def caida():
# normal_sigs, normal_nodes = parseToCoo('../loc6-20070501-100.sigs',
# undirected=True)
# model, para, debug_ret = select_model(len(normal_nodes), normal_sigs, 50, 50, True)
# import ipdb;ipdb.set_trace()
# ROOT = '/home/wangjing/LocalResearch/CyberData/caida-data/passive-2013/'
ROOT = '../'
normal_sigs, normal_nodes = parseToCoo(ROOT + \
'equinix-sanjose.dirA.20130117-125912.UTC.anon-10.sigs',
undirected=True, first_k=None)
botnet_sigs, botnet_nodes = parseToCoo(
'../ddostrace.20070804_134936-10.sigs', undirected=True)
model, para, debug_ret = select_model(len(normal_nodes), normal_sigs, 4,
1000, True)
data_set = normal_sigs * 3 + botnet_sigs + normal_sigs * 3
divs1 = monitor_deg_dis(data_set, 'CHJ', [debug_ret['CHJ'][1], 1e-10])
divs3 = monitor_deg_dis(data_set, 'BA', [debug_ret['BA'][1], 1e-10])
divs2 = monitor_deg_dis(data_set, 'ER', [debug_ret['ER'][1], 1e-10])
dump_data = {'d1': divs1, 'd2': divs2, 'd3': divs3}
dump(dump_data, './CHJ_overperform_BA.pk')
P.subplot(311)
P.plot(divs1)
P.subplot(312)
P.plot(divs2)
P.subplot(313)
P.plot(divs3)
P.show()
import ipdb
ipdb.set_trace()
def old_caida():
ROOT = '/home/wangjing/LocalResearch/CyberData/caida-data/'
T = 4.33
dur_set = np.linspace(0.1, T * 0.9, 20)
lk_list = []
for dur in dur_set:
dat = load(ROOT + 'passive-2013-sigs-%f/sigs.pk' % (dur))
sigs, nodes = old_pk_to_coo(dat, True)
print('len sigs', len(sigs))
# model, para, debug_ret = select_model(len(nodes), sigs, min([40, len(sigs)]), 200, True)
model, para, debug_ret = select_model(len(nodes), sigs, len(sigs), 200,
True)
lk_list.append(debug_ret['ER'][2])
print('log likelihood value', debug_ret['ER'][2])
print('para', para)
print('model', model)
dump({
'x': dur_set,
'y': lk_list,
'x_name': 'dur_set',
'y_name': 'lk_list'
}, './caida-backbone-er-different-window-size-large-sample.pk')
P.plot(dur_set, lk_list)
# P.plot(sdd)
P.show()
import ipdb
ipdb.set_trace()
def caida():
# normal_sigs, normal_nodes = parseToCoo('../loc6-20070501-100.sigs',
# undirected=True)
# model, para, debug_ret = select_model(len(normal_nodes), normal_sigs, 50, 50, True)
# import ipdb;ipdb.set_trace()
# ROOT = '/home/wangjing/LocalResearch/CyberData/caida-data/passive-2013/'
ROOT = '../'
normal_sigs, normal_nodes = parseToCoo(ROOT + \
'equinix-sanjose.dirA.20130117-125912.UTC.anon-10.sigs',
undirected=True, first_k=None)
botnet_sigs, botnet_nodes = parseToCoo('../ddostrace.20070804_134936-10.sigs', undirected=True)
model, para, debug_ret = select_model(len(normal_nodes), normal_sigs, 4, 1000, True)
data_set = normal_sigs * 3 + botnet_sigs + normal_sigs * 3
divs1 = monitor_deg_dis(data_set, 'CHJ', [debug_ret['CHJ'][1], 1e-10])
divs3 = monitor_deg_dis(data_set, 'BA', [debug_ret['BA'][1], 1e-10])
divs2 = monitor_deg_dis(data_set, 'ER', [debug_ret['ER'][1], 1e-10])
dump_data = {'d1': divs1, 'd2': divs2, 'd3': divs3}
dump(dump_data, './CHJ_overperform_BA.pk')
P.subplot(311)
P.plot(divs1)
P.subplot(312)
P.plot(divs2)
P.subplot(313)
P.plot(divs3)
P.show()
import ipdb;ipdb.set_trace()
def validate_select_model_with_ER(N):
