def add_snapshots_to_namespace(self, namespace):
prefix = 'snapshots'
importer = namespace.vars['inst_importer']
exporter = namespace.vars['inst_exporter']
snapshot_source = SnapshotStateOpenStack(exporter).create_snapshot()
snapshot_dest = SnapshotStateOpenStack(importer).create_snapshot()
path_source = "%s/source/%s.snapshot" % (prefix, snapshot_source.timestamp)
path_dest = "%s/dest/%s.snapshot" % (prefix, snapshot_dest.timestamp)
namespace.vars['snapshots']['source'].append({'path': path_source, 'timestamp': snapshot_source.timestamp})
namespace.vars['snapshots']['dest'].append({'path': path_dest, 'timestamp': snapshot_dest.timestamp})
dump_to_file(path_source, snapshot_source)
dump_to_file(path_dest, snapshot_dest)
def run(self, inst_exporter=None, inst_importer=None, snapshots={'source': [], 'dest': []}, **kwargs):
snapshot_source = SnapshotStateOpenStack(inst_exporter).create_snapshot()
snapshot_dest = SnapshotStateOpenStack(inst_importer).create_snapshot()
path_source = "%s/source/%s.snapshot" % (self.prefix, snapshot_source.timestamp)
path_dest = "%s/dest/%s.snapshot" % (self.prefix, snapshot_dest.timestamp)
snapshots['source'].append({'path': path_source, 'timestamp': snapshot_source.timestamp})
snapshots['dest'].append({'path': path_dest, 'timestamp': snapshot_dest.timestamp})
dump_to_file(path_source, snapshot_source)
dump_to_file(path_dest, snapshot_dest)
return {
'snapshots': snapshots
}
def get_readable_html(url, htmlfile=None):
"""given an url, return the local path to a readable version"""
html = urllib.urlopen(url).read()
sanitized_html = BeautifulSoup(html).prettify() # detect encoding
readable = readability.Document(sanitized_html)
body = readable.summary()
html = u"""
<html>
<head>
<meta http-equiv="Content-Type" content="text/html; charset=utf-8" />
</head>
<body>
%(body)s
</body>
<html>
""" % {'body': body}
return dump_to_file(BeautifulSoup(html).prettify(), htmlfile)
def main():
parser = argparse.ArgumentParser(
prog='memdumper',
formatter_class=argparse.RawDescriptionHelpFormatter,
description=textwrap.dedent(""))
parser.add_argument('-p',
'--process',
help='the process that you will be injecting to')
parser.add_argument(
'-o',
'--outdir',
type=str,
metavar="dir",
help='provide full output directory path. (def: \'dump\')')
parser.add_argument('-U',
'--usb',
action='store_true',
help='device connected over usb')
parser.add_argument('-v', '--verbose', action='store_true', help='verbose')
parser.add_argument(
'-r',
'--read-only',
action='store_true',
help="dump read-only parts of memory. More data, more errors")
arguments = parser.parse_args()
# Define Configurations
process = arguments.process
max_chunk_size = 1e7 # 10MB
outdir = os.path.join(os.getcwd(), "dump")
if arguments.outdir:
outdir = arguments.outdir
PERMS = 'rw-'
if arguments.read_only:
PERMS = 'r--'
if arguments.verbose:
logging.basicConfig(format='%(levelname)s:%(message)s',
level=logging.DEBUG)
else:
logging.basicConfig(format='%(levelname)s:%(message)s',
level=logging.INFO)
session = None
try:
if arguments.usb:
session = frida.get_usb_device().attach(process)
else:
session = frida.attach(process)
except Exception as e:
logging.error("Can't connect to App")
logging.error(str(e))
sys.exit()
# Selecting Output directory
shutil.rmtree(outdir, ignore_errors=True)
os.makedirs(outdir)
logging.info("Starting Memory dump...")
