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#coding:utf-8
import networkx as nx
import numpy as np
import csv
import random
import torch
import matplotlib.pyplot as plt
from sklearn.model_selection import StratifiedKFold
from itertools import islice
lable_dict={}
class S2VGraph(object):
def __init__(self, g, label, node_tags=None, node_features=None):
'''
g: a networkx graph
label: an integer graph label
node_tags: a list of integer node tags
node_features: a torch float tensor, one-hot representation of the tag that is used as input to neural nets
edge_mat: a torch long tensor, contain edge list, will be used to create torch sparse tensor
neighbors: list of neighbors (without self-loop)
'''
self.label = label
self.g = g
self.node_tags = node_tags
self.neighbors = []
self.node_features = 0
self.edge_mat = 0
self.max_neighbor = 0
def csv_dataset(addr,fake_port):
addr="/home/liyuzhen/dataset/ids2017/"+addr+".csv"
read=csv.reader(open('%s' %addr,'r'))
ip_DDoS=["172.16.0.1","192.168.10.50"]
ip={}
flows_norm=[]
flows_DDoS=[]
port_DDoS=[]
port_norm=[]
num=0
for row in islice(read,1,None):
num=num+1
tmp_one_packet=[]
if ((row[2]=='') or (row[3]=='')):
continue
tmp_one_packet.append(1)#包大小
tmp_one_packet.append(row[1])#协议
if((row[2] in ip_DDoS) and (row[3] in ip_DDoS)):#可能是DDoS
if((row[2]=="192.168.10.5") and (row[3]=="172.16.0.1")):
a=row[6]+' '+row[5]
elif((row[2]=="172.16.0.1") and (row[3]=="192.168.10.5")):
a=row[5]+' '+row[6]
tmp_one_packet[0]*=-1
if(a==''):
print("error! in %s %s is UDP" %(addr,num))
if(a not in fake_port):
if((a not in port_DDoS) and (a not in port_norm)):
if(row[2]=="172.16.0.1"):
port_DDoS.append(a)
elif(a=="192.168.10.50"):
port_norm.append(a)
elif(a in port_DDoS):
flows_DDoS.append(tmp_one_packet)
else:
a=row[2]+' '+row[3]
b=row[3]+' '+row[2]
if((a not in ip) and (b not in ip)):
ip[a]=len(ip)
flows_norm.append([])
if(a in ip):
tmp_one_packet[0]*=-1
flows_norm[ip[a]].append(tmp_one_packet)
elif((a not in ip) and (b not in ip)):
print("error: in csv_dataset")
elif(b in ip):
flows_norm[ip[b]].append(tmp_one_packet)
o=[]
o.append(flows_DDoS)
return o,flows_norm
def cons_graph(all_traffic,num_node,tags):
g_list=[]
print("len all_traffic: %s" %len(all_traffic))
num_graph=int(len(all_traffic)/num_node)
print("num_graph: %s" %num_graph)
#print(num_graph)
last_graph=len(all_traffic)-num_node*num_graph
for y in range(num_graph):
g = nx.Graph()
node_first=0
node_last=0
node_tags = []
#node_features = []
a=0
for z in range(num_node):
traffic=num_node*y+z
g.add_node(z)
if(all_traffic[traffic][1] not in lable_dict.keys() ):
lable_dict[all_traffic[traffic][1]]=len(lable_dict)
node_tags.append(lable_dict[all_traffic[traffic][1]])
#node_features.append(all_traffic[traffic][0])
if(z>0):#构造图的边
if(all_traffic[traffic][0]*all_traffic[traffic-1][0]>0):
#print(1)
g.add_edge(z-1, z)
#nx.draw(g,with_labels=True,pos=nx.circular_layout(g))
#plt.show()
else:
#print(2)
g.add_edge(z,node_first)
a=a+1
#plt.show()
if(a>=2):
g.add_edge(node_last,z-1)
#nx.draw(g,with_labels=True,edge_color='b',pos=nx.circular_layout(g))
node_first=z
node_last=z-1
g.add_edge(z,node_last)
g_list.append(S2VGraph(g, tags, node_tags))
return g_list
def load_data(degree_as_tag,para):
'''
dataset: name of dataset
test_proportion: ratio of test train split
seed: random seed for random splitting of dataset
'''
print('loading data')