p_set = np.linspace(0.0001, 0.05, 10)
dat = dict()
dat['p_set'] = p_set
dat['ret'] = []
for p in p_set:
normal_sigs = gen_sigs('ER', 100, N, p)
normal_nodes = range(N)
model, para, debug_ret = select_model(len(normal_nodes), normal_sigs, 50, 50, True)
dat['ret'].append(debug_ret)
dump(dat, 'validate_select_model_with_ER-N-%s.pk' % (N))
def validate_select_model_with_ER(N):
p_set = np.linspace(0.0001, 0.05, 10)
dat = dict()
dat['p_set'] = p_set
dat['ret'] = []
for p in p_set:
normal_sigs = gen_sigs('ER', 100, N, p)
normal_nodes = range(N)
model, para, debug_ret = select_model(len(normal_nodes), normal_sigs,
50, 50, True)
dat['ret'].append(debug_ret)
dump(dat, 'validate_select_model_with_ER-N-%s.pk' % (N))
def validate_select_model_with_BA(m):
N_set = [50, 150, 250, 350]
dat = dict()
dat['N'] = N_set
dat['ret'] = []
for N in N_set:
normal_sigs = gen_sigs('BA', 100, N, m)
normal_nodes = range(N)
model, para, debug_ret = select_model(len(normal_nodes), normal_sigs, 50, 50, True)
dat['ret'].append(debug_ret)
dump(dat, 'validate_select_model_with_BA-m-%i.pk' % (m))
def validate_select_model_with_BA(m):
N_set = [50, 150, 250, 350]
dat = dict()
dat['N'] = N_set
dat['ret'] = []
for N in N_set:
normal_sigs = gen_sigs('BA', 100, N, m)
normal_nodes = range(N)
model, para, debug_ret = select_model(len(normal_nodes), normal_sigs,
50, 50, True)
dat['ret'].append(debug_ret)
dump(dat, 'validate_select_model_with_BA-m-%i.pk' % (m))
def validate_select_model_with_power_law(p):
N_set = [50, 150, 250, 350]
dat = dict()
dat['N'] = N_set
dat['ret'] = []
for N in N_set:
normal_sigs = gen_sigs('powerlaw_cluster_graph', 100, N, 2, p)
normal_nodes = range(N)
model, para, debug_ret = select_model(len(normal_nodes), normal_sigs, 50, 50, True)
dat['ret'].append(debug_ret)
# import ipdb;ipdb.set_trace()
dump(dat, 'validate_select_model_with_power_law-p-%s.pk' % (p))
def validate_select_model_with_power_law(p):
N_set = [50, 150, 250, 350]
dat = dict()
dat['N'] = N_set
dat['ret'] = []
for N in N_set:
normal_sigs = gen_sigs('powerlaw_cluster_graph', 100, N, 2, p)
normal_nodes = range(N)
model, para, debug_ret = select_model(len(normal_nodes), normal_sigs,
50, 50, True)
dat['ret'].append(debug_ret)
# import ipdb;ipdb.set_trace()
dump(dat, 'validate_select_model_with_power_law-p-%s.pk' % (p))
def old_caida():
ROOT = '/home/wangjing/LocalResearch/CyberData/caida-data/'
T = 4.33
dur_set = np.linspace(0.1, T*0.9, 20)
lk_list = []
for dur in dur_set:
dat = load(ROOT+'passive-2013-sigs-%f/sigs.pk' % (dur))
sigs, nodes = old_pk_to_coo(dat, True)
print('len sigs', len(sigs))
# model, para, debug_ret = select_model(len(nodes), sigs, min([40, len(sigs)]), 200, True)
model, para, debug_ret = select_model(len(nodes), sigs, len(sigs), 200, True)
lk_list.append(debug_ret['ER'][2])
print('log likelihood value', debug_ret['ER'][2])
print('para', para)
print('model', model)
dump({'x':dur_set, 'y':lk_list, 'x_name':'dur_set', 'y_name':'lk_list'},
'./caida-backbone-er-different-window-size-large-sample.pk')
P.plot(dur_set, lk_list)
# P.plot(sdd)
P.show()
import ipdb;ipdb.set_trace()