def gammaIso(G1, G2):
nm = isomorphism.categorical_node_match(['fillcolor', 'shape'],
['', ''])
em = isomorphism.categorical_multiedge_match('color', '')
#G1.remove_nodes_from(nx.isolates(G1)) #Drop Isolates
#G2.remove_nodes_from(nx.isolates(G2)) #Drop Isolates
#Create set of induced subgraphs for the Graphs and check for isomorphism.
len_G1_nodes = len(G1.nodes())
len_G2_nodes = len(G2.nodes())
min_len = min(len_G1_nodes, len_G2_nodes)
max_len = max(len_G1_nodes, len_G2_nodes)
G1_list = createAllComboList(G1.nodes(), min_len)
G2_list = createAllComboList(G2.nodes(), min_len)
induced_subgraph_G1 = [
G1.subgraph(node_list) for node_list in G1_list
]
induced_subgraph_G2 = [
G2.subgraph(node_list) for node_list in G2_list
]
new_induced_subgraph_G1 = []
new_induced_subgraph_G2 = []
#Remove graphs with empty nodes and empty edges
for g in induced_subgraph_G1:
if (len(g.nodes()) == 0 or len(g.edges()) == 0):
continue
new_induced_subgraph_G1.append(g)
for g in induced_subgraph_G2:
if (len(g.nodes()) == 0 or len(g.edges()) == 0):
continue
new_induced_subgraph_G2.append(g)
induced_subgraph_G1_rev = new_induced_subgraph_G1[::-1]
induced_subgraph_G2_rev = new_induced_subgraph_G2[::-1]
similarity = 0
for g1 in induced_subgraph_G1_rev:
for g2 in induced_subgraph_G2_rev:
if (len(g1.nodes()) == len(g2.nodes())
and len(g1.edges()) == len(g2.edges())):
GM = isomorphism.MultiDiGraphMatcher(g1, g2, nm, em)
if (GM.is_isomorphic()):
similarity = max(similarity,
len(g1.nodes()) / float(max_len))
return similarity, GM.mapping
return 0, {}
def match(pattern: type_base.SubgraphData,
graph: nx.MultiDiGraph) -> typing.Iterable[typing.Mapping]:
"""
:param graph: Graph to identify and replace isomorphisms (in place)
:return bool indicating whether or not match was successful
"""
mapping = isomorphism.MultiDiGraphMatcher(graph,
pattern.graph,
node_match=_node_match,
edge_match=_edge_match)
return mapping.subgraph_isomorphisms_iter()
def build_matcher(graph: nx.MultiDiGraph, nodes: list, edges: list, node_attrs: list = None,
edge_attrs: list = None):
if node_attrs is not None or edge_attrs is not None:
log.warning('\'edge_attrs\' or `\'node_attrs\'` parameter was passed to function \'find_pattern_matches\', '
'but they are not used anymore. Pattern matching proceeds according to \'nodes\' and \'edges\' '
'parameters. Please avoid passing \'edge_attrs\' and \'node_attrs\' parameters to any pattern '
'matching function like \'find_pattern_matches\', \'apply_pattern\' and \'pattern\' because it '
'will be deprecated in the next release.')
subgraph = nx.MultiDiGraph(name='pattern')
subgraph.add_nodes_from(nodes)
subgraph.add_edges_from(edges)
return ism.MultiDiGraphMatcher(graph, subgraph, node_match, edge_match)
elements_2 = row[1].split(",")
type = elements_2[len(elements_2) - 1]
if 0 <= c:
if c > 0:
if c % (d + 1) != 0:
client_ip = elements_1[2]
server_ip = elements_1[4]
client_port = elements_1[3]
server_port = elements_1[5]
un_graph.add_node(client_ip)
un_graph.add_node(server_ip)
un_graph.add_edge(client_ip + client_port,
server_ip + server_port)
else:
labels.append(type)
m0 = isomorphism.MultiDiGraphMatcher(
samples[0], un_graph)
m1 = isomorphism.MultiDiGraphMatcher(
samples[1], un_graph)
if m0.is_isomorphic() and not m1.is_isomorphic():
classification.append("LDAP")
elif not m0.is_isomorphic() and m1.is_isomorphic():
classification.append("NetBIOS")
else:
classification.append("BENIGN")
un_graph = nx.MultiDiGraph()
c = c + 1
print(c)
# print(classification)
print(classification_report(labels, classification))
reader = csv.reader(csvfile, delimiter=' ', quotechar='|')
c = 0 # c counts for each edge
un_graph = nx.MultiDiGraph()
for row in reader:
elements_1 = row[0].split(",")
elements_2 = row[1].split(",")
type = elements_2[len(elements_2) - 1]
if 0 <= c:
if c > 0:
if c % (d + 1) != 0:
client_ip = elements_1[2]
server_ip = elements_1[4]
client_port = elements_1[3]
server_port = elements_1[5]
un_graph.add_node(client_ip)
un_graph.add_node(server_ip)
un_graph.add_edge(client_ip + client_port,
server_ip + server_port)
else:
for s in samples:
m = isomorphism.MultiDiGraphMatcher(
s, un_graph)
if m.is_isomorphic():
# print(str(m))
classification[samples.index(s)] += 1
un_graph = nx.MultiDiGraph()
c = c + 1
print(c)
print(classification)