script = session.create_script("""'use strict';
rpc.exports = {
enumerateRanges: function (prot) {
return Process.enumerateRangesSync(prot);
},
readMemory: function (address, size) {
return Memory.readByteArray(ptr(address), size);
}
};
""")
script.on("message", utils.frida_on_message)
script.load()
agent = script.exports
mem_ranges = agent.enumerate_ranges(PERMS)
# TODO: Make an extension to dump all region names
# import json
# logging.debug("All sections:")
# logging.debug("===============")
# logging.debug(json.dumps(
# {"path": mem_range["file"]["path"], "base": base, "size": size}, indent=4, sort_keys=True))
# logging.debug("file_path: %s", str(mem_range["file"]["path"]))
# Performing the memory dump
for idx, mem_range in enumerate(mem_ranges):
base = mem_range["base"]
size = mem_range["size"]
if (not "file" in mem_range or not "path" in mem_range["file"]):
continue
if not "dalvik-main" in mem_range["file"]["path"]:
continue
if size > max_chunk_size:
logging.debug("Too big, splitting the dump into chunks")
utils.split_big_chunk(agent, base, size, max_chunk_size, outdir)
continue
utils.dump_to_file(agent, base, size, outdir)
idx += 1
utils.print_progress(idx,
len(mem_ranges),
prefix='Progress:',
suffix='Complete',
max_percent=50)
logging.info("Done")
def test(self, params):
print('\n%s: testing...' %datetime.now())
sys.stdout.flush()
session = Session(self._graph, self.results_dir, params['model_name'])
if 'init_step' not in params or params['init_step'] is None:
init_step = session.init_step
else:
init_step = params['init_step']
if 'step_num' not in params or params['step_num'] is None:
step_num = int(np.ceil(np.float(self.fold_size) / self._batch_size))
else:
step_num = params['step_num']
results_file_name = Tester.RESULTS_FILE + '-' + str(init_step) + '-' + \
self.fold_name + '-' + str(step_num) + '.json'
results_file = os.path.join(self.results_dir, results_file_name)
if not params['load_results'] or not os.path.isfile(results_file):
session.init(self._classifier, init_step, params['restoring_file'])
session.start()
if init_step == 0:
print 'WARNING: testing an untrained model'
total_step_num = step_num * params['epoch_num']
test_num = total_step_num * self._batch_size
print('%s: test_num=%d' % (datetime.now(), step_num * self._batch_size))
print('%s: epoch_num=%d' % (datetime.now(), params['epoch_num']))
results = {}
results['losses'] = np.zeros(test_num, dtype=np.float32)
results['probs'] = np.zeros((test_num, Reader.CLASSES_NUM), dtype=np.float32)
results['labels'] = np.zeros(test_num, dtype=np.int64)
start_time = time.time()
for step in range(total_step_num):
#print('%s: eval_iter=%d' %(datetime.now(), i))
loss_batch, prob_batch, label_batch = session.run(
[self._cross_entropy_losses, self._probs, self._input['labels']]
)
begin = step * self._batch_size
results['losses'][begin:begin+self._batch_size] = loss_batch
results['probs'][begin:begin+self._batch_size, :] = prob_batch
results['labels'][begin:begin + self._batch_size] = label_batch
if (step+1) % step_num == 0:
print "Epoch num: %d" % ((step+1)/step_num)
if session.should_stop():
break
duration = time.time() - start_time
print('%s: duration = %.1f sec' %(datetime.now(), float(duration)))
sys.stdout.flush()
if self.writer is not None:
summary_str = session.run(self._all_summaries)
self.writer.write_summaries(summary_str, init_step)
session.stop()
else:
print 'WARNING: using precomputed results'
results = utils.load_from_file(results_file)
results['loss'] = np.mean(results['losses']).item()
results = self.get_all_stats(results)
if self.writer is not None and not params['load_results']:
self.writer.write_scalars({'losses/testing/cross_entropy_loss': results['loss'],
'accuracy': results['accuracy']}, init_step)
utils.dump_to_file(results, results_file)
return init_step, results['loss']
def test(self):
for i in range(3):
files = self.get_files()
# files.insert(0, '>>%i' % i)
utils.dump_to_file('findIt_%i.dump' % i, '\n'.join(files))
def test(self, params):
print('\n%s: testing...' % datetime.now())
sys.stdout.flush()
session = Session(self._graph, self.results_dir, params['model_name'])
if 'init_step' not in params or params['init_step'] is None:
init_step = session.init_step
else:
init_step = params['init_step']
if 'step_num' not in params or params['step_num'] is None:
step_num = int(np.ceil(
np.float(self.fold_size) / self._batch_size))
else:
step_num = params['step_num']
results_file_name = Tester.RESULTS_FILE + '-' + str(init_step) + '-' + \
self.fold_name + '-' + str(step_num) + '.json'
results_file = os.path.join(self.results_dir, results_file_name)
if not params['load_results'] or not os.path.isfile(results_file):
session.init(self._classifier, init_step, params['restoring_file'])
session.start()
if init_step == 0:
print 'WARNING: testing an untrained model'
total_step_num = step_num * params['epoch_num']
test_num = total_step_num * self._batch_size
print('%s: test_num=%d' %
(datetime.now(), step_num * self._batch_size))
print('%s: epoch_num=%d' % (datetime.now(), params['epoch_num']))
results = {}
results['losses'] = np.zeros(test_num, dtype=np.float32)
results['probs'] = np.zeros((test_num, Reader.CLASSES_NUM),
dtype=np.float32)
results['labels'] = np.zeros(test_num, dtype=np.int64)
start_time = time.time()
for step in range(total_step_num):
#print('%s: eval_iter=%d' %(datetime.now(), i))
loss_batch, prob_batch, label_batch = session.run([
self._cross_entropy_losses, self._probs,
self._input['labels']
])
begin = step * self._batch_size
results['losses'][begin:begin + self._batch_size] = loss_batch
results['probs'][begin:begin +
self._batch_size, :] = prob_batch
results['labels'][begin:begin + self._batch_size] = label_batch
if (step + 1) % step_num == 0:
print "Epoch num: %d" % ((step + 1) / step_num)
if session.should_stop():
break
duration = time.time() - start_time
print('%s: duration = %.1f sec' %
(datetime.now(), float(duration)))
sys.stdout.flush()
if self.writer is not None:
summary_str = session.run(self._all_summaries)
self.writer.write_summaries(summary_str, init_step)
session.stop()
else:
print 'WARNING: using precomputed results'
results = utils.load_from_file(results_file)
results['loss'] = np.mean(results['losses']).item()
results = self.get_all_stats(results)
if self.writer is not None and not params['load_results']:
self.writer.write_scalars(
{
'losses/testing/cross_entropy_loss': results['loss'],
'accuracy': results['accuracy']
}, init_step)
utils.dump_to_file(results, results_file)
return init_step, results['loss']