g_list = []
fake_port=[[],[],[],[],[]]
LOIT=['52208 80', '53821 80', '55462 80', '57140 80', '58824 80', '60523 80', '62189 80', '63853 80', '65476 80', '27636 80', '28767 80', '30046 80', '31479 80', '33096 80', '34746 80', '36379 80', '38035 80', '39665 80', '41284 80', '42889 80', '43201 80']
hulk=['49632 80', '53058 80', '53688 80', '53682 80', '53658 80', '53648 80', '53642 80', '53634 80', '53630 80', '53660 80', '53598 80', '53578 80', '53568 80', '53566 80', '53554 80', '53552 80', '53528 80', '53512 80', '53510 80', '53506 80', '53496 80', '53494 80', '53490 80', '53472 80', '53468 80', '53470 80', '53458 80', '53440 80', '53438 80', '53436 80', '53426 80', '53422 80', '53846 80', '53858 80', '53866 80', '53872 80', '53878 80', '53890 80', '53896 80', '53902 80', '53912 80', '53918 80', '53926 80', '53932 80', '53940 80', '53948 80', '53956 80', '53982 80', '53990 80', '53996 80', '53898 80', '53922 80', '53964 80', '54018 80', '53740 80', '53742 80', '53766 80', '53794 80', '53812 80', '53808 80', '53818 80', '53850 80', '53862 80', '53874 80', '53892 80', '53904 80', '53910 80', '53942 80', '53958 80', '53984 80', '53992 80', '54006 80', '53788 80', '53798 80', '53796 80', '53810 80', '53826 80', '53842 80', '53856 80', '53864 80', '53870 80', '53900 80', '53888 80', '53924 80', '53930 80', '53938 80', '53952 80', '53970 80', '53978 80', '53988 80', '54002 80', '54016 80', '54012 80', '54024 80', '54026 80', '54030 80', '54032 80', '54034 80', '54040 80', '54046 80', '54048 80', '54050 80', '54058 80', '54056 80', '54060 80', '54066 80', '54062 80', '54070 80', '54080 80', '54082 80', '54084 80', '54096 80', '54100 80', '54114 80', '54126 80', '54140 80', '54146 80', '54144 80', '54154 80', '54166 80', '54178 80', '54182 80', '54190 80', '54192 80', '54200 80', '54210 80', '54214 80', '54208 80', '54216 80', '54220 80', '54232 80', '54228 80', '54234 80', '54238 80', '54244 80', '54252 80', '54256 80', '54258 80', '54274 80', '54282 80', '54286 80', '54318 80', '54268 80', '54280 80', '54276 80', '54298 80', '54308 80', '54266 80', '54272 80', '54278 80', '54304 80', '54312 80', '54292 80', '54314 80', '54370 80', '54362 80', '54376 80', '54344 80', '54388 80', '54368 80', '54378 80', '54360 80', '54420 80', '54428 80', '54432 80', '54446 80', '54448 80', '54454 80', '54408 80', '54412 80', '54422 80', '54452 80', '54462 80', '54480 80', '54484 80', '54504 80', '54518 80', '54530 80', '54532 80', '54542 80', '54562 80', '54564 80', '54576 80', '54592 80', '54614 80', '54626 80', '54630 80', '54632 80', '54650 80', '54654 80', '54656 80', '54672 80', '54676 80', '54680 80', '54684 80', '54692 80', '54704 80', '54708 80', '54714 80', '54716 80', '54718 80', '54724 80', '54726 80', '54730 80', '54736 80', '54738 80', '54754 80', '54766 80', '54764 80', '54768 80', '54774 80', '54780 80', '54800 80', '54798 80', '54806 80', '54808 80', '54810 80', '54822 80', '54824 80', '54840 80', '54842 80', '54844 80', '54850 80', '54852 80', '54854 80', '54860 80', '54866 80', '54872 80', '54874 80', '54876 80', '54884 80', '54886 80', '54890 80', '54892 80', '54894 80', '54898 80', '54904 80', '54912 80', '54916 80', '54920 80', '54938 80', '54942 80', '54950 80', '54956 80', '54968 80', '54972 80', '54974 80', '54978 80', '54980 80', '54986 80', '54994 80', '54998 80', '55006 80', '55010 80', '55022 