def verify_candidates(candidates, user_movies_matrix, start_time):
print("\nVerifying candidates...")
count = 0
print("Number of buckets in total: " + str(len(candidates)))
for cnr, candidate_group in enumerate(candidates):
# print("Number of candidates in bucket " + str(cnr) + ": " + str(len(candidate_group)))
for cnr1, candidate1 in enumerate(candidate_group):
for cnr2 in range(cnr1 + 1, len(candidate_group)):
candidate2 = list(candidate_group)[cnr2]
jsim = sim.jaccard(user_movies_matrix[candidate1],
user_movies_matrix[candidate2])
if jsim >= 0.50:
print("Number of candidates in bucket " + str(cnr) + ": " +
str(len(candidate_group)))
count = count + 1
print((candidate1, candidate2))
print("Similarity: " + str(
sim.jaccard(user_movies_matrix[candidate1],
user_movies_matrix[candidate2])))
print("Found until now: " + str(count))
util.print_time(start_time)
print()
# print()
print(count)
def store_similarity(p):
length = len(p)
dict = {} #最终要得到的字典
for key in p.keys():
dict1 = {} #每个点对应的相似度字典
for i in range(key + 1, length):
dict1.setdefault(i, jaccard(p.get(key), p.get(i)))
dict.setdefault(key, dict1)
return dict
def test_jaccard(self):
G = self.G
jac = jaccard(G)
nt.assert_equal(len(jac), 7)
for i in range(7):
assert(i in jac)
for i in self.G.jaccard.keys():
nt.assert_equal(len(self.G.jaccard[i]), len(jac[i]))
for j in self.G.jaccard[i].keys():
nt.assert_almost_equal(jac[i][j], self.G.jaccard[i][j], places=4)
def test_jaccard(self):
G = self.G
jac = jaccard(G)
nt.assert_equal(len(jac), 7)
for i in range(7):
assert (i in jac)
for i in self.G.jaccard.keys():
nt.assert_equal(len(self.G.jaccard[i]), len(jac[i]))
for j in self.G.jaccard[i].keys():
nt.assert_almost_equal(jac[i][j],
self.G.jaccard[i][j],
places=4)
def verify_partial_candidates(candidate_group, user_movies_matrix, bucket_nr,
nr_found, start_time):
for cnr1, candidate1 in enumerate(candidate_group):
for cnr2 in range(cnr1 + 1, len(candidate_group)):
candidate2 = list(candidate_group)[cnr2]
jsim = sim.jaccard(user_movies_matrix[candidate1],
user_movies_matrix[candidate2])
if jsim >= 0.50:
pair = sorted((candidate1, candidate2))
data.save_pair(pair)
print("\tFound similar pair: " + str(pair))
print("\tSimilarity: " + str(
sim.jaccard(user_movies_matrix[candidate1],
user_movies_matrix[candidate2])))
print("\tBucket number: " + str(bucket_nr))
print("\tNumber of candidates in the bucket: " +
str(len(candidate_group)))
nr_found[0] = nr_found[0] + 1
print("\tFound until now: " + str(nr_found[0]))
util.print_time(start_time, "\t")
print()
def apply2(user_movies_matrix, bands=5, rows=10):
permutations = []
for r in range(100):
permutation = []
for u in user_movies_matrix:
permutation.append(u[r])
permutations.append(permutation)
signature_matrix = np.array(
[np.zeros(100).astype(int) for i in range(len(user_movies_matrix))])
for permutation in permutations:
for i, user_movies in enumerate(user_movies_matrix):
if permutation[i] in user_movies:
signature_matrix[i][0]
for b in range(1, bands + 1):
buckets = {}
for user_id, user_movies in enumerate(user_movies_matrix):
row_signature = ''
for r in range(1, rows + 1):
min_hash = min(
[mmh3.hash(movie, seed=b * r) for movie in user_movies])
row_signature = row_signature + str(min_hash)
if row_signature in buckets:
buckets[row_signature].append(user_id)
else:
buckets[row_signature] = [user_id]
for bucket in buckets:
candidate_group = buckets[bucket]
if len(candidate_group) > 1:
for cnr1, candidate1 in enumerate(candidate_group):
for cnr2 in range(cnr1 + 1, len(candidate_group)):
candidate2 = candidate_group[cnr2]
print((candidate1, candidate2))
print(
sim.jaccard(user_movies_matrix[candidate1],
