def __init__(self,
db_name,
feature_type,
dataset_type,
json_asm2id,
max_instructions,
max_num_vertices,
encoder,
batch_size,
flags=None):
self.db_name = db_name
self.feature_type = feature_type
self.dataset_type = dataset_type
self.encoder = encoder
self.max_instructions = max_instructions
self.max_num_vertices = max_num_vertices
self.batch_dim = 0
self.num_pairs = 0
self.num_batches = 0
self.flags = flags
self.converter = InstructionsConverter(json_asm2id)
self.normalizer = FunctionNormalizer(self.max_instructions)
conn = sqlite3.connect(self.db_name)
cur = conn.cursor()
q = cur.execute("SELECT count(*) from " + self.dataset_type)
count = int(q.fetchone()[0])
n_chunk = int(count / batch_size) - 1
self.num_batches = n_chunk
conn.close()
def __init__(self, db_name, feature_type, dataset_type, json_asm2id,
max_instructions, max_num_vertices):
self.db_name = db_name
self.feature_type = feature_type
self.dataset_type = dataset_type
self.max_instructions = max_instructions
self.max_num_vertices = max_num_vertices
self.batch_dim = 0
self.num_pairs = 0
self.num_batches = 0
self.converter = InstructionsConverter(json_asm2id)
self.normalizer = FunctionNormalizer(self.max_instructions)
class SAFE:
def __init__(self, model):
self.converter = InstructionsConverter("data/i2v/word2id.json")
self.normalizer = FunctionNormalizer(max_instruction=150)
self.embedder = SAFEEmbedder(model)
self.embedder.loadmodel()
self.embedder.get_tensor()
def embedd_function(self, filename, address):
analyzer = RadareFunctionAnalyzer(filename, use_symbol=False, depth=0)
functions = analyzer.analyze()
instructions_list = None
for function in functions:
if functions[function]['address'] == address:
instructions_list = functions[function][
'filtered_instructions']
break
if instructions_list is None:
print("Function not found")
return None
converted_instructions = self.converter.convert_to_ids(
instructions_list)
instructions, length = self.normalizer.normalize_functions(
[converted_instructions])
embedding = self.embedder.embedd(instructions, length)
return embedding
class FunctionsEmbedder:
def __init__(self, model, batch_size, max_instruction):
self.batch_size = batch_size
self.normalizer = FunctionNormalizer(max_instruction)
self.safe = SAFEEmbedder(model)
self.safe.loadmodel()
self.safe.get_tensor()
def compute_embeddings(self, functions):
functions, lenghts = self.normalizer.normalize_functions(functions)
embeddings = self.safe.embedd(functions, lenghts)
return embeddings
@staticmethod
def create_table(db_name, table_name):
conn = sqlite3.connect(db_name)
c = conn.cursor()
c.execute(
"CREATE TABLE IF NOT EXISTS {} (id INTEGER PRIMARY KEY, {} TEXT)".
format(table_name, table_name))
conn.commit()
conn.close()
def compute_and_save_embeddings_from_db(self, db_name, table_name):
FunctionsEmbedder.create_table(db_name, table_name)
conn = sqlite3.connect(db_name)
cur = conn.cursor()
q = cur.execute(
"SELECT id FROM functions WHERE id not in (SELECT id from {})".