80', '55024 80', '55026 80', '55028 80', '55032 80', '55034 80', '55040 80', '55048 80', '55052 80', '55056 80', '55058 80', '55064 80', '55068 80', '55070 80', '55072 80', '55076 80', '55094 80', '55104 80', '55108 80', '55116 80', '55128 80', '55134 80', '55144 80', '55148 80', '55156 80', '55164 80', '55172 80', '55184 80', '55186 80', '55190 80', '55204 80', '55210 80', '55214 80', '55218 80', '55228 80', '55232 80', '55242 80', '55244 80', '55246 80', '55260 80', '55274 80', '55272 80', '55288 80', '55302 80', '55312 80', '55314 80', '55316 80', '55320 80', '55324 80', '55338 80', '55340 80', '55352 80', '55358 80', '55366 80', '55374 80', '55376 80', '55386 80', '55390 80', '55396 80', '55402 80', '55400 80', '55410 80', '55416 80', '55422 80', '55420 80', '55424 80', '55448 80', '55454 80', '55456 80', '55458 80', '55464 80', '55466 80', '55484 80', '55490 80', '55498 80', '55504 80', '55512 80', '55518 80', '55516 80', '55532 80', '55534 80', '55546 80', '55542 80', '55558 80', '55570 80', '55578 80', '55582 80', '55604 80', '55612 80', '55620 80', '55628 80', '55632 80', '55634 80', '55638 80', '55640 80', '55644 80', '55652 80', '55658 80', '55670 80', '55678 80', '55686 80', '55692 80', '55696 80', '55698 80', '55702 80', '55706 80', '55708 80', '55714 80', '55718 80', '55726 80', '55728 80', '55762 80', '55768 80', '55772 80', '55780 80', '55778 80', '55786 80', '55782 80', '55792 80', '55798 80', '55802 80', '55800 80', '55804 80', '55810 80', '55814 80', '55828 80', '55826 80', '55832 80', '55830 80', '55836 80', '55838 80', '55856 80', '55858 80', '55862 80', '55868 80', '55870 80', '55876 80', '55878 80', '55884 80', '55894 80', '55904 80', '55910 80', '55912 80', '55914 80', '55916 80', '55920 80', '55918 80', '55946 80', '55954 80', '55952 80', '55976 80', '55986 80', '55988 80', '55992 80', '55998 80', '56006 80', '56012 80', '56044 80', '56042 80', '56060 80', '56076 80', '56074 80', '56090 80', '56092 80', '56096 80', '56098 80', '56106 80', '56108 80', '56110 80', '56112 80', '56116 80', '56118 80', '56122 80', '56120 80', '56130 80', '56134 80', '56144 80', '56148 80', '56146 80', '56150 80', '56158 80', '56172 80', '56168 80', '56178 80', '56180 80', '56182 80', '56184 80', '56190 80', '56192 80', '56188 80', '56194 80', '56196 80', '56204 80', '56202 80', '56218 80', '56216 80', '56240 80', '56242 80', '56244 80', '56248 80', '56262 80', '56264 80', '56268 80', '56274 80', '56278 80', '56276 80', '56282 80', '56284 80', '56296 80', '56298 80', '56294 80', '56292 80', '56302 80', '56308 80', '56318 80', '56324 80', '56326 80', '56340 80', '56336 80', '56360 80', '56368 80', '56372 80', '56396 80', '56400 80', '56414 80', '56408 80', '56416 80', '56426 80', '56432 80', '56438 80', '56440 80', '56436 80', '56444 80', '56470 80', '56482 80', '56486 80', '56488 80', '56498 80', '56502 80', '56510 80', '56512 80', '56516 80', '56534 80', '56536 80', '56540 80', '56546 80', '56548 80', '56552 80', '56566 80', '56564 80', '56568 80', '56580 80', '56586 80', '56600 80', '56598 80', '56596 80', '56604 80', '56612 80', '56618 80', '56622 80', '56624 80', '56626 80', '56628 80', '56630 80', '56640 80', '56644 80', '56646 80', '56650 80', '56652 80', '56658 80', '56656 80', '56662 80', '56660 80', '56664 80', '56672 80', '56688 80', '56700 80', '56696 80', '56712 80', '56716 80', '56726 80', '56728 