user_movies_matrix[candidate2]))
def neighbors(data, n, simtype):
for user_key in data:
user = data[user_key]
user["neighbors"] = {}
for other_key in data:
other = data[other_key]
if user_key == other_key:
continue
farthest, index = farthest_close_neighbor(user)
sim = None
if simtype == "pearson":
sim = similarity.pearson(user, other)
else:
sim = similarity.jaccard(user, other)
if len(user["neighbors"]) < n or sim > farthest:
user["neighbors"][other_key] = {
"id": other_key,
"similarity": sim
}
if len(user["neighbors"]) > n:
user["neighbors"].pop(index)
def main(args):
print('Start test')
creds = ReadDictJson(args.credentails)
if not creds:
print('Failed to load credentials file {}. Exiting'.format(args.credentails))
return False
s3def = creds['s3'][0]
s3 = s3store(s3def['address'],
s3def['access key'],
s3def['secret key'],
tls=s3def['tls'],
cert_verify=s3def['cert_verify'],
cert_path=s3def['cert_path']
)
trainingset = '{}/{}/'.format(s3def['sets']['trainingset']['prefix'] , args.trainingset)
print('Load training set {}/{} to {}'.format(s3def['sets']['trainingset']['bucket'],trainingset,args.trainingset_dir ))
s3.Mirror(s3def['sets']['trainingset']['bucket'], trainingset, args.trainingset_dir)
trainingsetDescriptionFile = '{}/description.json'.format(args.trainingset_dir)
trainingsetDescription = json.load(open(trainingsetDescriptionFile))
config = {
'batch_size': args.batch_size,
'trainingset': trainingsetDescription,
'input_shape': [args.training_crop[0], args.training_crop[1], args.train_depth],
'classScale': 0.001, # scale value for each product class
'augment_rotation' : 5., # Rotation in degrees
'augment_flip_x': False,
'augment_flip_y': True,
'augment_brightness':0.,
'augment_contrast': 0.,
'augment_shift_x': 0.0, # in fraction of image
'augment_shift_y': 0.0, # in fraction of image
'scale_min': 0.75, # in fraction of image
'scale_max': 1.25, # in fraction of image
'ignore_label': trainingsetDescription['classes']['ignore'],
'classes': trainingsetDescription['classes']['classes'],
'epochs': 1,
'area_filter_min': 25,
'weights': None,
'channel_order': args.channel_order,
's3_address':s3def['address'],
's3_sets':s3def['sets'],
'initialmodel':args.initialmodel,
'training_dir': None, # used by LoadModel
'learning_rate': 1e-3, # used by LoadModel
'clean' : True,
'test_archive': trainingset,
'run_archive': '{}{}/'.format(trainingset, args.initialmodel),
'min':args.min,
}
trainingsetDescriptionFile = '{}/description.json'.format(args.trainingset_dir)
trainingsetDescription = json.load(open(trainingsetDescriptionFile))
strategy = None
if(args.strategy == 'mirrored'):
strategy = tf.distribute.MirroredStrategy(devices=args.devices)
else:
device = "/gpu:0"
if args.devices is not None and len(args.devices) > 0:
device = args.devices[0]
strategy = tf.distribute.OneDeviceStrategy(device=device)
# Prepare datasets for similarity computation
objTypes = {}
for objType in trainingsetDescription['classes']['objects']:
if objType['trainId'] not in objTypes:
objTypes[objType['trainId']] = copy.deepcopy(objType)
# set name to category for objTypes and id to trainId
objTypes[objType['trainId']]['name'] = objType['category']
objTypes[objType['trainId']]['id'] = objType['trainId']
results = {'class similarity':{}, 'config':config, 'image':[]}
for objType in objTypes:
results['class similarity'][objType] = {'union':0, 'intersection':0}
with strategy.scope(): # Apply training strategy
model = LoadModel(config, s3)
accuracy = tf.keras.metrics.Accuracy()
# Display model
model.summary()
#train_dataset = input_fn('train', args.trainingset_dir, config)
val_dataset = input_fn('val', args.trainingset_dir, config)