format(table_name))
ids = q.fetchall()
for i in tqdm(range(0, len(ids), self.batch_size)):
functions = []
batch_ids = ids[i:i + self.batch_size]
for my_id in batch_ids:
q = cur.execute(
"SELECT instructions_list FROM filtered_functions where id=?",
my_id)
functions.append(json.loads(q.fetchone()[0]))
embeddings = self.compute_embeddings(functions)
for l, id in enumerate(batch_ids):
cur.execute("INSERT INTO {} VALUES (?,?)".format(table_name),
(id[0], np.array2string(embeddings[l])))
conn.commit()
class PairFactory(DatasetGenerator):
def __init__(self,
db_name,
feature_type,
dataset_type,
json_asm2id,
max_instructions,
max_num_vertices,
encoder,
batch_size,
flags=None):
self.db_name = db_name
self.feature_type = feature_type
self.dataset_type = dataset_type
self.encoder = encoder
self.max_instructions = max_instructions
self.max_num_vertices = max_num_vertices
self.batch_dim = 0
self.num_pairs = 0
self.num_batches = 0
self.flags = flags
self.converter = InstructionsConverter(json_asm2id)
self.normalizer = FunctionNormalizer(self.max_instructions)
conn = sqlite3.connect(self.db_name)
cur = conn.cursor()
q = cur.execute("SELECT count(*) from " + self.dataset_type)
count = int(q.fetchone()[0])
n_chunk = int(count / batch_size) - 1
self.num_batches = n_chunk
conn.close()
def remove_bad_acfg_node(self, g):
nodeToRemove = []
for n in g.nodes(data=True):
f = n[1]['features']
if len(f.keys()) == 0:
nodeToRemove.append(n[0])
for n in nodeToRemove:
g.remove_node(n)
return g
def split(self, a, n):
return [a[i::n] for i in range(n)]
def get_node_matrix(self, nodes):
if isinstance(nodes, int):
print(nodes)
num_node = len(nodes)
node_matrix = np.zeros([num_node, 8])
for i, n in enumerate(nodes):
f = n[1]['features']
if isinstance(f['constant'], int):
node_matrix[i, 0] = f['constant']
else:
node_matrix[i, 0] = len(f['constant'])
if isinstance(f['string'], int):
node_matrix[i, 1] = f['string']
else:
node_matrix[i, 1] = len(f['string'])
node_matrix[i, 2] = f['transfer']
node_matrix[i, 3] = f['call']
node_matrix[i, 4] = f['instruction']
node_matrix[i, 5] = f['arith']
node_matrix[i, 6] = f['offspring']
node_matrix[i, 7] = f['betweenness']
return node_matrix
def get_data_from_acfg(self, g):
g = self.remove_bad_acfg_node(g)
if len(g.nodes) > 0:
adj = nx.adjacency_matrix(g)
node_matrix = self.get_node_matrix(g.nodes(data=True))
else:
adj = sparse.bsr_matrix(np.zeros([1, 1]))
node_matrix = np.zeros([1, 8])
lenghts = [8] * len(node_matrix)
return adj, node_matrix, lenghts
def get_data_from_cfg(self, cfg):
adj = sparse.csr_matrix([1, 1])
lenghts = []
node_matrix = []
try:
adj = nx.adjacency_matrix(cfg)
nodes = cfg.nodes(data=True)
for i, n in enumerate(nodes):
filtered = self.converter.convert_to_ids(n[1]['features'])
lenghts.append(len(filtered))
node_matrix.append(self.normalizer.normalize(filtered)[0])
except:
pass
return adj, node_matrix, lenghts
def async_chunker(self, epoch, batch_size, shuffle=True):
self.num_pairs = 0
conn = sqlite3.connect(self.db_name)
cur = conn.cursor()
q = cur.execute("SELECT id from " + self.dataset_type)
ids = q.fetchall()
ids = [ii[0] for ii in ids]
data_len = len(ids)
n_chunk = int(data_len / batch_size) - 1
random.seed(17)
self.num_batches = n_chunk
lista_chunk = range(0, n_chunk)
coda = Queue(maxsize=50)
n_proc = 10
listone = self.split(lista_chunk, n_proc)
for i in range(0, n_proc):
l = list(listone[i])
p = Process(target=self.async_create_pair,
args=((epoch, l, batch_size, coda, shuffle,
self.encoder)))
p.start()
while coda.empty():
pass
for i in range(0, n_chunk):
yield self.async_get_dataset(i, n_chunk, batch_size, coda, shuffle)
def get_pair_from_db(self, epoch_number, chunk, number_of_functions,
label_encoder):
conn = sqlite3.connect(self.db_name)
cur = conn.cursor()
functions = []
labels = []
lenghts = []
q = cur.execute("SELECT id FROM " + self.dataset_type)
ids = q.fetchall()
rng = random.Random(epoch_number)
rng.shuffle(ids)
data_len = len(ids)
i = 0
while i < number_of_functions:
if chunk * int(number_of_functions) + i > data_len:
break
ii = ids[chunk * int(number_of_functions) + i]
q = cur.execute(
"SELECT " + self.feature_type + " FROM " + self.feature_type +
" WHERE id=?", ii)
if self.feature_type == 'acfg':
adj, node, lenghts0 = self.get_data_from_acfg(
json_graph.adjacency_graph(json.loads(q.fetchone()[0])))
elif self.feature_type == 'lstm_cfg':
adj, node, lenghts0 = self.get_data_from_cfg(
json_graph.adjacency_graph(json.loads(q.fetchone()[0])))
functions.append([(adj, node)])
lenghts.append(lenghts0)
if self.flags is None or self.flags.class_kind == "CMP" or self.flags.class_kind == "FML":
query_str = "SELECT compiler FROM functions WHERE id=?"
