BOTCLOUDS
The Future of Cloud-based Botnets?
Kassidy Clark, Martijn Warnier and Frances M. T. Brazier
Faculty of Technology, Policy and Management, Delft University of Technology, Delft, The Netherlands
Keywords:
Botnet, Click fraud, Cloud, Cloud services, DDoS, Extrusion detection, Intrusion detection, Security.
Abstract:
Many Cloud Service Providers (CSP) offer access to scalable, reliable computing resources following a pay-
as-you-go model. Research into security of the Cloud focusses mainly on protecting legitimate users of Cloud
services from attacks by external, malicious users. Little attention is given to prohibit malicious users from
using the Cloud to launch attacks, such as those currently done by botnets. These attacks include launching
a DDoS attack, sending spam and perpetrating click fraud. This paper discusses the threat of Cloud-based
botnets, or botclouds and the need for new techniques to detect them. Two experiments show how simple and
cheaply these attacks can be launched from botclouds.
1 INTRODUCTION
Cloud services refer to the provisioning of hardware
and software resources across the Internet (Armbrust
et al., 2010). Cloud Service Providers (CSP) typically
offer both refined software services, such as databases
and raw compute resources, such as storage or pro-
cessing power. Customers often use these services
following a pay-as-you-go model. Using Cloud ser-
vices, companies can choose to, in effect, rent com-
puter resources, rather than to invest in them outright,
also providing elasticity of computing resources. For
instance, if a cloud service customer discovers that
he has over estimated his needs and has thus over-
provisioned cloud services, he can scale down the size
of his cloud. The reverse is also true, if a customer
finds that he has under-provisioned cloud services, he
can scale up the size of his cloud with little effort.
There are a growing number of CSPs includ-
ing Microsoft, Google and Amazon Web Services
(AWS). AWS is currently the largest
1
. AWS offers a
web interface for human access, as well a scriptable,
Java-based Application Programming Interface (API)
for automated access.
Research into the security of the Cloud focuses
primarily on protecting legitimate users of Cloud ser-
vices from attacks by external, malicious users (An-
thes, 2010; Maggi and Zanero, 2010; Chen et al.,
1
Techno-Pulse. http://www.techno-pulse.com/2009/12/
top-cloud-computing-service-providers.html. Accessed:
November 2010.
2010). Little attention has been given to the possi-
bility of malicious users using the Cloud to launch at-
tacks, such as those currently performed by botnets.
There is evidence that botnets are moving into the
Cloud
2
. Due to their ease of use, high reliability and
scalability, Cloud services provide malicious users a
new platform with which to launch attacks, literally
with the click of a button.
This paper discusses the possibility of attacks
originating from Cloud-based botnets, or botclouds.
The simplicity of these attacks is demonstrated in two
experiments. Section 2 gives an overview of botnets,
including attacks, structure and detection techniques.
The concept of botclouds is presented in Section 3.
Two botcloud attacks are demonstrated in Section 4.
Finally, the implications of botclouds and botcloud
detection are discussed in Section 5.
2 BOTNETS
A bot is a partially autonomous piece of software that
can be controlled remotely. The person controlling a
bot is referred to as a botmaster. A group of bots un-
der control of a botmaster is referred to as a botnet. A
botnet is created by first infecting a computer without
the knowledge or consent of its owner by, for exam-
ple, sending a virus as an email attachment (Ianelli
2
SecurityFocus. http://www.securityfocus.com/brief/
1046. Accessed: November 2010.
597
Clark K., Warnier M. and M. T. Brazier F..
BOTCLOUDS - The Future of Cloud-based Botnets?.
DOI: 10.5220/0003381305970603
In Proceedings of the 1st International Conference on Cloud Computing and Services Science (CLOSER-2011), pages 597-603
ISBN: 978-989-8425-52-2
Copyright
c
2011 SCITEPRESS (Science and Technology Publications, Lda.)
and Hackworth, 2005). Once a computer is infected
with bot software, it contacts the botmaster. At this
moment, the bot becomes part of the botnet. The bot-
master can then send orders to the bot to carry out
(malicious) tasks. Botnets can comprise thousands or
even millions of bots, such as the BredoLab botnet
estimated to have 30 million bots
3
.