trainingsetdesc = {}
validationsetdec = {}
for dataset in config['trainingset']['sets']:
if dataset['name'] == 'val':
validationsetdec = dataset
if dataset['name'] == 'train':
trainingsetdesc = dataset
print("Begin inferences")
dtSum = 0.0
accuracySum = 0.0
total_confusion = None
iterator = iter(val_dataset)
numsteps = int(validationsetdec['length']/config['batch_size'])
if(config['min']):
numsteps=min(args.min_steps, numsteps)
try:
for i in tqdm(range(numsteps)):
image, annotation = iterator.get_next()
initial = datetime.now()
logits = model.predict(image, batch_size=config['batch_size'], steps=1)
segmentation = tf.argmax(logits, axis=-1)
dt = (datetime.now()-initial).total_seconds()
dtSum += dt
imageTime = dt/config['batch_size']
for j in range(config['batch_size']):
img = tf.squeeze(image[j]).numpy().astype(np.uint8)
ann = tf.squeeze(annotation[j]).numpy().astype(np.uint8)
seg = tf.squeeze(segmentation[j]).numpy().astype(np.uint8)
accuracy.update_state(ann,seg)
seg_accuracy = accuracy.result().numpy()
accuracySum += seg_accuracy
imagesimilarity, results['class similarity'], unique = jaccard(ann, seg, objTypes, results['class similarity'])
confusion = tf.math.confusion_matrix(ann.flatten(),seg.flatten(), config['classes']).numpy().astype(np.int64)
if total_confusion is None:
total_confusion = confusion
else:
total_confusion += confusion
results['image'].append({'dt':imageTime,'similarity':imagesimilarity, 'accuracy':seg_accuracy.astype(float), 'confusion':confusion.tolist()})
except Exception as e:
print("Error: test exception {} step {}".format(e, i))
numsteps = i
except:
print("Error: test exception step {}".format(i))
numsteps = i
num_images = numsteps*config['batch_size']
average_time = dtSum/num_images
average_accuracy = accuracySum/num_images
sumIntersection = 0
sumUnion = 0
sumAccuracy = 0.0
dataset_similarity = {}
for key in results['class similarity']:
intersection = results['class similarity'][key]['intersection']
sumIntersection += intersection
union = results['class similarity'][key]['union']
sumUnion += union
class_similarity = similarity(intersection, union)
# convert to int from int64 for json.dumps
dataset_similarity[key] = {'intersection':int(intersection) ,'union':int(union) , 'similarity':class_similarity}
results['class similarity'] = dataset_similarity
total_similarity = similarity(sumIntersection, sumUnion)
now = datetime.now()
date_time = now.strftime("%m/%d/%Y, %H:%M:%S")
test_summary = {'date':date_time, 'model':config['initialmodel']}
test_summary['model']=config['initialmodel']}
test_summary['accuracy']=average_accuracy
test_summary['class_similarity']=dataset_similarity
test_summary['similarity']=total_similarity
test_summary['confusion']=total_confusion.tolist()
test_summary['images']=num_images
test_summary['image time']=average_time
test_summary['batch size']=config['batch_size']
test_summary['test store'] =s3def['address']
test_summary['test bucket'] = s3def['sets']['trainingset']['bucket']
test_summary['results'] = results
print ("Average time {}".format(average_time))
print ('Similarity: {}'.format(dataset_similarity))
# If there is a way to lock this object between read and write, it would prevent the possability of loosing data
training_data = s3.GetDict(s3def['sets']['trainingset']['bucket'], config['test_archive']+args.tests_json)
if training_data is None:
training_data = []
training_data.append(test_summary)
s3.PutDict(s3def['sets']['trainingset']['bucket'], config['test_archive']+args.tests_json, training_data)
test_url = s3.GetUrl(s3def['sets']['trainingset']['bucket'], config['test_archive']+args.tests_json)
print("Test results {}".format(test_url))