elif self.flags.class_kind == "CMPOPT":
query_str = "SELECT compiler,optimization FROM functions WHERE id=?"
elif self.flags.class_kind == "OPT":
query_str = "SELECT optimization FROM functions WHERE id=?"
q = cur.execute(query_str, ii)
q_compiler = q.fetchone()
if self.flags.class_kind == "CMPOPT":
compiler = q_compiler[0] + '-' + q_compiler[1]
elif self.flags.class_kind == "FML":
compiler = str(q_compiler[0]).split('-')[0]
else:
compiler = q_compiler[0]
encoded = label_encoder.transform([compiler])
labels.append(encoded)
i += 1
if self.feature_type == 'acfg':
pairs, labels, output_len = padAndFilter(functions, labels,
[[[1]]] * len(functions),
self.max_num_vertices)
output_len = [[1]]
elif self.feature_type == 'lstm_cfg':
pairs, labels, output_len = padAndFilter(functions, labels,
lenghts,
self.max_num_vertices)
return pairs, labels, output_len
def async_create_pair(self, epoch, n_chunk, number_of_functions, q,
shuffle, encoder):
for i in n_chunk:
pairs, y, lenghts = self.get_pair_from_db(epoch, i,
number_of_functions,
encoder)
assert (len(pairs) == len(y))
n_samples = len(pairs)
len1 = []
for l in lenghts:
len1.append(l[0])
adj1 = []
nodes1 = []
for p in pairs:
adj1.append(p[0][0])
nodes1.append(p[0][1])
y_ = []
for yy in y:
y_.append(yy[0])
for i in range(0, n_samples, number_of_functions):
upper_bound = min(i + number_of_functions, n_samples)
ret_adj = adj1[i:upper_bound]
ret_nodes = nodes1[i:upper_bound]
ret_len = len1[i:upper_bound]
ret_y = y_[i:upper_bound]
q.put((ret_adj, ret_nodes, ret_y, ret_len), block=True)
def async_get_dataset(self, chunk, n_chunk, number_of_pairs, q, shuffle):
item = q.get()
n_samples = len(item[0])
self.batch_dim = n_samples
self.num_pairs += n_samples
return item[0], item[1], item[2], item[3]
def get_couple_from_db(self, epoch_number, chunk):
conn = sqlite3.connect(self.db_name)
cur = conn.cursor()
pairs = []
labels = []
q = cur.execute(
"SELECT true_pair, false_pair from " + self.dataset_type +
" WHERE id=?", (int(epoch_number), ))
true_pairs_id, false_pairs_id = q.fetchone()
true_pairs_id = json.loads(true_pairs_id)
false_pairs_id = json.loads(false_pairs_id)
conn.close()
data_len = len(true_pairs_id)
i = 0
normalizer = FunctionNormalizer(self.max_instructions)
while i < self.batch_size:
if chunk * int(self.batch_size / 2) + i > data_len:
break
p = true_pairs_id[chunk * int(self.batch_size / 2) + i]
f0, f1 = self.get_pair_fromdb(p[0], p[1])
pairs.append((f0, f1))
labels.append(+1)
p = false_pairs_id[chunk * int(self.batch_size / 2) + i]
f0, f1 = self.get_pair_fromdb(p[0], p[1])
pairs.append((f0, f1))
labels.append(-1)
i += 2
pairs, lengths = normalizer.normalize_function_pairs(pairs)
function1, function2 = zip(*pairs)
len1, len2 = zip(*lengths)
n_samples = len(pairs)
if self.shuffle:
shuffle_indices = np.random.permutation(np.arange(n_samples))
function1 = np.array(function1)[shuffle_indices]
function2 = np.array(function2)[shuffle_indices]
len1 = np.array(len1)[shuffle_indices]
len2 = np.array(len2)[shuffle_indices]
labels = np.array(labels)[shuffle_indices]
else:
function1 = np.array(function1)
function2 = np.array(function2)
len1 = np.array(len1)
len2 = np.array(len2)
labels = np.array(labels)
upper_bound = min(self.batch_size, n_samples)
len1 = len1[0:upper_bound]
len2 = len2[0:upper_bound]
function1 = function1[0:upper_bound]
function2 = function2[0:upper_bound]
y_ = labels[0:upper_bound]
return function1, function2, len1, len2, y_
def __init__(self, model):
self.converter = InstructionsConverter("data/i2v/word2id.json")