2.1 Botnet Attacks
Common attacks launched by botnets include (1)
launching Distributed Denial of Service (DDoS) at-
tacks, (2) sending spam email and spreading malware,
(3) stealing private information and (4) performing
click fraud (Jing et al., 2009; Ianelli and Hackworth,
2005).
DDoS attacks are used to overload a target’s
servers so that legitimate traffic can no longer access
them (Mirkovic and Reiher, 2004). This is achieved
by simultaneously flooding a target domain with re-
quests until the response time to load a webpage is
longer than a legitimate user is willing to wait. Thus,
the system appears to have crashed. In some cases,
just the threat of a DDoS attack is often enough for
criminals to extort money from businesses.
Another common use of botnets is the sending
of spam and malware. It is estimated that more
than 97% of all email is unsolicited, bulk email (also
known as spam) and the majority is generated by bot-
nets (Anselmi et al., 2010). E-mail containing spam
messages or malicious attachments can be sent from
an infected machine using either a user’s personal ac-
count or their Internet Service Provider’s (ISP) e-mail
server. If each single machine sends only 10 such
messages per day, the massive size of most botnets
can thus send millions of spam messages each day.
Private information is stolen from bot infected
computers using keyloggers or making periodic
screenshots (Ianelli and Hackworth, 2005). Keylog-
gers capture passwords and usernames at the moment
that these are entered into a protected website, such as
a personal email site. In addition, a bot can identify
and copy sensitive financial data, such as credit card
information.
Click fraud can be used to attack different targets
for different reasons. A typical use of click fraud is
to intentionally click on advertisements from pay-per-
click providers, such as Google or Adsense (Kshetri,
2010). There are two general motivations for this kind
of fraud: inflationary and competitive (Wilbur and
Zhu, 2009). Inflationary click fraud is when attackers
3
InfoSecurity. http://www.infosecurity-magazine.com/
view/13620/bredolab-downed-botnet-linked-with-
spamitcom/. Accessed: November 2010.
can earn money by clicking on advertisements they
themselves are hosting. The pay-per-click providers
then charge the targeted company for these clicks and
pass this money on to those who actually host the ad-
vertisements, in this case the attackers.
Competitiveclick fraud is when attackers click the
advertisements of a company with the goal of driving
up that company’s advertising costs. In this case, the
attackers click on any advertisement of that company,
regardless of where they are hosted and regardless of
who receives money for the click.
Another use of click fraud is to artificially influ-
ence online polls. Online polls allow users to vote
on various topics. Examples include news sites that
poll how their readers feel about a certain news story
or software developers that poll their users for feature
requests. Often there is no monetary incentive to in-
fluence the results, but rather only a psychic benefit
such as enjoyment.
2.2 Botnet Classification
Detecting and combating botnets remains a difficult
task, but some progress has been made. Different bot-
net detection techniques correspond to different net-
work structures (Dagon et al., 2007) and communica-
tion protocols (Jing et al., 2009). Based on these two
attributes, two general classifications are recognized:
Internet Relay Chat (IRC) based botnets and (newer)
Peer to Peer (P2P) based botnets.
IRC is a text-based messaging protocol based on
the traditional client/server model. An infected com-
puter acts as an IRC client and contacts the IRC server
to join the network. The botmaster then uses the IRC
server to send instructions and data to the client for
execution. This structure is highly centralized around
the IRC server(s). If a server is taken offline, all bots
that depend on it will no longer be able to receive or
execute further instructions.
P2P based botnets use a more decentralized, dis-
tributed model without a centralized server. Each bot
has a list of known peers comprising only a small part
of the entire botnet. A botmaster sends data and in-
structions to one or more peers, that in turn pass these
on to all other peers they know. These messages then
traverse the entire network. As there is no central
server, there is also no central point of failure, thus
making this class of botnet more robust against attack.
Botnet creators havefound that truly decentralized
networks are relatively slow to organize and react to
new commands, so most P2P based botnets employ
a hybrid structure. Hybrid structures use a layer of
super-peers that maintain long lists of known peers.
When a new peer joins the network, it first contacts a
CLOSER 2011 - International Conference on Cloud Computing and Services Science
598
super-peer and downloads this list. This download
process and the role of these super-peers makes the
hybrid structure more prone to detection by system
users and administrators (Schoof and Koning, 2007).
