CAMP: Content-Agnostic Malware Protection
Moheeb Abu Rajab, Lucas Ballard, Noé Lutz, Panayiotis Mavrommatis, Niels Provos
Google Inc.
1600 Amphitheatre Parkway, Mountain View, CA 94043

is now in control of the computer and no vulnerabilities or
exploits were required for the installation [28], [12].
Traditional defense mechanisms such as Anti-Virus (AV)products are often ineffective against current malware downloads as AV engines tend to be content based and provide
an oracle to malware authors allowing them to repackage
their software until it is no longer detected [24]. Similarly,
URL blacklists such as Google’s Safe Browsing API [16]work well for identifying compromised web pages which tend
to be relatively static, but cannot be completely up to datein identifying all the domains from which malware is being
distributed. The abundance of dynamic DNS providers allow
for frequent rotation of domains from which malware is being
distributed. If the domains rotate faster than the update intervalof the malware lists, domains distributing malware are more
likely to remain undetected [3], [27].
The malware arms race has shown that neither signature
based AV engines nor malware lists are sufficient to protect
users. A whitelist based approach in which only trusted
software can be installed is more promising in that it cancompletely block the installation of socially engineered malware [6], [11]. Since such malware is not on the whitelist,
installation of it will not be allowed. However, whitelists sufferfrom drawbacks, too. The sheer variety of available softwareand the perpetual creation of new software makes the task
I. I NTRODUCTIONof creating a whitelist that covers all benign software nearly
Malware is a popular venue for monetization in the un- impossible. As a result, neither whitelist nor blacklist based
derground economy [7]. Adversaries frequently compromise approaches provide sufficient coverage to protect users fromvulnerabilities in users’ machines to automatically install mal- malicious software in practice.
ware via so-called drive-by downloads. The action of visiting
This paper offers a different approach in which the gap bea malicious web page in a vulnerable browser is sufficient tween known benign and known malicious software is bridged
for an adversary to gain complete control over the vulnerable by a content-agnostic reputation-based detection approach.
machine allowing her to install arbitrary software. Frequently, CAMP, our detection system, consists of a client component
installed malware can cause the computer to become part of built into Google Chrome and a server component responsible
a botnet or can exfiltrate sensitive data such as credit card for maintaining a reputation system that predicts the likelihood
numbers to be sold in the underground economy [20].
that a downloaded binary is malicious. CAMP makes use
As browsers and plugins are becoming more secure, the of Google’s Safe Browsing API to detect downloads known
vulnerable user base is shrinking and drive-by downloads to be malicious. For all binaries not detected, we extract
are becoming less effective as a malware distribution vector. additional features from information available to the web
While drive-by downloads are still a popular distribution vec- browser about the download and use these features to build ator, adversaries are increasingly turning to social engineering server-based reputation system. The reputation system predicts
for malware distribution. Fake Anti-Virus products are one if an unknown binary is malicious without prior knowledge ofsuch example in which the user is led to believe that their its content. We built CAMP to protect users of Google Chrome
computer has been infected and a free product to remedy the from downloading malware on Windows. Our system protects
situation is offered. Consequently, the user voluntarily down- over 200 million users, makes millions of reputation-basedloads and installs malware under the guise of a free security decisions every day, and identifies about 5 million malware
solution. The benefit to the adversary is that the malware downloads every month beyond the malware warnings that
Abstract---In spite of recent advances, the world wide web remains an important vector for malware installation. Approaches
to evaluating potentially malicious code before execution in abrowser, such as blacklisting or content-based detection arehindered by an attacker’s ability to easily change hosting domains
or mutate malware binaries. On the other hand, whitelistbased approaches are challenged by the large, dynamic, andheterogeneous space of benign binaries that they must track. In
practice these approaches continue to provide value for popularbinaries at either extreme of maliciousness (e.g., the current large
outbreak of malware, the benign binaries shipped with an OS),but bridging the gap between whitelist and blacklist detection
for web malware remains a significant challenge.
This paper presents CAMP, a content-agnostic malware protection system based on binary reputation that is designed toaddress these shortcomings. CAMP is built into the browser anddetermines the reputation of most downloads locally, relying onserver-side reputation data only when a local decision cannot be
made. This paper gives a detailed overview of CAMP and its
architecture and provides an evaluation of the system througha six-month deployment in which 200 million users of Google
Chrome requested between eight to ten million reputation requests a day. Our evaluation shows that CAMP exhibits accuracy
close to 99% relative to proprietary VM-based dynamic analysis,
is able to process requests in less than 130 ms on average, andwas able to detect approximately five million intentional malware
downloads per month that were not detected by existing solutions.