# Calculate Jaccard-coefficient for all links.
jaccard_vals = []
cosine_vals = []
checktable = {}
for i, label in enumerate(lbl):
u = i+1
for v in label:
if len(lbl[v-1]) == 0: continue
check_key = tuple(sorted([u, v]))
if check_key not in checktable:
sim_uv = similarity.jaccard(lbl[u-1], lbl[v-1])
jaccard_vals.append(sim_uv)
sim_uv_cos = (1.0 / math.sqrt(len(lbl[u-1]))) * (1.0 / math.sqrt(len(lbl[v-1]))) * float(len(set(lbl[u-1]) & set(lbl[v-1])))
cosine_vals.append(sim_uv_cos)
#sim_uv = float( len(set(lbl[u-1]) & set(lbl[v-1])) ) / float( len(
"""
orlist = sorted(list(set(lbl[u-1]) | set(lbl[v-1])))
feat_u = [0.0] * len(orlist)
"""
checktable[check_key] = 1
import pandas
### VERTEX SIMILARITY
graphs = pickle.load(open('data/graphs_networkx.pkl', 'rb'))
results = dict()
for graph_type, G in graphs.iteritems():
print "Calculating for %s" % (graph_type)
results[graph_type] = dict()
print "ASCOS ---------------------------"
results[graph_type]["ascos"] = ascos(G)
print "COSINE ---------------------------"
results[graph_type]["cosine"] = cosine(G)
print "JACCARD --------------------------"
results[graph_type]["jaccard"] = jaccard(G)
print "KATZ -----------------------------"
results[graph_type]["katz"] = katz(G)
print "LHN ------------------------------"
results[graph_type]["lhn"] = lhn(G)
print "RSS2 -----------------------------"
results[graph_type]["rss2"] = rss2(G)
print "DICE -----------------------------"
results[graph_type]["dice"] = dice(G)
print "INVERSE LOG WEIGHTED --------------"
results[graph_type]["inverse_log_weighted"] = inverse_log_weighted(G)
pickle.dump(results, open("data/sim_metrics.pkl", "wb"))
### IMAGE SIMILARITY
def show_similarity(p,q):
a=jaccard(p,q)
print(p,'\n',q)
print('二者相似度：',a)
def handlenonexe(documentsamples, thresholddict, graph):
"""
Form edges between non-exe samples based on the `strings` attribute
Args:
documentsamples: absolute paths of all non-exe malware samples
thresholddict : dict containing Jaccard Index threshold values
graph : networkx graph object
Raises:
None
Returns:
graph: populated networkx graph object
"""
# Get shelve database object
db = getdatabase()
malwareattributes = dict()
# Get attributes and create node
for path in documentsamples:
# Get `strings` for `path` sample from shelve db and store them
malwareattributes[path] = db[path]
# Add each malware sample to the graph as a node. The label of the
# node is equal to the first six chars of SHA256 hash of the sample
graph.add_node(path.split('/')[-1], label=os.path.split(path)[-1][:6])
# Create edge based on Jaccard index
for malware1, malware2 in itertools.combinations(documentsamples, 2):
# Compute the jaccard distance for the current pair
jaccardindex = jaccard(malwareattributes[malware1],
malwareattributes[malware2])
# Determine file types to use appropriate jaccard index value
malware1type = magic.from_file(malware1, mime=True)
malware2type = magic.from_file(malware2, mime=True)
if comparefiletypes(malware1type, malware2type):
try:
jaccardthreshold = thresholddict[malware1type]
except KeyError as e:
print(
"[*] Jaccard Index for filetype not available. Skipping..."