2.3 Botnet Detection Techniques
The main methods for traditional botnet detection are
honeypots and intrusion detection (Zeidanloo et al.,
2010). Honeypots are unprotected computers that are
intentionally (allowed to be) infected by botnets. Re-
searchers observe the bots to learn about the rest of the
network, including its behavior, structure and identi-
ties of its members. With this information, they can
take countermeasures against the rest of the network.
Intrusion detection is the process of monitoring a
host or network and analyzing the incoming network
traffic. Traffic can be analyzed for known botnet ac-
tivity or for general suspicious anomalies.
DNS tracking is a type of intrusion detection
that analyzes DNS queries between bots and their
server (Jing et al., 2009). As most bots must first
contact a server (or super-peer) to join the network,
researchers look for these messages to learn which
computers are infected. For instance, if a particular
IRC server is known, researchers can analyze net-
work traffic for DNS queries to this server. This re-
veals which computers are infected. This also works
in the opposite direction, if a computer is known to
be infected (e.g. using a honeypot), researchers can
watch for DNS queries to the server, thus learning
where the server is located. Once learned, the ad-
dress of the server can be added to public DNS black-
lists (DNSBL). Queries to blacklisted addresses are
blocked, thus preventing new from joining the botnet.
General traffic analysis is another type of intru-
sion detection that can be applied to both IRC and
P2P based botnets. This technique analyzes messages
being sent to and from a machine and compares the
type, content, protocol or messaging pattern to a list
of known botnets. For instance, certain botnets are
known to either use certain ports, or send messages
of a certain size, or at a certain interval, or to a cer-
tain type of host (Chandrashekar, 2009; Noh et al.,
2009). To detect previously unknown botnets, re-
searchers can look for anomalies or unusual network
activity. For instance, if ‘typical’ network usage is
known, then suspicious anomalies that differ from this
safe baseline can be investigated.
3 BOTCLOUDS
Rather than use a network of infected machines, Bot-
masters can use Cloud services to build botnets. Bot-
masters purchase a large group of machines from a
CSP and install a bot on each machine to form a bot-
net. Cloud-based botnets, or botclouds, have several
advantages over traditional botnets. A traditional bot-
net requires substantial time to build, whereas a bot-
cloud can be online in minutes. In addition, while a
botnet is unreliable due to the constant threat of in-
fected computers being switched off by their owners,
a botcloud is always online and ready. Finally, a bot-
net cannot fully utilize the processor or bandwidth re-
sources due to the constant threat of detection or com-
puter use by the owner; however, a botcloud can be
fully utilized with no fear of interruption.
3.1 Botcloud Attacks
A DDoS attack can be launched by a botcloud and
can, at least temporarily, have the same effect as a
DDoS attack launched by a traditional botnet. This
attack can, in theory, be detected and neutralized by a
CSP. This, however, will only work if they are moni-
toring for this type of activity. For instance, if a CSP
is actively monitoring outgoing traffic for surges of
HTTP requests that match a known pattern of DDoS
activity, the host sending these packets can be iden-
tified and, as a consequence, disconnected or shut
down. Therefore, a botcloud perpetrating this attack
can be successful in shutting down the target domain,
but perhaps only for a short period of time.
Sending spam from a botcloud can be (and has
been
4
) accomplished. A common defense against
spam is to blacklist the range of IP addresses from
which the spam is being sent. However, blacklisting
a large range of IP addresses of a CSP might block
access to many legitimate services, such as customers
that are hosting their email servers in the Cloud. This
very problem was encountered when the spam black-
list service Spamhaus.org detected spammers in the
Amazon Cloud and thus blocked a large block of IP
addresses used by Amazon Cloud customers
5
4. This
attack could also have been detected by a CSP, but
only if it is actively monitoring for it. Therefore, a
botcloud perpetrating this attack may be able to send
massive amounts of spam, but only for a short period
of time.
Click fraud can also be carried out by a botcloud.
This attack is more difficult to detect than the first
4
Washington Post. http://blog.washingtonpost.com/secur
ityfix/2008/07/amazon hey spammers get off my.html.
Accessed: November 2010.