Google’s Safe Browsing API shows for dangerous web sites.
Oberheide et al. [24] proposed CloudAV as a new model
CAMP achieves an accuracy close to 99% relative to propri- to increase the effectiveness of Anti-Virus products. Under
etary, VM-based dynamic analysis.
this model, Anti-Virus protection is moved into the cloud andIn this paper, we provide a detailed overview of the design multiple AV engines, working in parallel, are employed toand architecture that comprises our reputation-based detection improve detection rates. While combining the decision of mulsystem and evaluate its effectiveness based on data collected tiple AV engines leads to better detection, this approach is stillfrom February to July 2012. We discuss how the system subject to the limitations mentioned above as CloudAV also
minimizes the impact on the privacy of browser users and relies on timely signature updates. Furthermore, Oberheide etevaluate its detection performance focusing especially on false al. show that major Anti-Virus engines detect only 35% to 70%
of recent malware which means that many malware binaries
positives.
remain undetected. CloudAV also requires that all binaries
This paper makes the following contributions:
are uploaded to the cloud which exposes these downloads• We present CAMP, a content-agnostic malware protection system, which utilizes reputation-based detection to to a third-party and may constitute an unacceptable loss inprivacy for some users. While CAMP also moves detection
protect users from web-based malware.
of malware into the cloud, it reduces the privacy impact by• We perform an extensive, six month evaluation of CAMP
consisting of over 200 million unique users and millions employing whitelists so that most download URLs stay withinof daily reputation evaluations. We show that our content- the browser and do not need to be sent to a third party. Binary
agnostic detection approach is both accurate, with an payloads never leave the browser. Additionally, its contentaccuracy close to 99%, and well performing, processing agnostic approach does not suffer from the same limitations
as AV engines, e.g. delays in signature updates.
requests in less than 130 ms on average.
b) Blacklist-based
protection:
Blacklist-based• We compare CAMP with the current state of practice
approaches
provideprotection
frommalwareby identifying
and demonstrate that CAMP outperforms Anti-Virus, as
the
sites
from
which
it
is
being
served.
Servicessuchwell as various web services, e.g. McAfee’s Site Advias
Google’s
Safe
Browsing
API[16],
McAfee’sSitesor, Symantec’s SafeWeb, etc. Further, CAMP identifies
Advisor[22]
or
Symantec’s
Safe
Web
[23]
detectmalicious
large numbers of malware undetected by these services,
including 5 million malware downloads per month not or compromised web sites that may infect users with malware.
Browsers integrate with these services directly or via tool
identified by Google’s Safe Browsing API.
bars and prevent users from visiting known malicious sites.
The remainder of the paper is organized as follows. We
While effective in identifying compromised web-sites which
discuss related work in Section II. In Section III, we present
tend to be long lived, blacklists face challenges when trying
a detailed description of the system architecture and allto protect against adversaries that employ highly agile
components responsible for creating the reputation system.
malware distribution servers [27]. By frequently changing theSection IV discusses how to leverage the reputation systemdomains that serve malware, blacklists become less effective
for malware detection. We evaluate the performance of theas domains rotate faster than the time it takes to detect them.
system in Section V, and present a small case study of a highly
CAMP offers a different approach. Instead of exporting
dynamic malware campaign. We discuss CAMP’s uniquea blacklist to clients, CAMP protects users by augmenting
properties in Section VI. Finally, we conclude in Section VII.
blacklists and whitelists with a content-agnostic reputation
system that protects users from malicious binaries without
II. R ELATED W ORK
requiring a-priori knowledge of the binary or its serving
Protecting users from malware continues to be a challenging domains. Specifically, the browser performs the following to
problem. In the following, we provide an overview of differ- check the safety of a downloaded file: (1) the browser tries
ent approaches to prevent malware from infecting users and to locally determine whether the download is malicious by
explain how CAMP differs from prior work.
checking the download URL against a list of URLs knowna) Content-based detection: Anti-Virus software is one to serve malware exported to the browser using Google’sof the most common content-based malware detection tech- SafeBrowsing API, (2) the browser checks locally againstniques. Usually, Anti-Virus products run on end-user systems a dynamically updated list of trusted domains and trusted
and employ signature-based detection to identify variants of binary signers to determine whether the downloaded content is
known malware.
likely benign and, (3) for downloads that do not match any ofWhile Anti-Virus engines do help in protecting users from the local lists, the browser extracts content-agnostic features
malware infections, several challenges limit their effectiveness. from the download and sends a request to CAMP’s reputation
Malware authors have developed increasingly sophisticated service. As a result, CAMP protects users against malicious
evasion techniques, such as packing and polymorphism, aimed downloads that would likely have been missed by a blacklistat circumventing detection by AV engines [4], [30]. Addi- based approach. We compare CAMP’s performance to populartionally, the signature generation and update cycle causes an blacklist services in Section V.
inherent delay in protecting users against new variants of
CAMP and Google’s SafeBrowsing API complement one
malware.
another. The API exports blacklists of compromised sites that 
include content from malicious sites that automatically exploit  ! a browser with no interaction from the user. This works wellfor protecting browsers from infected landing pages as they
are relatively static, and compromise can be observed by an       
automated system to build the blacklist [25]. CAMP, on the  other hand, targets binaries that were downloaded by misledusers from highly dynamic infrastructures.
   c) Whitelist-based schemes: In contrast to blacklists,
whitelists ensure that only known benign software can beinstalled and installation of everything else is disallowed. For
example, Bit9 [6] and CoreTrace [11] maintain a list of bina ries verified to be benign. While whitelisting can be effectivein enterprise settings, it remains challenging to maintain an
up-to-date whitelist that covers the plethora of applications developed globally. Therefore, protecting downloads in thebrowser through whitelists alone is not currently practical.
Fig. 1. The diagram presents a high-level overview of the detection system
Nonetheless, to protect user privacy, CAMP derives a whitelist showing the communication between the client and server as well as thefrom its reputation data that is used by the web browser server-side processing infrastructure.
to locally decide if a binary download should be trusted.
Downloads not on the whitelist require a reputation-based
decision.
malware is installed in the background without knowledged) Reputation-based detection: A lot of research hasby the user. We believe that focusing on user downloads
been conducted on reputation systems [19] and how to use
is justified for several reasons: the security of browsers has
them for detecting malicious activities. Hao et al. [18] proincreased significantly over the last few years [17], [29], [32]posed SNARE, a reputation system for detecting spam email.
and proposed exploit detection systems such as Blade [21]Qian et al. [26] proposed using network-based clustering toor Zozzle [13] offer efficient mechanisms to prevent current
increase the accuracy of spam-oriented blacklists.
exploits from succeeding. As a result, automated exploitation
Notos [2] and EXPOSURE [5] offer a dynamic reputationof browsers has become more difficult and adversaries are also
engine for DNS. Both systems use features based on passive
incorporating social engineering techniques in which users
DNS resolution to predict the likelihood that a domain name isdownload malware themselves.
malicious. CAMP is complimentary to Notos and EXPOSURE
To efficiently determine if an unknown binary download
but is specifically targeted to predict whether an unknown
is malicious, CAMP is split into a client component and abinary download is malicious. Furthermore, the use of DNS
server component. The client component is integrated into alimits a reputation system to making decisions on the granuweb browser. As a first step, the client checks the download
larity of domain names whereas CAMP operates on all of theagainst both a local blacklist and whitelist. If no match isfeatures that are associated with a downloaded binary.
found, the client extracts features from the binary download,
Finally, closely related to our work, is Microsoft’ssends these features to the server, and displays a warning to
SmartScreen described briefly in a blog post [10]. SmartScreen
users if the server response instructs it to. The server processes
utilizes a reputation-based approach to protect users fromthe features sent from the browser and computes a reputationsocially-engineered malware. It does so by computing reputadecision informed by the client request and a reputation metriction for the download URL, the file identifier and the publisher
constructed from previous client requests. Figure 1 shows an
if available as a digital signature. Unlike CAMP, it requires
overview of the complete detection system. We will discussthat all download URLs are sent to a remote server. In contrast
it in detail in the following sections.
to SmartScreen, CAMP computes its reputation based on manyother features available to the browser, such as referrer URLs,
A. Binary Analysis
IP addresses, etc. In addition, this paper provides in-depth
Many machine learning algorithms require labeled groundtechnical details of CAMP’s reputation system and evaluates
truth
for training purposes. Our situation is similar in that we
its performance in an operational setting.
need to label our reputation data according to the nature ofIII. S YSTEM A RCHITECTURE
the binary, e.g. whether it is malicious or benign. We support
In the following, we provide an overview of CAMP’s design multiple dimensions for the labels so other classifications suchand architecture. Our goal is to create a system that scales as spyware are possible, too. Similarly, the type of binary is its
to hundreds of millions of users and protects them from own dimension, e.g. Windows PEbins are treated differently
downloading malware while at the same time limiting the from Mac OS X DMG files. However, for any binary type,impact on their privacy. Our focus is specifically on binaries a corresponding classification system needs to be available todownloaded by users rather than drive-by downloads in which provide accurate labels.

