rene/ma_cisco_malware
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change argument interface

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- add more properties for network specification
 - change names for consistency
This commit is contained in:
René Knaebel 2017-09-07 15:53:58 +02:00
parent 71f218888d
commit 595c2ea894
5 changed files with 42 additions and 24 deletions
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10
Makefile
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@ -1,18 +1,18 @@
run: run:
python3 main.py --mode train --train data/rk_mini.csv.gz --model results/test1 --epochs 2 --depth small \ python3 main.py --mode train --train data/rk_mini.csv.gz --model results/test1 --epochs 2 --depth small \
--hidden_char_dims 16 --domain_embd 8 --batch 64 --balanced_weights --type final --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/test2 --epochs 2 --depth small \ python3 main.py --mode train --train data/rk_mini.csv.gz --model results/test2 --epochs 2 --depth small \
--hidden_char_dims 16 --domain_embd 8 --batch 64 --balanced_weights --type inter --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/test3 --epochs 2 --depth medium \ python3 main.py --mode train --train data/rk_mini.csv.gz --model results/test3 --epochs 2 --depth medium \
--hidden_char_dims 16 --domain_embd 8 --batch 64 --balanced_weights --type final --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/test4 --epochs 2 --depth medium \ python3 main.py --mode train --train data/rk_mini.csv.gz --model results/test4 --epochs 2 --depth medium \
--hidden_char_dims 16 --domain_embd 8 --batch 64 --balanced_weights --type inter --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/test5 --epochs 2 --depth small \ python3 main.py --mode train --train data/rk_mini.csv.gz --model results/test5 --epochs 2 --depth small \
--hidden_char_dims 16 --domain_embd 8 --batch 64 --balanced_weights --type staggered --dense_embd 16 --domain_embd 8 --batch 64 --balanced_weights --type staggered
test: 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 data/rk_mini.csv.gz

20
arguments.py
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@ -46,8 +46,24 @@ parser.add_argument("--epochs", action="store", dest="epochs",
parser.add_argument("--embd", action="store", dest="embedding", parser.add_argument("--embd", action="store", dest="embedding",
default=128, type=int) default=128, type=int)
parser.add_argument("--hidden_char_dims", action="store", dest="hidden_char_dims", parser.add_argument("--filter_embd", action="store", dest="filter_embedding",
default=256, type=int) default=128, type=int)
parser.add_argument("--dense_embd", action="store", dest="dense_embedding",
default=128, type=int)
parser.add_argument("--kernel_embd", action="store", dest="kernel_embedding",
default=3, type=int)
parser.add_argument("--filter_main", action="store", dest="filter_main",
default=128, type=int)
parser.add_argument("--dense_main", action="store", dest="dense_main",
default=128, type=int)
parser.add_argument("--kernel_main", action="store", dest="kernel_main",
default=3, type=int)
parser.add_argument("--window", action="store", dest="window", parser.add_argument("--window", action="store", dest="window",
default=10, type=int) default=10, type=int)

16
main.py
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@ -58,21 +58,21 @@ if args.gpu:
PARAMS = { PARAMS = {
"type": args.model_type, "type": args.model_type,
"depth": args.model_depth, "depth": args.model_depth,
"batch_size": 64, # "batch_size": 64,
"window_size": args.window, "window_size": args.window,
"domain_length": args.domain_length, "domain_length": args.domain_length,
"flow_features": 3, "flow_features": 3,
# #
'dropout': 0.5, 'dropout': 0.5, # currently fix
'domain_features': args.domain_embedding, 'domain_features': args.domain_embedding,
'embedding_size': args.embedding, 'embedding_size': args.embedding,
'flow_features': 3, 'flow_features': 3,
'filter_embedding': args.hidden_char_dims, 'filter_embedding': args.filter_embedding,
'hidden_embedding': args.domain_embedding, 'dense_embedding': args.dense_embedding,
'kernel_embedding': 3, 'kernel_embedding': args.kernel_embedding,
'filter_main': 128, 'filter_main': args.filter_main,
'dense_main': 128, 'dense_main': args.dense_main,
'kernels_main': 3, 'kernel_main': args.kernel_main,
'input_length': 40, 'input_length': 40,
'model_output': args.model_output 'model_output': args.model_output
} }