80', '56724 80', '56732 80', '56730 80', '56734 80', '56740 80', '56744 80', '56736 80', '56748 80', '56746 80', '56750 80', '56752 80', '56762 80', '56766 80', '56772 80', '56776 80', '56784 80', '56790 80', '56792 80', '56796 80', '56800 80', '56816 80', '56818 80', '56820 80', '56834 80', '56840 80', '56850 80', '56860 80', '56862 80', '56866 80', '56874 80', '56872 80', '56882 80', '56884 80', '56886 80', '56890 80', '56892 80', '56904 80', '56908 80', '56910 80', '56912 80', '56914 80', '56918 80', '56920 80', '56922 80', '56936 80', '56938 80', '56950 80', '56952 80', '56954 80', '56960 80', '56966 80', '56970 80', '56976 80', '56990 80', '56992 80', '56994 80', '56996 80', '57002 80', '57006 80', '57008 80', '57010 80', '57012 80', '57014 80', '57016 80', '57018 80', '57022 80', '57026 80', '57032 80', '57044 80', '57046 80', '57050 80', '57052 80', '57062 80', '57066 80', '57064 80', '57068 80', '57074 80', '57076 80', '57080 80', '57084 80', '57092 80', '57094 80', '57096 80', '57098 80', '57100 80', '57106 80', '57108 80', '57116 80', '57118 80', '57124 80', '57126 80', '57128 80', '57130 80', '57132 80', '57136 80', '57142 80', '57144 80', '57148 80', '57150 80', '57152 80', '57154 80', '57156 80', '57158 80', '57160 80', '57162 80', '57166 80', '57168 80', '57170 80', '57172 80', '57174 80', '57178 80', '57182 80', '57184 80', '57188 80', '57190 80', '57192 80', '57194 80', '57200 80', '57202 80', '57204 80', '57206 80', '57210 80', '57214 80', '57216 80', '57218 80', '57222 80', '57226 80', '57230 80', '57232 80', '57236 80', '57238 80', '57254 80', '57260 80', '57268 80', '57280 80', '57278 80', '57284 80', '57292 80', '57296 80', '57302 80', '57306 80', '57310 80', '57320 80', '57322 80', '57328 80', '57340 80', '57352 80', '57358 80', '57362 80', '57364 80', '57378 80', '57372 80', '57384 80', '57386 80', '57388 80', '57390 80', '57394 80', '57412 80', '57426 80', '57454 80', '57458 80', '57470 80', '57474 80', '57478 80', '57482 80', '57484 80', '57488 80', '57490 80', '57492 80', '57498 80', '57500 80', '57508 80', '57506 80', '57514 80', '57516 80', '57520 80', '57526 80', '57528 80', '57530 80', '57536 80', '57538 80', '57540 80', '57542 80', '57544 80', '57548 80', '57556 80', '57558 80', '57564 80', '57566 80', '57568 80', '57570 80', '57574 80', '57576 80', '57582 80', '57586 80', '57596 80', '57600 80', '57602 80', '57604 80', '57608 80', '57618 80', '57628 80', '57646 80', '57650 80', '57652 80', '57654 80', '57656 80', '57664 80', '57668 80', '57670 80', '57680 80', '57682 80', '57686 80', '57692 80', '57690 80', '57710 80', '57712 80', '57716 80', '57720 80', '57728 80', '57736 80', '57738 80', '57744 80', '57754 80', '57756 80', '57766 80', '57768 80', '57774 80', '57778 80', '57780 80', '57784 80', '57786 80', '57788 80', '57794 80', '57800 80', '57802 80', '57804 80', '57812 80', '57810 80', '57824 80', '57828 80', '57838 80', '57844 80', '57852 80', '57854 80', '57882 80', '57886 80', '57894 80', '57908 80', '57922 80', '57930 80', '57932 80', '57934 80', '57938 80', '57956 80', '57958 80', '57964 80', '57972 80', '57970 80', '57968 80', '57974 80', '57978 80', '57980 80', '57992 80', '57998 80', '57996 80', '58002 80', '58008 80', '58026 80', '58032 80', '58038 80', '58042 80', '58044 80', '58046 80', '58048 80', '58080 80', '58086 80', '58094 80', '58096 80', '58108 