In this paper, we make use of a binary analysis system provide as many features as are available to it. The followingthat we developed independently to classify binaries based on is a list of features usually available to web browsers:
static features as well as dynamic execution traces. The main
• The final download URL and IP address corresponding
goal of this system is to limit the number of false positives
to the server hosting the download.
and we consciously sacrifice detection in favor of fewer false• Any referrer URL and corresponding IP address encounpositives.
tered when initiating the download, e.g. the results of
The labels produced by the binary analysis system form themultiple HTTP redirects.
ground truth that governs the training of the reputation system.
• The size of the download as well as content hashes, e.g.
The labels also allow us to compute detection performance ofSHA-256.
the reputation system post facto. We measure this as part of• The signature attached to the download which includes
our evaluation in Section V.
the signer as well any certificate chain leading to it. The
It is important to note that our reputation system does notclient also informs the server if it could successfully
require a particular binary classification solution and otherverify the signature and if it trusted the signature, e.g.
detection approaches [9], [33] or labeling solely based on theit was rooted in a trusted certificate authority.
output of AV engines should also be possible. Of course, theThe client then sends these features to the server and awaits
overall accuracy of the system would be dependent on theits response. The server may reply with several different
accuracy of the underlying labeling mechanism.
verdicts based on the reputation data available to it. It can
Binary analysis systems are not perfect and are susceptible
inform the web browser that the download is predicted to be
to evasion [15], [31]. Since CAMP is a reputation systembenign in which case the web browser completes the downloadthat requires a reasonable estimate of ground truth, any errors
without displaying a warning, or it can tell the web browser
in labeling might propagate to CAMP’s decisions. However,that the download is either deemed malicious (Figure 2) orsince CAMP is independent of the classification mechanism,
unknown (Figure 3). In both of the latter cases, the web
any advances to dynamic analysis or Anti-Virus products willbrowser warns the user about the download and offers to delete
seamlessly improve CAMP’s performance. Nonetheless, we
the downloaded file. The unknown verdict indicates that theshow in Section V-B that our binary analysis system exhibits
server did not have sufficient reputation information to label
reasonable accuracy.
the download as either benign or malicious. Our evaluation
B. Clientshows that in the majority of all cases unknown by itself
isgood a predictor for malicious downloads. The different
When a user initiates a binary download in her web browser,
reputation
verdicts are explained in more detail in Section IV.
the web browser applies several checks before asking theUserprivacy
is an important goal for CAMP. Verifying theserver for a reputation decision. If any of these checks fail,content
typeof
the file and that it neither matches blacklists
the client makes a local decision on whether the download isnorwhitelists
drasticallylimits the number of downloads formalicious or not.
whicharemoteserveriscontacted.
As shown in Section III-D,
1) The web browser determines if the binary is alreadythewebbrowser
contacts
theCAMP
service for only about
known to be malicious based on its URL, for example,
30%
ofbinarydownloads.
Furthermore,
the web browser sends
via a list of known malware sites. In our case, we use
only
featurescomputedfromthebinary,not the binary itself,
Google’s Safe Browsing API to make that determination.
totheserver.
If the binary is known to be malicious, the browser candisplay a warning directly without requiring a reputation
C. Server
decision.
2) The browser determines if the binary download could
The server pipeline has two different roles when processing
potentially be harmful to the computer, e.g. it might requests. First, the server receives the client request andcorrespond to an executable which may carry malicious renders a reputation verdict based on its reputation systemcode, or a DMG which is how Mac OS X software is which encompasses aggregate information from all downloads
installed.
observed by the server during its measurement intervals,
3) The binary download is checked against a dynamically including e.g. 1 day, 7 day and 90 day intervals. Second, theupdated whitelist of trusted domains and trusted binary server uses the information provided by the client to update
signers. The list of trusted domains or trusted signing its reputation data.
certificates consists of reputable software publishers
The reputation verdict is computed by a reputation metricknown for not distributing malware. If the binary down- calculated by a binary circuit that has access to all features
load matches the whitelist, the browser assumes that from the client request and any reputation data that is refthe download is benign and no server-side reputation erenced by those features, e.g. how many known benign ordecision is required.
malicious binaries are hosted on a given IP address, etc.
If all the above checks do not result in a local decision, theTo incorporate information provided by the clients intobrowser extracts features from the downloaded binary. Since the reputation system, client requests are first despammed to
the reputation decision is made on the server, the client can prevent misbehaving clients from unduly influencing the data.