BOTCLOUDS - The Future of Cloud-based Botnets?
599
two. Most detection techniques work from the side
of the pay-per-click provider to differentiate between
clicks generated by humans and those generated by
machines (Haddadi, 2010; Zhang and Guan, 2008). If
a fraudulent click is detected, it is removed from the
official tally, but no further action is taken. Unfortu-
nately, pay-per-click providers do not have a strong
incentive to stop click fraud. In fact, they have a
strong economic incentive to allow it to occur as they
have financial benefit from each click, regardless of
its validity (Kshetri, 2010). Therefore, a botcloud per-
petrating click fraud could remain in the Cloud for a
long period of time without detection. Without so-
phisticated traffic analysis on the part of the CSP, this
attack might never be detected or permanently neu-
tralized.
3.2 Botcloud Detection
As discussed above, the main methods of botnet de-
tection are honeypots and intrusion detection. Porting
these methods to the Cloud is not a straightforward
process. Deploying honeypots in the Cloud requires
that a CSP monitors all activity on all or a subset of
the machines used by CSP customers. As customers
have paid for these machines, there may be (legal) ob-
jections to this privacy breach (Ruiter and Warnier,
2011). Furthermore, even if all system activity is
monitored, it is non-trivial to differentiate legitimate
from illegitimate activity.
Deploying intrusion detection software on each
machine of the Cloud individually is similarly com-
plex. Most intrusion detection algorithms are trained
using a safe baseline of normal’ incoming network
activity. This baseline is compared to new patterns of
network activity to determine if abnormal, and thus
suspicious, activity is occurring. In the Cloud, this
would require working closely with each individual
customer to develop this safe baseline; an impractical
task.
In addition to monitoring network activity enter-
ing the Cloud, network activity exiting the cloud can
also be monitored for suspicious activity. This is re-
ferred to as outbound intrusion detection or Extrusion
Detection and has been applied to the domain of Inter-
net Service Providers detecting outgoing spam (Clay-
ton, 2004). Proactivelymonitoring for suspicious out-
bound activity, such as DDoS or spam, will alert a
CSP to the presence of malicious users and prompt
further action, such as termination of those users.
Click fraud remains the exception and requires close
collaboration between pay-per-click service providers
and CSPs. Without these minimal security measures,
CSPs leave it to the victims of botcloud attacks to re-
port the attack. This approach, while costing less time
and money for the CSP will possibly lead to a bad
reputation of that CSP.
4 EXPERIMENTATION
This section discusses two experiments to demon-
strate the possibility of creating a botcloud and
launching attacks. For both experiments, a leading
CSP was used to lauch the attacks and a web server,
owned by the authors of this paper, was used as the
target machine.
4.1 DDoS Attack
The first experiment builds a botcloud to perform a
DDoS attack on a low-end, personal web server. The
server is a Dell PowerEdge 830 with a 2.6GHz pro-
cessor and 2GB RAM. The server runs Debian Linux
and the Apache Webserver with PHP and MySQL ca-
pability. The server hosts a test site consisting of a
small (< 5K), static HTML page and a dynamically
generated PHP page that queries a MySQL database
when executed.
A separate workstation is used to monitor the re-
sponsiveness of the server. This is done using a stan-
dard web browser and refreshing the test site period-
ically. Before the attack, the page refreshes in less
than one second. In addition, the server is contacted
every second using the ping command. Before the at-
tack, the server consistently responds in less than 100
milliseconds.
A fully automated script creates a botcloud of 20
bots. Each bot is commanded to send 1000 simulta-
neous requests to the web server for a period of 60
seconds. When the script is executed, it creates a bot-
cloud that sends 20,000 simultaneous page requests
per second for a duration of one minute.
The essence of this script is shown in Figure 1.
The script works as follows. First, a machine in-
stance is initiated using the run-instances CSP com-
mand. This command can specify the disk image,
number of machines and machine type (e.g. amount
of memory or disk). An asymmetric key (cloudkey) is
used to automate the login process. After the machine
has started, the unique identifier (IDENT) is used to
lookup the IP address (IPADDRESS) by executing the
describe-instances CSP command. Once the IP ad-
dress is known, a new process is spawned that logs
into the newly created machine using the cloudkey.