80', '58112 80', '58122 80', '58120 80', '58134 80', '58138 80', '58140 80', '58154 80', '58160 80', '58168 80', '58170 80', '58174 80', '58192 80', '58202 80', '58210 80', '58216 80', '58214 80', '58220 80', '58222 80', '58224 80', '58226 80', '58232 80', '58234 80', '58238 80', '58242 80', '58244 80', '58246 80', '58250 80', '58260 80', '58268 80', '58272 80', '58280 80', '58284 80', '58294 80', '58296 80', '58300 80', '58302 80', '58306 80', '58308 80', '58310 80', '58312 80', '58314 80', '58316 80', '58318 80', '58320 80', '58322 80', '58324 80', '58326 80', '58328 80', '58330 80', '58344 80', '58358 80', '58362 80', '58366 80', '58368 80', '58376 80', '58380 80', '58386 80', '58388 80', '58392 80', '58394 80', '58400 80', '58398 80', '58404 80', '58402 80', '58406 80', '58408 80', '58410 80', '58414 80', '58420 80', '58422 80', '58426 80', '58424 80', '58430 80', '58434 80', '58444 80', '58448 80', '58454 80', '58456 80', '58462 80', '58460 80', '58464 80', '58466 80', '58470 80', '58474 80', '58472 80', '58486 80', '58492 80', '58496 80', '58498 80', '58502 80', '58504 80', '58508 80', '58510 80', '58514 80', '58516 80', '58518 80', '58522 80', 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'58522 80', '58524 80', '58532 80', '58540 80', '58542 80', '58550 80', '58554 80', '58560 80', '58564 80', '58570 80', '58572 80', '58576 80', '58578 80', '58584 80', '58588 80', '58590 80', '58592 80', '58594 80', '58648 80', '58650 80', '58656 80', '58654 80', '58658 80', '58660 80', '58662 80', '58666 80', '58668 80', '58670 80', '58676 80', '58674 80', '58684 80', '58686 80', '58692 80', '58700 80', '58704 80', '58710 80', '58714 80', '58718 80', '58726 80', '58742 80', '58750 80', '58758 80', '58768 80', '58770 80', '58778 80', '58782 80', '58784 80', '58788 80', '58790 80', '58792 80', '58806 80', '58808 80', '58810 80', '58914 80', '58928 80', '58934 80', '58948 80', '58946 80', '58972 80', '58976 80', '58980 80', '58992 80', '58996 80', '59000 80', '59010 80', '59018 80', '59030 80', '59034 80', '59040 80', '59042 80', '59044 80', '59050 80', '59054 80', '59058 80', '59092 80', '59094 80', '59098 80', '59100 80', '59102 80', '59106 80', '59108 80', '59110 80', '59114 80', '59118 80', '59128 80', '59146 80', '59148 80', '59158 80', '59168 80', '59170 80', '59180 80', '59182 80', '59184 80', '59186 80', '59192 80', '59196 80', '59202 80', '59204 80', '59210 80', '59208 80', '59224 80', '59228 80', '59230 80', '59234 80', '59244 80', '59246 80', '59248 80', '59254 80', '59256 80', '59258 80', '59274 80', '59304 80', '59308 80', '59310 80', '59312 80', '59316 80', '59318 80', '59324 80', '59326 80', '59332 80', '59344 80', '59348 80', '59362 80', '59364 80', '59368 80', '59378 80', '59384 80', '59382 80', '59388 80', '59392 80', '59406 80', '59410 80', '59412 80', '59416 80', '59414 80', '59438 80', '59442 80', '59460 80', '59464 80', '59468 80', '59470 80', '59472 80', '59474 80', '59476 80', '59480 80', '59484 80', '59482 80', '59486 80', '59488 80', '59490 80', '59492 80', '59496 80', '59498 80', '59502 80', '59500 80', '59504 80', '59510 80', '59516 80', '59514 80', '59524 80', '59530 80', '59532 80', '59534 80', '59536 80', '59540 80', '59542 80', '59544 80', '59546 80', '59550 80', '59554 80', '59552 80', '59560 80', '59566 80', '59570 80', '59572 80', '59574 80', '59576 80', '59578 80', '59582 