Fig. 2. The browser warns the user that the download is malicious. The
intentionally discrete arrow presents an option to keep the file.

Fig. 3. The browser warns the user that the download is not commonly
downloaded.

Despammed requests are then processed within a few minutes
to generate up-to-date features.
To create a classification system that has both high performance and high reliability, we employ BigTable [8] and
MapReduce [14] for distributed data storage and parallel
processing. In the following, we provide a detailed overview
of each component involved in making reputation decisions.
1) Reputation System: The heart of the decision process isthe reputation system. To better understand its properties, weplace it within the analysis framework proposed by Hoffman
et al. [19]. According to Hoffman a reputation system can becharacterized across the following three dimensions:diminish its trust.
As CAMP is deployed to a large number of users, manyof the design decisions in building the system are in favorof reducing false positives. However, the performance of thereputation system can be adjusted gradually to favor recall
over precision.
We provide a detailed discussion of CAMP’s reputationbased detection in the next section but give an overview
here of the reputation system itself. The reputation system
is responsible for receiving a browser reputation request andreplying to it with a verdict.
For each client request, the reputation system can make adecision based on a-priori information if either the URL or thecontent hash is known to be malicious. Similarly, to respond to
major false positives, the reputation system consults a serverside whitelist to override any reputation decision.
The reputation metric is calculated by a binary circuit thatreferences reputation data in form of previously computed
aggregates. The features from the client request and thereputation formulation determine which aggregates are looked
up from the data store. The reputation system then computes
a verdict which can be either: benign, malicious or unknown;
see Section IV for a discussion of the different meanings. The
data store lookups happen in parallel and are non-blocking to
reduce overall latency. The time spent computing the decisionfrom the aggregates is insignificant compared to the time ittakes to look up data from the data store.
2) Frontend and Data Storage: The frontend is responsible
for receiving requests from web browsers and answering themwithout incurring any significant latency. To achieve low
latency, we split the reputation metric computation and theintegration of new data into the reputation system into separate
components. Upon receiving a request, the frontend issues aRemote Procedure Call (RPC) to the reputation system, which
determines whether the binary download is malicious. After
receiving an answer, the frontend writes the request and theverdict to a data store that other components of the pipelinecan process, and then returns the verdict to the client.
As CAMP needs to handle a large number of web browser
requests, the temporary storage of request data requires acarefully chosen storage layout. We use BigTable [8], a nonrelational distributed database that provides key-value stores
and allows the association of a timestamp with a given key.
While Bigtable scales well, it is limited to approximately 2GBof data per key. For subsequent data processing it is helpful
to index requests by the URL of the binary. However, as
we store each request for two weeks and some URLs arerequested frequently, on the order of hundreds of thousands
times a day, we chose not to index solely by URL. Instead, we
append the Reverse-Ordered hexadecimal string representation

Formulation, which represents the mathematical underpinnings of the reputation metric as well as its information
sources.
• Calculation, which is the concrete implementation of theformulation.
• Dissemination, which characterizes how the results fromthe reputation metric are propagated to participants.
•

In our case, both the calculation and dissemination are
centralized and deterministic. The storage of reputation datais transient as each item expires after 90 of days.
In the following, we explain the formulation of the reputation system in more details.
Our reputation data is based solely on direct, automatic
sources. The output of the binary analysis pipeline is a trusted
automatic source. Data collected and sent by web browsers
is also a direct, automatic source but may be untrusted.
The reputation data consists of features across a variety ofdimensions that each describe some aspect of the binary or
its hosting infrastructure. As mentioned in Section III-B, foreach binary download, we receive not only a content-hash but
also related information such as corresponding URLs and IP
addresses of the servers hosting them, etc. The server may
derive further features from the client request.
The reputation data maps each feature to an aggregate that
contains measurements over data observed during a given
time frame. Aggregates are continuous features and consistof two counts: the number of interesting observations and thetotal number of observations. For example, assume CAMP
observed 10 downloads, 6 of which were malicious, on IP
address IPA . The aggregate corresponding to the feature
IP:IPA would then be {6, 10}. Each aggregate also contains
the first and last time the particular feature was seen.
The aggregates include both positive as well as negative
events, i.e. they can be both trust building and trust diminishing. For example, the number of users downloading a binary
from a site may represent an increase in trust. On the other
hand, the number of malicious binaries hosted on a site may

