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()