self.normalizer = FunctionNormalizer(max_instruction=150)
self.embedder = SAFEEmbedder(model)
self.embedder.loadmodel()
self.embedder.get_tensor()
def __init__(self, model, batch_size, max_instruction):
self.batch_size = batch_size
self.normalizer = FunctionNormalizer(max_instruction)
self.safe = SAFEEmbedder(model)
self.safe.loadmodel()
self.safe.get_tensor()
class PairFactory(DatasetGenerator):
def __init__(self, db_name, feature_type, dataset_type, json_asm2id,
max_instructions, max_num_vertices):
self.db_name = db_name
self.feature_type = feature_type
self.dataset_type = dataset_type
self.max_instructions = max_instructions
self.max_num_vertices = max_num_vertices
self.batch_dim = 0
self.num_pairs = 0
self.num_batches = 0
self.converter = InstructionsConverter(json_asm2id)
self.normalizer = FunctionNormalizer(self.max_instructions)
def get_data_from_cfg(self, cfg):
adj = sparse.csr_matrix([1, 1])
lenghts = []
node_matrix = []
try:
adj = nx.adjacency_matrix(cfg)
nodes = cfg.nodes(data=True)
for i, n in enumerate(nodes):
filtered = self.converter.convert_to_ids(n[1]['features'])
lenghts.append(len(filtered))
node_matrix.append(self.normalizer.normalize(filtered)[0])
except:
pass
return adj, node_matrix, lenghts
def remove_bad_acfg_node(self, g):
nodeToRemove = []
for n in g.nodes(data=True):
f = n[1]['features']
if len(f.keys()) == 0:
nodeToRemove.append(n[0])
for n in nodeToRemove:
g.remove_node(n)
return g
def get_node_matrix(self, nodes):
num_node = len(nodes)
node_matrix = np.zeros([num_node, 8])
for i, n in enumerate(nodes):
f = n[1]['features']
if isinstance(f['constant'], list):
node_matrix[i, 0] = len(f['constant'])
else:
node_matrix[i, 0] = f['constant']
if isinstance(f['string'], list):
node_matrix[i, 1] = len(f['string'])
else:
node_matrix[i, 1] = f['string']
node_matrix[i, 2] = f['transfer']
node_matrix[i, 3] = f['call']
node_matrix[i, 4] = f['instruction']
node_matrix[i, 5] = f['arith']
node_matrix[i, 6] = f['offspring']
node_matrix[i, 7] = f['betweenness']
return node_matrix
def get_data_from_acfg(self, g):
g = self.remove_bad_acfg_node(g)
if len(g.nodes) > 0:
adj = nx.adjacency_matrix(g)
node_matrix = self.get_node_matrix(g.nodes(data=True))
else:
adj = sparse.bsr_matrix(np.zeros([1, 1]))
node_matrix = np.zeros([1, 8])
lenght = 8
return adj, node_matrix, lenght
def async_chunker(self, epoch, number_of_pairs, shuffle=True):
self.num_pairs = 0
conn = sqlite3.connect(self.db_name)
cur = conn.cursor()
q = cur.execute(
"SELECT true_pair, false_pair from " + self.dataset_type +
" WHERE id=?", (int(epoch), ))
true_pairs_id, false_pairs_id = q.fetchone()
true_pairs_id = json.loads(true_pairs_id)
false_pairs_id = json.loads(false_pairs_id)
assert len(true_pairs_id) == len(false_pairs_id)
data_len = len(true_pairs_id)
print("Data Len: " + str(data_len))
conn.close()
n_chunk = int(data_len / (number_of_pairs / 2)) - 1
self.num_batches = n_chunk
q = Queue(maxsize=50)
t = Thread(target=self.async_create_pairs,
args=(epoch, n_chunk, number_of_pairs, q))
t.start()
for i in range(0, n_chunk):
yield self.async_get_dataset(i, n_chunk, number_of_pairs, q,
shuffle)
def get_pair_from_db(self, epoch_number, chunk, number_of_pairs):
conn = sqlite3.connect(self.db_name)
cur = conn.cursor()
pairs = []
labels = []
lenghts = []
q = cur.execute(
"SELECT true_pair, false_pair from " + self.dataset_type +
" WHERE id=?", (int(epoch_number), ))
true_pairs_id, false_pairs_id = q.fetchone()
true_pairs_id = json.loads(true_pairs_id)
false_pairs_id = json.loads(false_pairs_id)