of the timestamp of the request to the URL. This causes thea malicious reputation decision as well as the number
data to be placed in different rows while maintaining identicalof requests for binaries known a priori to be malicious,
ordering compared to indexing by URL. This design decision
either based on their URL or corresponding content
was crucial in scaling CAMP to handle popular URLs.
hash. For the aggregates from the binary analysis system,3) Spam Filtering: The spam filter processes the data writthis contains the number of URLs hosting malicious
ten by the frontends in real time and discards requests that dodownloads as well as the number of malicious content
not originate from regular users. We do not require the spamhashes.
filter to be highly accurate and false positives are acceptable.
For example, the aggregate for
The spam filter may make use of any information provided
client|site:foo.com|reputationin the client request and has visibility into all client requests represents the total number of client requests for the sitemade within the last 24 hours. As a result, the spam filter can foo.com as well as the number of client requests for the same
apply velocity controls on the user IP address of the request, site that received a malicious reputation decision. Another
the Autonomous System Number (ASN) corresponding to the example is
IP address, etc. The spam filter also ensures that requests areanalysis|site:foo.com|urls
properly formed and contain all required features, e.g. properly which contains the total number of URLs found under foo.com
formatted URLs, etc.
as well as the number of such URLs that were labeled
Requests not discarded by the spam filter are forwarded malicious by the binary analysis system.
to an aggregator that computes aggregate features used by
To construct these aggregates, the despamming component
the reputation system. The output of the spam filter is also writes client requests to a temporary data store which is
employed to fetch binaries from the web that have not been indexed by the second aggregator index dimension. Then aanalyzed by the binary classifier. Since binary downloads may series of MapReduces [14] periodically processes all entries
carry sensitive information, we apply further filters so that in the data store. This process merges new data with older
only binaries that exhibit sufficient diversity of context are aggregates to generate aggregates over different time intervals,considered for analysis. The binary analysis does not gate currently 1 day, 7 days, 14 days, 28 days and 98 days. All
any reputation decision and may complete a long time after a aggregates computed this way are available to the reputation
reputation decision was sent back to the web browser.
system.
As stated previously, our goal is not only to make highly
accurate decisions but also to reduce the impact on the privacy D. Client-side Whitelistof web users as much as possible. The requests received fromCAMP reduces the privacy impact on its users in several
web browsers reveal not only the binary URL visited by a user different ways. Since the detection is content-agnostic, web
but by their very nature also a corresponding source IP address. browsers do not need to send binary payloads to CAMP’s
The data processing architecture of the reputation system is servers. As users download binaries that potentially contain
designed so that the source IP address is visible only to the sensitive information, any solution that requires transmission
spam filter and completely deleted after two weeks. The binary of payload data is likely to face privacy challenges.
URL is visible to rest of the pipeline, but is stored in such
Another way in which CAMP reduces the privacy impact
a way that it also is automatically deleted after two weeks. is by client-side whitelists that determine which binaries are
The only information that is stored for up to 90 days are the trusted in advance. If a binary is trusted, CAMP does not needaggregates that make up the reputation data.
to send the binary URL to a remote server. The whitelists4) Aggregator: The basis of the reputation system is contain trusted domains such as microsoft.com as well as
formed by the reputation data which consists of statistical trusted software publishers. A software publisher is identified
aggregates indexed by keys derived from request features. by the public key of the signing certificate and the CA certiAggregates are computed and written by the aggregator, which fying it. The goal of the whitelists is to resolve the majority
processes the output of the despammer and organizes aggre- of all downloads locally without requiring a reputation-based
gates according to a three-dimensional index.
decision. At the time of this writing, approximately 70% of all• The first index dimension is defined by whether the
downloads are considered benign due to policy or matchingaggregate is computed from client requests or based on client-side whitelists.
aggregates from the binary analysis system. Aggregates
For a domain or signer to be added to the whitelist, it needs
computed from client requests are considered untrusted to fulfill the following criterion. CAMP needs to have seen thewhereas aggregates from the binary analysis system are binaries from the domain or signer for at least 90 days without
inherently trusted.
encountering any signs of maliciousness. We add new domains• The second index dimension consists of features fromor signers to the whitelist in the order in which they contributeclient requests and additional features derived from the to the number of requests received by CAMP over that time
request on the server side.
period.
• The third index dimension contains broad categories overSince adding a domain or signer to the whitelist implies
which aggregates are computed. For client side aggre- that the CAMP server no longer receives any downloads for it,
gates, this contains the number of requests that received we employ a web crawl to regularly analyze binaries hosted

0.9

determine with a high degree of confidence whether a binary
is benign or not.
Binaries hosted on the web fall within a wide spectrum ranging from known-benign to known-malicious. Known-benign
binaries can be added to a whitelist and known-malicious
binaries to a blacklist. In an ideal situation, any binary iseither known to be good or known to be bad. The reputation
system described here is concerned with the gray area between
whitelists and blacklists.
For CAMP, we attempt to create a whitelist that covers the
majority of benign binaries and subject anything not coveredby it to a reputation decision. In the following, we explain
how to classify a binary using reputation data.

0.85% of whitelisted download requests.

0.8

0.75

0.7

0.65

0.6

0.55

0.5
Signers
Domains

0.45
0

1000

2000

3000

4000

5000

6000

7000

8000

9000

10000

Whitelist size.

