sudo suecho core >/proc/sys/kernel/core_pattern

afl-fuzz -i fuzz_in -o fuzz_out ./tcpdump  -ee -vv -nnr  @@

afl-showmap -o mapfile ./tcpdump -ee -vv -nnr  ./queue/id:000000,orig:small_capture.pcap

000087:1000142:1000248:1000928:1001092:1001322:1001382:1002101:4002141:1002184:1002346:1002403:2002589:1003031:2003072:1003160:2003220:1003251:1003567:1003574:2003827:1003984:2004084:1004178:4

import ospath = "文件目录"datanames = os.listdir(path)for i in datanames:    print(i)

import osinpath = "./tcpdump/queue"outpath= "./tcpdump/save_showmap"datanames = os.listdir(inpath)a=1##print(cmd)for i in datanames:    outname=outpath+'/'+str(a)    inname=inpath+'/'+i    cmd="afl-showmap -o {} ./tcpdump/tcpdump -ee -vv -nnr {}".format(outname,inname)    os.system(cmd)    a=a+1

import osinpath='./tcpdump/save_showmap'outpath='./tcpdump/save_binary'file_path = inpathdatanames = os.listdir(file_path)a=1for i in datanames: inname=inpath+'/'+str(a) outname=outpath+'/'+str(a) f_in=open(inname,'r') f_out=open(outname,'w') line=f_in.readline() last_number=1        while line:     now_number=int(line[:6])     ##print(now_number)     for j in range(last_number,now_number):         f_out.write(str(0)+'\n')     f_out.write(str(1)+"\n")     last_number=now_number+1     line=f_in.readline()     f_in.close() f_out.close() a+=1

a=1<<1000000print(a)

a=1b=11c=((a<<100000)-1) | (b<<100000)print(c)

a=1b=11c=((a<<100000)-1) | (b<<100000) def count_one(x):    s=0    while(x):        if (x & 1):            s+=1        x=x>>1    return s print(count_one(c))

import osinpath='./tcpdump/save_showmap'outpath='./tcpdump/save_bignum'file_path = inpathdatanames = os.listdir(file_path)a=1for i in datanames: inname=inpath+'/'+str(a) outname=outpath+'/'+str(a) f_in=open(inname,'r') f_out=open(outname,'w') big_number=0 line=f_in.readline()        while line:     now_number=int(line[:6])     big_number+=1<<now_number     line=f_in.readline()     f_out.write(str(big_number)) f_in.close() f_out.close() a+=1

#以下代码为：生成随机散点图from turtle import colorimport numpy as npimport matplotlib.pyplot as pltfrom pyclustering.cluster.center_initializer import kmeans_plusplus_initializerfrom pyclustering.cluster.kmeans import kmeans ## 前面是安装的库cluster_num=20 def draw_line(a,b):    a_x=a[0]    a_y=a[1]    b_x=b[0]    b_y=b[1]    plt.plot([a_x,b_x], [a_y,b_y],linewidth=1,color='green') def add_line_between_center_and_members(cs,group,x): # cs:聚类中心 group:二维数组表示的分组情况 x:原始的二维数组    for i in range(0,cluster_num):        for j in group[i]:            draw_line(cs[i],x[j]) def draw_initial_point(x):    x1=[]    y1=[]    for i in x:        x1+=[i[0]]        y1+=[i[1]]    plt.plot(x1,y1, 'o',color='b') ## 画初始的点，把一个二维数组拆成一维 def draw_center(cs):    x1=[]    y1=[]    for i in cs:        x1+=[i[0]]        y1+=[i[1]]    ###print(x1)    plt.plot(x1,y1, 'o',color='r')       ##plt.scatter(i[0],i[1],'o',color='r') ## 画聚类中心## 聚类个数 kmeans中的kx=np.random.randint(0,100,(100,2)) print("初始数据：")print(x) ## 随机一个二维数组draw_initial_point(x)initial_centers = kmeans_plusplus_initializer(x, cluster_num).initialize()kmeans_instance = kmeans(x, initial_centers)kmeans_instance.process()clusters = kmeans_instance.get_clusters()cs = kmeans_instance.get_centers() ## 详见：https://segmentfault.com/a/1190000039785725 print("聚类中心：")print(cs)draw_center(cs)print("分类情况：")print(clusters)print("test:")##plt.plot(x1,y1, 'o',color='b')add_line_between_center_and_members(cs,clusters,x)plt.show()

#以下代码为：生成随机散点图from turtle import colorimport osimport numpy as npimport matplotlib.pyplot as pltfrom pyclustering.utils.metric import distance_metric, type_metricfrom pyclustering.cluster.kmeans import kmeans, kmeans_visualizerfrom pyclustering.cluster.center_initializer import kmeans_plusplus_initializerfrom pyclustering.cluster import cluster_visualizerfrom pyclustering.samples.definitions import FCPS_SAMPLESfrom pyclustering.utils import read_sample ## 前面是安装的库inpath = "./save_bignum"datanames = os.listdir(inpath)slen=len(datanames)x=np.random.randint(0,1,(slen,2))mp=np.random.randint(0,1,(slen+1000,slen+1000)) for i in range(0,slen):    x[i,0]=i+1    x[i,1]=1print(x) def my_manhattan(p1,p2):    print(str(p1[0])+"  "+str(p2[0]))    if mp[p1[0]][p2[0]]:        return mp[p1[0]][p2[0]]    f1=open(inpath+'/'+str(p1[0]))    f2=open(inpath+'/'+str(p2[0]))    s1=int(f1.read())    s2=int(f2.read())    f1.close()    f2.close()    s=s1 ^ s2    ans=0    while(s):        if(s&1):            ans+=1        s>>=1    mp[p1[0]][p2[0]]=mp[p2[0]][p1[0]]=ans    return ans  my_metric = distance_metric(type_metric.USER_DEFINED, func=my_manhattan) cluster_num=100initial_centers = kmeans_plusplus_initializer(x, cluster_num).initialize()kmeans_instance = kmeans(x, initial_centers,metric=my_metric)kmeans_instance.process()clusters = kmeans_instance.get_clusters()cs = kmeans_instance.get_centers()

screen -S [Name] ##创造一个名字为Name的screenscreen -ls ##看查当前所有screen和编号

screen -r [ID] ps:如果连不上，先screen -d[ID]再-r

screen -X -S [ID] quit