80', '59584 80', '59586 80', '59590 80', '59592 80', '59602 80', '59606 80', '59614 80', '59618 80', '59626 80', '59628 80', '59630 80', '59632 80', '59638 80', '59640 80', '59644 80', '59652 80', '59660 80', '59662 80', '59664 80', '59668 80', '59676 80', '59678 80', '59680 80', '59682 80', '59690 80', '59692 80', '59694 80', '59696 80', '59702 80', '59708 80', '59710 80', '59714 80', '59716 80', '59722 80', '59726 80', '59742 80', '59746 80', '59754 80', '59762 80', '59768 80', '59774 80', '59792 80', '59794 80', '59798 80', '59800 80', '59804 80', '59808 80', '59818 80', '59820 80', '59824 80', '59836 80', '59838 80', '59842 80', '59846 80', '59856 80', '59862 80', '59868 80', '59876 80', '59894 80', '59896 80', '59902 80', '59908 80', '59912 80', '59914 80', '59926 80', '59936 80', '59938 80', '59940 80', '59946 80', '59950 80', '59954 80', '59958 80', '59976 80', '59996 80', '60002 80', '60018 80', '60020 80', '60024 80', '60028 80', '60034 80', '60050 80', '60052 80', '60058 80', '60070 80', '60078 80', '60082 80', '60084 80', '60086 80', '60090 80', '60100 80', '60102 80', '60114 80', '60122 80', '60120 80', '60128 80', '60132 80', '60134 80', '60138 80', '60146 80', '60152 80', '60156 80', '60158 80', '60160 80', '60164 80', '60172 80', '60180 80', '60178 80', '60186 80', '60192 80', '60198 80', '60210 80', '60216 80', '60234 80', '60238 80', '60236 80', '60250 80', '60252 80', '60254 80', '60258 80', '60256 80', '60270 80', '60272 80', '60274 80', '60278 80', '60280 80', '60284 80', '60282 80', '60286 80', '60290 80', '60296 80', '60302 80', '60306 80', '60312 80', '60326 80', '60328 80', '60332 80', '60340 80', '60342 80', '60344 80', '60348 80', '60352 80', '60360 80', '60364 80', '60368 80', '60372 80', '60374 80', '60376 80', '60380 80', '60384 80', '60388 80', '60390 80', '60398 80', '60406 80', '60412 80', '60420 80', '60422 80', '60424 80', '60438 80', '60442 80', '60452 80', '60466 80', '60480 80', '60488 80', '60490 80', '60500 80', '60502 80', '60508 80', '60518 80', '60520 80', '60524 80', '60542 80', '60550 80', '60554 80', '60560 80', '60562 80', '60568 80', '60574 80', '60576 80', '60580 80', '60598 80', '60604 80', '60608 80', '60610 80', '60612 80', '60622 80', '60624 80', '60628 80', '60636 80', '60642 80', '60650 80', '60658 80', '60660 80', '60662 80', '60674 80', '60676 80', '60680 80', '60706 80', '60708 80', '60716 80', '60714 80', '60730 80', '60732 80', '60734 80', '60738 80', '60740 80', '60746 80', '60754 80', '60756 80', '60764 80', '60766 80', '60786 80', '60802 80', '60808 80', '60816 80', '60814 80', '60822 80', '60836 80', '60840 80', '60856 80', '60852 80', '60864 80', '60868 80', '60876 80', '60894 80', '60898 80', '60912 80', '60910 80', '60916 80', '60918 80', '60932 80', '60942 80', '60944 80', '60956 80', '60960 80', '60958 80', '60970 80', '60986 80', '60990 80', '60988 80', '60992 80', '60994 80', '60998 80', '60996 80', '32778 80', '32772 80', '32786 80', '32782 80', '32788 80', '32790 80', '32798 80', '32796 80', '32806 80', '32808 80', '32804 80', '32812 80', '32816 80', '32828 80', '32832 80', '32836 80', '32842 80', '32838 80', '32862 80', '32868 80', '32872 80', '32882 80', '32886 80', '32888 80', '32890 80', '32896 80', '32906 80', '32900 80', '32910 80', '32912 80', '32914 80', '32928 80', '32926 80', '32930 80', '32950 80', '32956 80', '32954 