Fig. 4. The graph shows the percentage of downloads for which a web
browser would make a local reputation decision depending on the size of thedomain and signer whitelist.

on the trusted domains or signed by trusted signers. If thebinary analysis reveals evidence of malicious behavior, thecorresponding entries are removed from the whitelist and areagain subject to reputation-based detection.
Figure 4 shows the number of downloads that a web
browser would handle locally when adding more trusted sites
to the domain or the signer whitelist. We estimated thesevalues by considering all reputation requests that we received,
then iteratively adding the most popular trusted signers anddomains to our whitelist and tracking which requests would
have been handled locally by the browser. The graph does notcontain measurements for whitelists smaller than our initialwhitelists as CAMP does not have any knowledge about
requests corresponding to them. Our initial domain whitelist
contained 243 entries and matched approximately 47% of all
download requests remaining after policy checks. The graphshows that by increasing the domain whitelist to 1000 entries
the local percentage of downloads not requiring a request to
CAMP would increase to 73%. The signer whitelist is more
effective as increasing it to 1000 entries would cover about
85% of downloads locally.
IV. R EPUTATION S YSTEM

A. Feature Extraction
For each feature in the client request, the reputation systemderives features that form the indices for aggregate lookups.
The aggregates contain statistics about the total number oftimes a feature occurred and how many times it occurred ina malicious context. Each feature type has a different set ofderived features.
For IP addresses, the features are the IP address itself, thecorresponding /24 netblock, as well as the corresponding /16
netblock. The rationale for using netblocks is that serving IP
address frequently change, e.g. due to load-balancing or due toadversaries switching IP addresses to avoid IP-based blocking.
For netblocks that are used predominantly for serving malicious content, the likelihood that a yet unknown IP address inthat netblock is also serving malicious content is often higher.
CAMP also uses URL-based features. The reputation request from the client contains the URL pointing directly tothe binary download as well as any URL encountered by the
web browser when following redirects to the binary. For each
URL, the reputation system extracts the host, the domain, andthe site. The domain and site are usually identical, but differ
for dynamic DNS providers where the site is the same as thehost name. The goal of using the site and domain as a feature
is to track potential ownership of resources hosted on them.
For the signature-based features, we extract the signer
key and the corresponding CA certifying the key for eachcertificate chain encountered in the binary signature. We also
keep track if the signature was trusted on the client, e.g.
the client had a trusted CA that was the root of one of thecertificate chains.
Some of the client features such as the content hash areused directly in subsequent aggregate lookups.

The goal of our reputation system is to determine a-priori if
a binary is likely going to be malicious or not without having
access to the content of the binary itself. Instead, metadata
about the binary such, as its hosting infrastructure and how
B. Aggregate Computation
the user reached it, form the basis for detection.
One of the challenges with detecting malware is the ease
The aggregation step discussed in Section III-C4 computes
with which malware authors can change their binaries, e.g. aggregates for all possible dimensions based on client featuresby repacking, and how quickly they can change the locations and features derived on the server. However, not all of thesefrom which binaries are being served. On the other hand, aggregates are consulted when making a reputation decision.
there is no need to frequently change the hosting location of Which aggregates are consulted for rendering a reputation
benign binaries or the binaries themselves. Differences such as verdict depends on the configuration of the reputation systemthese may be leveraged to create a reputation metric that can described below.

URL

Requesthttp://a.foo.com/

IP

10.0.0.1

Aggregate Index
analysis—host:a.foo.com—urls
analysis—domain:foo.com—urls
analysis—site:foo.com—digests
client—site:foo.com—requests
analysis—ip:10.0.0.1—urls
analysis—ip:10.0.0.1—digests
client—ip:10.0.0.1—requests
analysis—ip24:10.0.0.1/24—urls
analysis—ip16:10.0.0.1/16—digests
client—ip24:10.0.0.1/24—requests

1 day
0/1
0/2
3/22
10/20
0/1
0/1
1183/1208
2/112
0/3
1313/2571

7 day
1/10
1/20
4/25
20/50
0/1
0/1
9801/11327
5/237
0/5
9912/32227

28 day3/100
7/200
11/153
123/22000/1
0/133555/37455
6/238
0/5
34127/43119

TABLE IT HIS TABLE SHOWS HOW REQUEST FEATURES RELATE TO DIFFERENT INDICES AND CORRESPONDING REPUTATION AGGREGATES . F OR BREVITY , NOT
ALL CLIENT FEATURES NOR ALL DERIVED FEATURES ARE SHOWN HERE .

Table I provides an example of how client features suchas an IP address or URL are turned into indices for looking
up aggregates. The counts associated with each index measure
how often the index was encountered either in client requests
or by the binary analysis pipeline. The first value counts thenumber of malicious verdicts and the second value counts thetotal number of occurrences. For example, the index
analysis|domain:"foo.com"|urls
measures how many different URLs under the domain foo.com
were analyzed by the binary analysis system and how often
they were labeled malicious, i.e. for the 7-day time period,
the analyzer checked 20 URLs from foo.com and found oneof them malicious.
An example for a client aggregate is the index
client|ip24:10.0.0.1/24|requests
which measures how many client requests for the netblock10.0.0.1/24 were received by CAMP during the specified time
period. For the last 24 hours, CAMP received 2571 requests
total and used reputation to label 1313 of them as malicious.
As the binary analysis system provides us with ground truth
for client requests for which the payload is known to us, we
make decisions based on the direct and derived features aswell as their corresponding aggregates. We explain how thedecision is calculated in the next section.
C. Reputation Decision
The aggregates computed for each reputation request provide a large number of continuously valued features suitable
for many types of machine learning. One drawback of employing machine learning is that the trained models are often
large and difficult to understand by humans. As any detection
mechanism is going to produce false positives, we favor aclassifier that allows human inspection and reasoning about
false positives. To achieve this, we employ a depth-2 boolean
circuit with a small number of boolean inputs. Figure 5 shows
the boolean circuit and its inputs as used in CAMP. Its
structure is simple as it contains only AND gates at depth 1 andone OR gate at depth 2. Each individual AND gate represents
a single detection rule. In the following, we use AND gate andrule synonymously.
Aggregates are mapped to boolean inputs via simple threshold functions. The reputation metric is calculated from a