10
models/__init__.py
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@ -13,7 +13,7 @@ def get_models_by_params(params: dict):
input_length = params.get("input_length") input_length = params.get("input_length")
filter_embedding = params.get("filter_embedding") filter_embedding = params.get("filter_embedding")
kernel_embedding = params.get("kernel_embedding") kernel_embedding = params.get("kernel_embedding")
hidden_embedding = params.get("hidden_embedding") hidden_embedding = params.get("dense_embedding")
dropout = params.get("dropout") dropout = params.get("dropout")
# mainly prediction model # mainly prediction model
flow_features = params.get("flow_features") flow_features = params.get("flow_features")
@ -21,7 +21,7 @@ def get_models_by_params(params: dict):
window_size = params.get("window_size") window_size = params.get("window_size")
domain_length = params.get("domain_length") domain_length = params.get("domain_length")
filter_main = params.get("filter_main") filter_main = params.get("filter_main")
kernel_main = params.get("kernels_main") kernel_main = params.get("kernel_main")
dense_dim = params.get("dense_main") dense_dim = params.get("dense_main")
model_output = params.get("model_output", "both") model_output = params.get("model_output", "both")
# create models # create models
@ -32,12 +32,12 @@ def get_models_by_params(params: dict):
else: else:
raise Exception("network not found") raise Exception("network not found")
embedding_model = networks.get_embedding(embedding_size, input_length, filter_embedding, kernel_embedding, embedding_model = networks.get_embedding(embedding_size, input_length, filter_embedding, kernel_embedding,
hidden_embedding, dropout) hidden_embedding, 0.5)
old_model = networks.get_model(dropout, flow_features, domain_features, window_size, domain_length, old_model = networks.get_model(0.25, flow_features, domain_features, window_size, domain_length,
filter_main, kernel_main, dense_dim, embedding_model, model_output) filter_main, kernel_main, dense_dim, embedding_model, model_output)
new_model = networks.get_new_model(dropout, flow_features, domain_features, window_size, domain_length, new_model = networks.get_new_model(0.25, flow_features, domain_features, window_size, domain_length,
filter_main, kernel_main, dense_dim, embedding_model, model_output) filter_main, kernel_main, dense_dim, embedding_model, model_output)
return embedding_model, old_model, new_model return embedding_model, old_model, new_model

10
run.sh
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@ -16,9 +16,10 @@ do
--train ${DATADIR}/currentData.csv \ --train ${DATADIR}/currentData.csv \
--model ${RESDIR}/${output}_${depth}_${mtype} \ --model ${RESDIR}/${output}_${depth}_${mtype} \
--epochs 50 \ --epochs 50 \
--embd 64 \ --embd 128 \
--hidden_char_dims 128 \ --filter_embd 256 --kernel_embd 8 --dense_embd 128 \
--domain_embd 32 \ --domain_embd 32 \
--filter_main 32 --kernel_main 8 --dense_main 1024 \
--batch 256 \ --batch 256 \
--balanced_weights \ --balanced_weights \
--model_output ${output} \ --model_output ${output} \
@ -35,9 +36,10 @@ do
--train ${DATADIR}/currentData.csv \ --train ${DATADIR}/currentData.csv \
--model ${RESDIR}/both_${depth}_inter \ --model ${RESDIR}/both_${depth}_inter \
--epochs 50 \ --epochs 50 \
--embd 64 \ --embd 128 \
--hidden_char_dims 128 \ --filter_embd 256 --kernel_embd 8 --dense_embd 128 \
--domain_embd 32 \ --domain_embd 32 \
--filter_main 32 --kernel_main 8 --dense_main 1024 \
--batch 256 \ --batch 256 \
--balanced_weights \ --balanced_weights \
--model_output both \ --model_output both \