data_len = len(true_pairs_id)
i = 0
while i < number_of_pairs:
if chunk * int(number_of_pairs / 2) + i > data_len:
break
p = true_pairs_id[chunk * int(number_of_pairs / 2) + i]
q0 = cur.execute(
"SELECT " + self.feature_type + " FROM " + self.feature_type +
" WHERE id=?", (p[0], ))
if self.feature_type == 'acfg':
adj0, node0, lenghts0 = self.get_data_from_acfg(
json_graph.adjacency_graph(json.loads(q0.fetchone()[0])))
elif self.feature_type == 'lstm_cfg':
adj0, node0, lenghts0 = self.get_data_from_cfg(
json_graph.adjacency_graph(json.loads(q0.fetchone()[0])))
q1 = cur.execute(
"SELECT " + self.feature_type + " FROM " + self.feature_type +
" WHERE id=?", (p[1], ))
if self.feature_type == 'acfg':
adj1, node1, lenghts1 = self.get_data_from_acfg(
json_graph.adjacency_graph(json.loads(q0.fetchone()[0])))
elif self.feature_type == 'lstm_cfg':
adj1, node1, lenghts1 = self.get_data_from_cfg(
json_graph.adjacency_graph(json.loads(q1.fetchone()[0])))
pairs.append(((adj0, node0), (adj1, node1)))
lenghts.append([lenghts0, lenghts1])
labels.append(+1)
p = false_pairs_id[chunk * int(number_of_pairs / 2) + i]
q0 = cur.execute(
"SELECT " + self.feature_type + " FROM " + self.feature_type +
" WHERE id=?", (p[0], ))
if self.feature_type == 'acfg':
adj0, node0, lenghts0 = self.get_data_from_acfg(
json_graph.adjacency_graph(json.loads(q0.fetchone()[0])))
elif self.feature_type == 'lstm_cfg':
adj0, node0, lenghts0 = self.get_data_from_cfg(
json_graph.adjacency_graph(json.loads(q0.fetchone()[0])))
q1 = cur.execute(
"SELECT " + self.feature_type + " FROM " + self.feature_type +
" WHERE id=?", (p[1], ))
if self.feature_type == 'acfg':
adj1, node1, lenghts1 = self.get_data_from_acfg(
json_graph.adjacency_graph(json.loads(q0.fetchone()[0])))
elif self.feature_type == 'lstm_cfg':
adj1, node1, lenghts1 = self.get_data_from_cfg(
json_graph.adjacency_graph(json.loads(q1.fetchone()[0])))
pairs.append(((adj0, node0), (adj1, node1)))
lenghts.append([lenghts0, lenghts1])
labels.append(-1)
i += 2
if self.feature_type == 'acfg':
pairs, labels, output_len = padAndFilter(pairs, labels,
self.max_num_vertices)
elif self.feature_type == 'lstm_cfg':
pairs, labels, output_len = padAndFilterLSTM(
pairs, labels, lenghts, self.max_num_vertices)
return pairs, labels, output_len
def async_create_pairs(self, epoch, n_chunk, number_of_pairs, q):
for i in range(0, n_chunk):
pairs, y_, lenghts = self.get_pair_from_db(epoch, i,
number_of_pairs)
q.put((pairs, y_, lenghts), block=True)
def async_get_dataset(self, chunk, n_chunk, number_of_pairs, q, shuffle):
item = q.get()
pairs = item[0]
y_ = item[1]
lenghts = item[2]
assert (len(pairs) == len(y_))
n_samples = len(pairs)
self.batch_dim = n_samples
self.num_pairs += n_samples
# Unpack the list
graph1, graph2 = zip(*pairs)
len1, len2 = zip(*lenghts)
adj1, nodes1 = zip(*graph1)
adj2, nodes2 = zip(*graph2)
if shuffle:
shuffle_indices = np.random.permutation(np.arange(n_samples))
adj1 = np.array(adj1)[shuffle_indices]
nodes1 = np.array(nodes1)[shuffle_indices]
adj2 = np.array(adj2)[shuffle_indices]
nodes2 = np.array(nodes2)[shuffle_indices]
y_ = np.array(y_)[shuffle_indices]
for i in range(0, n_samples, number_of_pairs):
upper_bound = min(i + number_of_pairs, n_samples)
ret_adj1 = adj1[i:upper_bound]
ret_nodes1 = nodes1[i:upper_bound]
ret_len1 = len1[i:upper_bound]
ret_adj2 = adj2[i:upper_bound]
ret_nodes2 = nodes2[i:upper_bound]
ret_len2 = len2[i:upper_bound]
ret_y = y_[i:upper_bound]
return ret_adj1, ret_nodes1, ret_adj2, ret_nodes2, ret_y, ret_len1, ret_len2
