idx]
flow = flow[idx]
client_tr = np.zeros_like(idx, float)
client_tr[:pos_idx.shape[-1]] = 1.0
client = np.zeros_like(idx, float)
client[:pos_idx.shape[-1]] = 1.0
server = server[idx]
names = names[idx]
name = name[idx]
return domain, flow, names, client_tr, server
return domain, flow, name, client, server
def create_testset_from_flows(user_flow_df, char_dict, max_len, window_size=10):
def create_raw_dataset_from_flows(user_flow_df, char_dict, max_len, window_size=10):
logger.info("get chunks from user data frames")
with Pool() as pool:
results = []
@ -134,24 +129,13 @@ def create_testset_from_flows(user_flow_df, char_dict, max_len, window_size=10):
results.append(pool.apply_async(get_user_chunks, (user_flow, window_size)))
windows = [window for res in results for window in res.get()]
logger.info("create training dataset")
domain, flow, hits, names, server, trusted_hits = create_dataset_from_lists(chunks=windows,
vocab=char_dict,
max_len=max_len)
domain, flow, hits, name, server, trusted_hits = create_dataset_from_windows(chunks=windows,
vocab=char_dict,
max_len=max_len)
# make client labels discrete with 4 different values
hits = np.apply_along_axis(lambda x: discretize_label(x, 3), 0, np.atleast_2d(hits))
# select only 1.0 and 0.0 from training data
pos_idx = np.where(np.logical_or(hits == 1.0, trusted_hits >= 1.0))[0]
neg_idx = np.where(hits == 0.0)[0]
idx = np.concatenate((pos_idx, neg_idx))
# choose selected sample to train on
domain = domain[idx]
flow = flow[idx]
client_tr = np.zeros_like(idx, float)
client_tr[:pos_idx.shape[-1]] = 1.0
server = server[idx]
names = names[idx]
hits = np.apply_along_axis(lambda x: make_label_discrete(x, 3), 0, np.atleast_2d(hits))
return domain, flow, names, client_tr, server
return domain, flow, name, hits, trusted_hits, server
def store_h5dataset(path, data: dict):
@ -163,14 +147,13 @@ def store_h5dataset(path, data: dict):
def load_h5dataset(path):
f = h5py.File(path, "r")
keys = f.keys()
data = {}
for k in keys:
for k in f.keys():
data[k] = f[k]
return data
def create_dataset_from_lists(chunks, vocab, max_len):
def create_dataset_from_windows(chunks, vocab, max_len):
"""
combines domain and feature windows to sequential training data
:param chunks: list of flow feature windows
@ -204,7 +187,7 @@ def create_dataset_from_lists(chunks, vocab, max_len):
hits, names, servers, trusted_hits)
def discretize_label(values, threshold):
def make_label_discrete(values, threshold):
max_val = np.max(values)
if max_val >= threshold:
return 1.0
@ -251,8 +234,8 @@ def load_or_generate_h5data(h5data, train_data, domain_length, window_size):
user_flow_df = get_user_flow_data(train_data)
logger.info("create training dataset")
domain, flow, name, client, server = create_dataset_from_flows(user_flow_df, char_dict,
max_len=domain_length,
window_size=window_size)
max_len=domain_length,
window_size=window_size)
logger.info("store training dataset as h5 file")
data = {
"domain": domain.astype(np.int8),

2
main.py
View File

@ -275,7 +275,7 @@ def main_test():
# np.save(model_args["model_path"] + "/domain_embds.npy", domain_embeddings)
results["domain_embds"] = domain_embeddings
joblib.dump(results, model_args["model_path"] + "results.joblib", compress=3)
joblib.dump(results, model_args["model_path"] + "/results.joblib", compress=3)
def main_visualization():