This process then executes the httperf script to flood
the target with HTTP requests. Finally, the machine
CLOSER 2011 - International Conference on Cloud Computing and Services Science
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# begin loop of 20 bots
for i in { 1 . .2 0 }
do
# i n i t i a t e a bot in s tanc e
IDENT=run in s t an ce s disk image1 n 1 g open k cloudkey1 t type1 | grep INSTANCE | cut f 2
# get ip a ddress of runnin g bot
IPADDRESS= de s crib e i n s t an ce s $IDENT | grep INSTANCE | cu t f 4
# spawn new process to lauch 1000 re q ues t s per second for 60 seconds
ssh o StrictHostK eyChecking =no i ˜ / bin / cloudkey1 . pem root@$IPADDRESS \
h t t p e r f hog www. example . com ur i / index . html r a t e 1000 numconn 60000 time out 1 &
# term i n ate bot
ter m i nate in st an c e s $IDENT
done
Figure 1: Summary of DDoS botcloud script.
is shutdown using the terminate-instances CSP com-
mand.
This attack overloaded the server. After only 10
seconds, the web browser could no longer refresh the
page. Server response to ping requests slowed down
until approximately only one of 10 requests were an-
swered while the others timed-out (after one second).
This particular script targeted a plain HTML page, but
could be modified to request a page that requires more
server processing, such as a PHP page that queries a
local database. This would require fewer requests to
overload the server.
4.2 Click Fraud
The second experiment builds a botcloud to perform
click fraud on an online poll hosted by a local server.
The server hardware and software is identical to the
description in the first experiment. To simulate stan-
dard click fraud detection techniques, the server ex-
ecutes tests before accepting a vote as legitimate.
First, the web server checks for a unique IP address.
Therefore, no single machine can vote twice from the
same IP address. Secondly, to prevent the same ma-
chine from voting from multiple IP addresses (e.g. by
switching networks), the server saves a cookie on the
machine casting the vote. Before accepting a vote, the
server tests for the presence of this cookie. If present,
the vote is rejected.
The final fraud detection technique records a
unique identifier for each vote cast. This unique iden-
tifier is created by adding a timestamp to each vote.
These timestamps are logged for later analysis.
An automated script creates a botcloud of 1000
bots. Each bot is commanded to cast a single, fraud-
ulent vote. When the script is executed, it creates a
botcloud that casts 1000 fraudulent votes, each with a
unique IP address. The essence of this script is shown
in Figure 2.
This script works as follows. First, a bot instance is
initiated using the run-instances CSP command. Af-
ter the machine starts, the unique identifier (IDENT)
is used to lookup the IP address (IPADDRESS) of the
machine. Once the address is known, a new process is
spawned to login to the bot using the cloudkey. This
process then executes the wget command to execute
the fraudulent vote. Finally, the bot is terminated us-
ing the terminate-instances CSP command.
To avoid timestamp anomaly analysis done by the
web server, randomness is added to the script in two
places. First, between loops, the script rests for a ran-
dom length of time (e.g. 2 seconds, 5 minutes, etc.).
This prevents the web server from detecting inexpli-
cable surges of votes. Second, random padding is
added to the timestamp of each vote request. This
is done by choosing a random, three digit number and
appending this to the vote request.
The attack completed successfully. The web
server registered 1000 votes from unique machines
with unique IP addresses. Furthermore, visual analy-
sis of the voting log and its timestamps did not reveal
any obvious anomalies, such as clusters of votes.
5 DISCUSSION
While these experimentsmay not accurately represent
the effects of a large scale attack, they demonstrate
the possibility of constructing such an attack. They
illustrate the straightforward manner with which such
attacks can be launched. Both of these attacks were
constructed and executed in less than one day and for
approximately 100 euros and both were successful in
their respective goal. Furthermore, neither attack was
detected or shutdown by the CSP.
The price of renting a botnet is difficult to esti-
mate, as these activities take place on the obscured
online black markets. However, some researchers at
BOTCLOUDS - The Future of Cloud-based Botnets?