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Fig. 5. This figure shows a depth-2 boolean circuit for classifying a binary
based on boolean inputs computed from the aggregates of a reputation request(‘‘Ping’’).

boolean circuit and the thresholds for each aggregate. These
thresholds are determined via a training phase discussed below.
Logically, the boolean circuit can be separated into threedifferent parts: the site aggregates, the IP aggregates and theunknown aggregates.
For the IP and site aggregates, we use two different functions to map an aggregate to a boolean value. The threshold
function is defined as f (p, n, t) := np ≥ t and the countfunction is defined as f (p, n, t) := n ≥ t where p is thenumber of positive samples in the aggregate, n is the total
number of samples and t is the threshold provided by themodel.
The booleans derived from the unknown aggregates are
more complex. The goal of the unknown aggregates is to flag
binaries for which we do not have sufficient reputation tolabel them benign. The input is formed by the following threenegated booleans:
1) The boolean is true if the binary analysis pipeline has
already analyzed the binary based on the content hash.
2) The boolean is true if the binary was signed and theclient could trace the certificate chain to a trusted CAroot.
3) The boolean is true if the binary is popular because alarge number of users downloaded either the digest itself
or other binaries from the same hosting site.
As a result, a binary is labeled unknown by the binary circuit

if CAMP has never seen it before and the binary is not signed
by a trusted signer and not from a popular download site.
The parameters are thresholds on the number of downloads
required before the binary or the site is considered popular.
The reputation system is configured by choosing thresholds
according to the precision and recall for each AND gate.
Precision and recall are determined from a labeled training
set. The training set is created by matching the content hashincluded in reputation requests from web browsers with thecontent hash from the binary analysis system. The binary
analysis system provides the label, e.g. benign or malicious,and the reputation request provides the features. The training
set consists of 4, 000 benign requests and 1, 000 malicious
requests.
Figure 6 shows the precision and recall for site aggregatesthat result from changing the thresholds for the aggregate
mapping functions. We see that achieving a precision larger
than 0.95 results in recall of less than 0.2.
Figure 7 shows the precision and recall for IP aggregates.
We see that achieving a precision larger than 0.95 results inrecall of less than 0.15.
We omit the precision and recall for the unknown rule as
they are very similar to the previous two graphs. As can beseen from these graphs, a high precision leads to low recall
when considering each AND gate individually. Our evaluationin Section V shows that the combination of all AND gates, thatis the complete boolean circuit, leads to acceptable detection
results with very low false positive rates.

(Section III-C4). The despammer runs continuously, requiring
1.25 cores and 25 GB RAM. The valid requests that thedespammer outputs are processed by an indexer in the aggregation pipeline, which writes data to a temporary BigTable [8]for aggregation by a series of MapReduces [14]. The indexer
uses 2 cores and 8 GB RAM. The aggregation MapReduces
run periodically throughout the day. Each run finishes inapproximately one hour and uses 90 cores and 120 GB
RAM. The MapReduces produce a model that is comprised ofapproximately 4.75 billion aggregates requiring approximately
1.4 TB of BigTable storage. This model is replicated tolocations near each of CAMP’s frontends.
The above metrics do not include resources for running ourbinary analysis pipeline, or the CPU and memory resources
required by BigTable.
B. Accuracy of Binary Analysis

Before we evaluate CAMP’s accuracy relative to our baseline proprietary VM-based dynamic analysis framework, it isimportant to understand the accuracy of that framework. To
do so, we compared the framework against the AV scanners
provided by VirusTotal [1].
We selected a sample of 2200 binaries that our dynamic
analysis framework processed on a single day, but were notknown to VirusTotal. Of these, 1100 were labeled clean by ourdynamic analysis framework, and 1100 were labeled malicious.
We submitted each of the binaries to VirusTotal, and waited
10 days, to allow AV engines to catch up. We then consulted
VirusTotal for each of the 2200 binaries to see how many AV
V. E VALUATION
engines agreed with our initial analysis.
For CAMP’s initial deployment, we used Google Chrome
After 10 days, 99% of the binaries that were flagged as
and targeted Windows executables, e.g. PEbins, MSI-files, etc..
malicious by our framework were flagged by 20% or more
The following evaluation measures the performance of CAMP
of AV engines on VirusTotal. Only 12% of the binaries thatin that context. We evaluate resource requirements, accuracy
we flagged as clean were also flagged by 20% or more of theof the baseline dynamic analysis framework, and the precision
AV engines. Of these false negatives, a large percentage areof the reputation system. We also provide a comparison toclassified as AdWare, which our system intentionally does notother common malware prevention solutions and a case study
detect.
of a campaign that CAMP identified.
These results indicate that, by design, our dynamic analysis
framework
has a very high true positive rate, but does suffer
A. Server-Side Resource Requirementsfrom some false negatives. As we improve this framework,
Here we briefly overview CAMP’s operational performance
CAMP’s detection rates should improve as well.
and resource requirements. To service 200 million Google
Chrome users, CAMP frontends and reputation RPC servers
C. Accuracy of CAMP
(Section III-C2) use approximately 4 cores and 9 GB RAM
The evaluation is based on data collected between February
globally to serve on average 190 QPS. Most of these resources
are reserved for geographic distribution and load balancing, 2012 to July 2012 from our production deployment of CAMP.
and we have load-tested CAMP frontends at 400 QPS using During that time frame, CAMP was used by approximately
1 core and 3 GB RAM. The median latency is 127 ms and 200 million users. Each day, it received between 8 million to
the 90th percentile is 313 ms. As many binary downloads 10 million reputation requests and labeled approximately 200
take much longer time, on the order of seconds, requesting to 300 thousand download requests as malicious; During oura CAMP verdict does not significantly add to the download evaluation, the reputation database kept track of approximately
3.2 billion aggregates.
latency observed by users.
Download requests can be processed with minimal overhead
To get a better understanding of how well the reputation
since CAMP leverages preprocessing to build models that metric works in practice, we measured the true positive (tpr),
are amenable to fast lookup. This is a multi-stage process, false positive (fpr), true negative (tnr) and false negative rates
encompassing despamming (Section III-C3) and aggregation (fnr) of the reputation requests received by CAMP. The rates

