def find_starts(target: Binary, start_cut: int = 8) -> Iterable[Match]:
match_finder = make_start_scout()
itree = target.partition_without_matches()
matches = []
for interval in itree:
data = target.read()[interval.begin:interval.end]
for addr, matcher in match_finder(data, interval.begin):
match, new = Match.deduplicate(target, addr, matcher.cut_size,
dict(certainty=1))
match.matched_by.add(matcher)
if new:
target.matches.add(match)
matches.append(match)
return matches
def match_matchers_against(target: Binary,
graph=None,
parallelize=True) -> Iterable[Match]:
"""match_candidates_against matches all candidates against the given
Binary.
Args:
target: the target binary to match the candidates against
"""
settings = SettingsStorage.get_settings()
parallelize = parallelize or settings.get("matcher_parallelization", False)
if graph is None:
graph = makeGraph()
bin_ = target.read()
partitions = target.partition()
if parallelize:
matches = parallel_prepartioned_graph_match(graph, bin_, partitions)
else:
matches = prepartioned_graph_match(graph, bin_, partitions)
match_results = []
for matchers, size, end in matches:
start = end - size
match, new = Match.deduplicate(target, start, size,
dict(certainty=1 / len(matchers)))
for matcher in matchers:
match.matched_by.add(matcher)
if new:
target.matches.add(match)
match_results.append(match)
orm.commit()
if settings.get("find_fnc_starts", False):
starts = find_starts(target,
start_cut=settings.get("fnc_start_size", 8))
match_results.extend(starts)
orm.commit()
return match_results