def ana1(w1, w2, lamb):
data = load('./Result/small_prob.pk')
# bot_ids = ab_traf_bot_det(data['adj_mats'], w1=10, w2=0.001, lamb=0)
bot_ids = ab_traf_bot_det(data['adj_mats'], w1=w1, w2=w2, lamb=lamb)
bot_ips = [data['ips'][i] for i in bot_ids]
# print('bot_ips', bot_ips)
ddos_ips = data['ddos_ips']
stat = get_quantitative(ddos_ips, bot_ips,
data['ips'], show=True)
return stat, bot_ips
# flows.txt // the normal IP Address. We can stored it separately.
# this is used to get All ddos ip addresses. This tool should only deal with
# SIGs. botnet data should be converted to SIGs first.
def get_ips(data, format_=None):
nnx = NetworkXGraph(data=data)
if format_ is not None:
return [format_(ip) for ip in nnx.get_vertices()]
return nnx.get_vertices()
# GetAdjacent Matrix
ab_ids = range(200, 230)
sigs = load('./Result/sigs_nx.pk')
adj_mats = [nx.to_scipy_sparse_matrix(sigs[i]) for i in ab_ids]
#### Identify the Pivot Nodes ######
tr = load('./Result/GCM_tr.pk')
weights = tr['solution']
p_nodes = ident_pivot_nodes(adj_mats, weights, 0.8)
# select *SN* Nodes together with p_nodes
N = adj_mats[0].shape[0]
perm = np.random.permutation(N - len(p_nodes))
SN = 100
selected_nodes = perm[:(SN - len(p_nodes))]
selected_nodes = np.concatenate([selected_nodes, p_nodes])
# get Adjacent matrix for the selected subgraph
# CSV Dataset Read and Flushed
"""rd = ReadDataSet("Data/Dataset")
rd.readCSV()"""
# Create Graph and save
"""rd = ReadDataSet("Data/Slashdot0811")
G = nx.Graph()
for ln in rd.readTxt():
a, b = map(int, ln.split())
G.add_edge(a, b)"""
# Save Graph
"""flush(G)"""
# Load Graph
G = load()
# Print Node Contents
"""for item in G.nodes(data=True):
print item"""
# Get Friends of node
node = '2014BCS072'
print G.node[node]
print ""
pairL = ProcessNetwork(G, node)
for item in pairL:
print item, G.node[item]
# Calculate accuracies
"""lst = []
def getAll(datasave=True):
"""
return [index, time, num1, num1delta, num2, num2delta]
"""
print('Local Data 수집 시작')
MGLocalFetchBot.old = load('localdata.bin')
#[url, num1, num2, index]
bundle = [[
'https://www.suwon.go.kr/web/safesuwon/corona/PD_index.do#none',
'body > div.layout > div > ul > li:nth-child(1) > div > div.status.clearfix > table > tbody > tr > td:nth-child(1)',
'body > div.layout > div > ul > li:nth-child(1) > div > div.status.clearfix > table:nth-child(2) > tbody > tr > td:nth-child(2)',
'body > div.layout > div > ul > li:nth-child(1) > div > div.status.clearfix > div'
],
[
'https://corona.seongnam.go.kr/', '#value01', '#value03',
'#corona_page > div.corona_page_top > div > div.contents_all > span'
]]
threads = []
i = 0
idx_ls = [0, 2]
for data in bundle:
threads.append(
threading.Thread(target=MGLocalFetchBot.Fetch_local_case1,
args=(data, idx_ls[i])))
i += 1
threads.append(
threading.Thread(target=MGLocalFetchBot.Fetch_local_case2))
threads.append(
threading.Thread(target=MGLocalFetchBot.Fetch_local_case3))
start = time.time()
for x in threads:
x.start()
for x in threads:
x.join()
end = time.time()
datals = []
if datasave:
numls = []
print(MGLocalFetchBot.local_data)
for k, v in sorted(MGLocalFetchBot.local_data.items()):
datals.append(v)
if datasave:
if len(v) == 0:
numls.append(MGLocalFetchBot.old[k])
else:
numls.append([v[2], v[4]])
# print(numls)
if datasave:
MGLocalFetchBot.newls = numls
print('Local Data 수집 끝', end - start)
print(datals)
return datals
# sigs_nx.pk // sigs stored in networkx format
# GCM_Tr.pk // solution --> this is unecessary, we will use equal weights
# flows.txt // the normal IP Address. We can stored it separately.
# this is used to get All ddos ip addresses. This tool should only deal with
# SIGs. botnet data should be converted to SIGs first.
def get_ips(data, format_=None):
nnx = NetworkXGraph(data=data)
if format_ is not None:
return [format_(ip) for ip in nnx.get_vertices()]
return nnx.get_vertices()
# GetAdjacent Matrix
ab_ids = range(200, 230)
sigs = load('./Result/sigs_nx.pk')
adj_mats = [nx.to_scipy_sparse_matrix(sigs[i]) for i in ab_ids]
#### Identify the Pivot Nodes ######
tr = load('./Result/GCM_tr.pk')
weights = tr['solution']
p_nodes = ident_pivot_nodes(adj_mats, weights, 0.8)
# select *SN* Nodes together with p_nodes
N = adj_mats[0].shape[0]
perm = np.random.permutation(N - len(p_nodes))
SN = 100
selected_nodes = perm[:(SN-len(p_nodes))]
selected_nodes = np.concatenate([selected_nodes, p_nodes])
# get Adjacent matrix for the selected subgraph