601
# begin loop of 1000 b ots
for i in { 1..1000}
do
# r e s t for random l engt h of t ime ( to avoid d et e ct io n )
REST=$RANDOM
l e t REST %= $MAXREST”
sl e ep $REST
# i n i t i a t e a bot in s tanc e
IDENT=run in s t an ce s disk image1 n 1 g open k cloudkey1 t type1 | grep INSTANCE | cut f 2
# get ip a ddress of runnin g bot
IPADDRESS= de s crib e i n s t an ce s $IDENT | grep INSTANCE | cu t f 4
# random padding ( to avoi d d et e c t i o n )
PADDING=0
while [ $PADDING” le 100 ]
do
PADDING=$RANDOM
l e t PADDING %= 1000
done
DATE= da t e +%s $PADDING
# spawn new process to c a st fr a u d u l e n t vo t e
ssh o StrictHostK eyChecking =no i ˜ / bin / cloudkey1 . pem root@$IPADDRESS \
wget h t t p : / /www. example . com / frontmodules / vote . c l a s s . php ? id =786359& =$DATE &
# term i n ate bot
ter m i nate in st an c e s $IDENT
done
Figure 2: Summary of click fraud botcloud script.
VeriSign estimate this price between $9 an hour and
$67 a day
6
. While this is cheaper than the equivalent
botcloud, botnets are also less reliable than Cloud ser-
vices, as discussed above. To make matters worse,
criminals willing to launch botnet attacks are most
likely also willing to commit identity theft. When
creating an account for Cloud services, a false name
and stolen credit card information is used
7
, thus mak-
ing the cost of the service a non-issue. With a dozen
stolen credit cards, a criminal could launch a series of
a dozen botclouds, possibly on different CSPs. When
one Cloud is finally detected and shut down, the next
is launched, and so on, resulting in an ongoing, mas-
sive attack.
The experiments presented above used a relatively
small number of machines to launch the attacks, how-
ever the very nature of Cloud services allows these
attacks to scale. The same scripts could be used to
launch an attack using thousands or tens of thousands
of machines across multiple CSPs.
As botnet detection techniques continue to im-
prove and the price of Cloud services continues to
drop, botmasters will move their activities to the
Cloud. However, most current botnet detection meth-
6
VeriSign. http://www.verisign.co.uk/press/page
20100
4.html. Accessed: December 2010
7
O’Reilly Community. http://broadcast.oreilly.com/
2009/03/blame-the-credit-card-franchis.html. Accessed:
November 2010
ods will not easily port to the Cloud. The CSP is in
the best position to detect botclouds and should be
responsible for maintaining intrusion detection mech-
anisms to detect not only incoming attacks (as they
do now) but also outgoing attacks. The CSP has com-
plete control of the Cloud and thus complete control
of any botclouds they detect. In general, the CSP is
in a better position to detect attacks better than other
actors. For instance, an extrusion detection system
designed to identify click fraud could be deployed
across all nodes under the control of a single CSP. To
achievethe same coverageoutside of the Cloud would
require multiple Internet Service Providers (ISP) to
implement and coordinate the same system.
As with the pay-per-click providers mentioned
earlier, CSPs do not currently have a strong incentive
to monitor all customers from the time they start using
Cloud services. As long as the customer pays, they
can use the service. Moreover, analyzing all outgoing
traffic costs time and resources. As long as CPSs con-
tinue to take action to shut down malicious users only
after the fact (e.g. after the attack has been carried
out), Cloud based attacks will increase.
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602
6 CONCLUSIONS
This paper discusses the ability to launch attacks from
within the Cloud against external targets. Two ex-
periments demonstrate the simplicity and low cost of
launching such attacks. Porting traditional botnet de-
tection techniques to the Cloud is not straightforward,
thus new techniques are required. One possible tech-
nique is extrusion detection. This would require CSPs
to monitor outbound traffic to detect and respond to
suspicious activity. Current policy is to wait until
the victims of attacks contact the responsible CSP at
which point action is taken to disable the attack. Until
CPSs implement a comprehensive botcloud detection
and removal policy, botmasters will continue to move
their malicious activities into the Cloud and botclouds
will continue to grow.
Possible areas of future work include research into
Cloud deployment of extrusion detection systems and
designing incentives for CSPs to proactively monitor
for botclouds.
ACKNOWLEDGEMENTS
This work is a result of support provided by the NLnet
Foundation (http://www.nlnet.nl).
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