80', '32962 80', '32966 80', '32974 80', '32986 80', '33002 80', '33006 80', '33016 80', '33018 80', '33026 80', '33036 80', '33042 80', '33054 80', '33050 80', '33058 80', '33070 80', '33072 80', '33078 80', '33076 80', '33102 80', '33104 80', '33108 80', '33112 80', '33118 80', '33130 80', '33136 80', '33132 80', '33162 80', '33164 80', '33166 80', '33178 80', '33176 80', '33188 80', '33200 80', '33202 80', '33210 80', '33206 80', '33214 80', '33230 80', '33240 80', '33242 80', '33244 80', '33254 80', '33258 80', '33272 80', '33276 80', '33278 80', '33292 80', '33290 80', '33288 80', '33294 80', '33310 80', '33316 80', '33314 80', '33328 80', '33330 80', '33340 80', '33348 80', '33352 80', '33358 80', '33360 80', '33362 80']
fake_port.append(GoldenEye)
fake_port.append(hulk)
fake_port.append(LOIT)
fake_port.append(slowhttptest)
fake_port.append(slowloris)
addr=["1","2","3","4","5","GoldenEye","Hulk","LOIT","slowhttptest","slowloris"]
#addr=["GoldenEye","Hulk","LOIT","slowhttptest","slowloris"]
tag=[0,0,0,0,0,1,2,3,4,5]
#tag=[0,1,2,3,4]
class_flow=6
num_node=para['num_node']
for i in range(len(addr)):
flows_DDoS,flows_norm=csv_dataset(addr[i],fake_port[i])
#load一个文件建一次图
if(tag[i]!=0):
for k in flows_DDoS:
#print("flow_DDoS:%s" % len(k))
g_list.extend(cons_graph(k,num_node,tag[i]))
#print("flow_DDoS:%s" %len(g_list))
for m in flows_norm:
#print("flow_norm:%s" % len(m))
li=cons_graph(m,num_node,0)
if(li != None):
g_list.extend(li)
#print("flow_norm:%s" %len(g_list))
for g in g_list:#
g.neighbors = [[] for i in range(len(g.g))]
for i, j in g.g.edges():
g.neighbors[i].append(j)
g.neighbors[j].append(i)
degree_list = []
for i in range(len(g.g)):
g.neighbors[i] = g.neighbors[i]
degree_list.append(len(g.neighbors[i]))
g.max_neighbor = max(degree_list)
#g.label = lable_dict[g.label]
edges = [list(pair) for pair in g.g.edges()]#拓展边，无向边
edges.extend([[i, j] for j, i in edges])
deg_list = list(dict(g.g.degree(range(len(g.g)))).values())
g.edge_mat = torch.LongTensor(edges).transpose(0,1)
if degree_as_tag:
for g in g_list:
g.node_tags = list(dict(g.g.degree).values())
#Extracting unique tag labels
tagset = set([])
for g in g_list:
#print(g.node_tags)
tagset = tagset.union(set(g.node_tags))
#print(tagset)
tagset = list(tagset)
tag2index = {tagset[i]:i for i in range(len(tagset))}
num_normal=0
num_DDoS=0
for g in g_list:
if(g.label==0):
num_normal+=1
if(g.label==1):
num_DDoS+=1
g.node_features = torch.zeros(len(g.node_tags), len(tagset))
g.node_features[range(len(g.node_tags)), [tag2index[tag] for tag in g.node_tags]] = 1
num_DDoS=len(g_list)-num_normal
print('# classes: %d' %class_flow)
print('# maximum node tag: %d' % len(tagset))
print("# data: %d" % len(g_list))
print("num_normal:%d" %num_normal)
print("num_DDoS:%d" %num_DDoS )
return g_list, class_flow
def separate_data(graph_list, seed, fold_idx):
assert 0 <= fold_idx and fold_idx < 10, "fold_idx must be from 0 to 9."
skf = StratifiedKFold(n_splits=10, shuffle = True, random_state = seed)
labels = [graph.label for graph in graph_list]
idx_list = []
for idx in skf.split(np.zeros(len(labels)), labels):
idx_list.append(idx)
train_idx, test_idx = idx_list[fold_idx]
train_graph_list = [graph_list[i] for i in train_idx]
test_graph_list = [graph_list[i] for i in test_idx]
return train_graph_list, test_graph_list
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