)
continue
else:
#print("Different file signatures detected: " + str(malware1type) + \
# "," + str(malware2type))
# Two malware of different file types cannot possibly be related
continue
# If the jaccard is above `jaccardthreshold`, then add an edge
if jaccardindex > jaccardthreshold:
node1 = malware1.split('/')[-1]
node2 = malware2.split('/')[-1]
graph.add_edge(node1,
node2,
penwidth=1 + (jaccardindex - jaccardthreshold) * 10)
return graph
def handleexe(exesamples, thresholddict, ngram, graph):
"""
Form edges between exe samples based on the dynamic api calls attribute
Args:
exesamples : absolute paths of all exe malware samples
thresholddict: dict containing Jaccard Index threshold values
ngram : ngram number of api sequences
graph : networkx graph object
Raises:
None
Returns:
graph: populated networkx graph object
"""
malwareattributes = dict()
# Read task and error ids of previous cuckoo dispatch
taskids, errorids = readids()
reportsurl = "http://localhost:8090/tasks/report/"
headers = {"Authorization": "Bearer WTAIn0HHtRIUlR9_uJkJDg"}
for taskid in taskids:
taskreporturl = reportsurl + str(taskid)
# Get report for `taskid`
r = requests.get(taskreporturl, headers=headers)
# Convert 'str' Cuckoo output to Python 'dict'
taskreportjson = json.loads(r.content.decode('utf-8'))
# Get info about all processes related to task from cuckoo task report
taskprocessesinfo = taskreportjson['behavior']['processes']
# Extract `process_path`, `process_name`, `first_seen` and `pid` from
# task report
taskprocessdict = extractstaticfeatures(taskprocessesinfo)
# Add dynamic API calls info to `taskprocessdict`
taskprocessdict = extractapicalls(taskprocessdict, taskprocessesinfo)
# Find order of processes in task based on their `first_seen`
orderedts = []
for pid in taskprocessdict:
orderedts.append(taskprocessdict[pid]['first_seen'])
orderedts.sort()
# Extract api calls in order considering all processes related to task
apiattributes = extractorderedapi(orderedts, taskprocessdict)
# Calculate n-gram APIs
apiattributes = calculatengram(ngram, apiattributes)
# Get parent process (original malware exe process)
path = getparentprocess(taskid, exesamples)
# Store dynamic API calls attribute
malwareattributes[path] = apiattributes
# Add exe samples' node to graph
for path in exesamples:
graph.add_node(path.split('/')[-1], label=os.path.split(path)[-1][:6])
# Create edge based on Jaccard index
for malware1, malware2 in itertools.combinations(exesamples, 2):
# Compute the jaccard distance for the current pair
jaccardindex = jaccard(malwareattributes[malware1],
malwareattributes[malware2], 'api')
node1 = malware1.split('/')[-1]
node2 = malware2.split('/')[-1]
# Determine file types to use appropriate jaccard index value
malware1type = magic.from_file(malware1, mime=True)
malware2type = magic.from_file(malware2, mime=True)
if comparefiletypes(malware1type, malware2type):
try:
jaccardthreshold = thresholddict[malware1type]
except KeyError as e:
print("[*] Jaccard Index for filetype: " + str(malware1type) +
" not available. Skipping...")
continue
else:
#print("Different file signatures detected: " + str(malware1type) + \
# "," + str(malware2type))
# Two malware of different file types cannot possibly be related
continue
# If the jaccard index is above `jaccardthreshold`, add an edge
if jaccardindex > jaccardthreshold:
graph.add_edge(node1,
node2,
penwidth=1 + (jaccardindex - jaccardthreshold) * 10)
return graph
import pandas
### VERTEX SIMILARITY
graphs = pickle.load(open('data/graphs_networkx.pkl','rb'))
results = dict()
for graph_type,G in graphs.iteritems():
print "Calculating for %s" %(graph_type)
results[graph_type] = dict()
print "ASCOS ---------------------------"
results[graph_type]["ascos"] = ascos(G)
print "COSINE ---------------------------"
results[graph_type]["cosine"] = cosine(G)
print "JACCARD --------------------------"
results[graph_type]["jaccard"] = jaccard(G)
print "KATZ -----------------------------"
results[graph_type]["katz"] = katz(G)
print "LHN ------------------------------"
results[graph_type]["lhn"] = lhn(G)
print "RSS2 -----------------------------"
results[graph_type]["rss2"] = rss2(G)
print "DICE -----------------------------"
results[graph_type]["dice"] = dice(G)
print "INVERSE LOG WEIGHTED --------------"
results[graph_type]["inverse_log_weighted"] = inverse_log_weighted(G)
pickle.dump(results,open("data/sim_metrics.pkl","wb"))
### IMAGE SIMILARITY