def async_get_funcs(file_name, use_ghidra=True):
if use_ghidra:
ret_list = gh.get_function_information(file_name)
else:
ret_list = fh.get_function_information(file_name)
for func in ret_list:
func['file_name'] = file_name
return ret_list
def get_sparse_file_data(file_name):
info = get_function_information(file_name)
#arg_info = get_arg_funcs(file_item)
#trimmed = trim_funcs(arg_info)
if info:
trimmed = trim_funcs(info, file_name=file_name)
else:
trimmed = []
return trimmed
def get_vulnerabilities(file_name, ld_path):
print("[+] Getting argument functions")
if use_ghidra:
arg_funcs = fh.get_function_information(file_name)
else:
arg_funcs = fh.get_arg_funcs(file_name)
for func in arg_funcs:
func['file_name'] = file_name
arg_funcs = fix_functions(arg_funcs)
#arg_funcs = [x for x in arg_funcs if 'mtd_write_firmware' in x['name']]
print("[+] Analyzing {} functions".format(len(arg_funcs)))
return get_bugs_from_functions(arg_funcs, ld_path)
def test():
parser = argparse.ArgumentParser()
parser.add_argument("File")
args = parser.parse_args()
info = fh.get_function_information(args.File)
#info = fh.get_arg_funcs(args.File)
info = trim_funcs(info, args.File)
vect, func_sparse = funcs_to_sparse(info)
transformer = Normalizer().fit(func_sparse)
func_sparse = transformer.transform(func_sparse)
#svd = TruncatedSVD(random_state=2)
svd = TruncatedSVD(n_components=5, n_iter=7, random_state=42)
func_sparse = svd.fit_transform(func_sparse)
scores = []
clust_count = []
for x in range(2, 20):
result = KMeans(n_clusters=x, random_state=2).fit(func_sparse)
score = silhouette_score(func_sparse, result.labels_, metric="cosine")
scores.append(score)
clust_count.append(x)
print("Clusters {:<3} | Silhoette Score : {}".format(x, score))
plt.plot(clust_count, scores)
plt.xlabel("Cluster Centroid Count")
plt.ylabel("Silhoette Score")
plt.grid = True
plt.show()
pass