11

bad binaries thr.
bad requests thr.
bad URLs thr.

0.6

0.6
Recall

0.8

Recall0.8

bad binaries thr.
bad requests thr.
bad URLs thr.

0.4

0.4

0.2

0.2

0

0
0

0.2

0.4

0.6

0.8

1

0

0.2

0.4

0.6

Precision

0.8

1Precision

Fig. 6.
The figure shows precision and recall for site aggregates fordifferent thresholds of the aggregate mapping functions.
1Fig. 7. The figure shows precision and recall for IP aggregates for different
thresholds of the aggregate mapping functions.
0.05

True Positive
True Negative
False Positive
False Negative

0.6

0.03
Rate

0.04

Rate

0.8

0.4

0.02

0.2

0.01

0

0

1

/0

07

1

/0

06

1

/0

05

1

/0

04

1

/0

Fig. 8. The figure shows the accuracy of the reputation-based classifier
as determined post-facto from the production deployment of CAMP.

03

1

/0

07

1

/0

06

1

/0

05

1

/0

04

1

/0

03

Date

Date

Fig. 9. The figure shows the false positive rate of the reputation-basedclassifier as determined post-facto from the production deployment of
CAMP.

fp
tp
tn
, fpr := fp+tn, tnr := tn+fp
are defined as follows; tpr := tp+fn
is optimized to minimize false positives.
fn
At the end of the measurement period, the overall accuracy
and fnr := tp+fn .
tp+tn
The labels to compute the rates are taken from the binary of CAMP was 98.6%. We compute this as tp+tn+fp+fn . The
analysis system for binaries that could be matched to rep- dominating factor in overall accuracy comes from our traffic
utation requests. See Section V-B for an evaluation of the distribution; the majority of requests are for benign binaries
accuracy of the labels. The results are shown in Figure 8. and our system exhibits low false positive rates. As with false
During the measurement period, the true negative rate was positive and true negative rates, accuracy increases further
greater or equal to 98% and the false positive rate was around when taking the client-side whitelists into account.
In June 2012, the true positive rate was around 70% and
1% for most of the measurement period but for three occasions
the false negative rate around 30%. CAMP’s ability to detect
where it increased to around 2%.
A more detailed graph of the false positive rate is shown 70% of recent malware without access to the content of thein Figure 9. We see that the false positive rate was noticeably binary validates the reputation-based detection approach. We
lower than 2% for most of the measurement period. When provide a more detailed comparison between CAMP and AV
taking into account that the web browser makes local reputa- engines in Section V-D.
In designing CAMP, our main consideration was avoidtion decisions for about 70% of downloads, the effective true
negative rate increases from 98% to 99.5% and the effective ing false positives and as such the binary analysis pipeline
false positive rate decreases from 2% to 0.6%. While false frequently prefers false negatives over false positives which
positives are inherent to our content-agnostic approach, our has a negative impact on all measurements presented here,
criteria for choosing CAMP’s reputation algorithm parameters e.g. a false negative in the binary analysis pipeline couldratio/ip/client
ratio/site/client
ratio/site/analysis
ratio/ip/analysis
binary/unknown
multiple rules

Number of Requests

250000200000150000100000

50000

0

1e+06

Number of client requests with blacklist label

300000

all malware requests
signed malware requests
signed/untrusted malware requests

100000

10000

1000

100

10

07

07

07

07

07

07

/2

/2

0

2

4

6

/2

/1

4

8

/1

2

/1

/1

1

1

/0

/0

1

1

/0

1

/0

/0

Fig. 10. The graph shows the stacked contribution to detection in cases
where a single AND gate or rule is responsible for labeling a download as
malicious or unknown. It also shows the number of cases in which multiple
rules flagged the binary download.

07

07

06

05

04

03

Date

Date

Fig. 11. The graph shows the number of client requests corresponding tobinaries labeled as malware by the binary classifier. It also shows requests
to malware were the binary was signed.

result in an incorrect false positive for the reputation-based day, 74% no longer appeared in our requests the next day. This
classifier. Specifically, when CAMP detects a malware binary percentage increased to 80% over 10 days. We hypothesize
correctly, but the binary analysis system has a false negative, that this is because such sites are malicious and rotate toour evaluation will count this as a false positive for CAMP.
avoid blacklists. This corroborates previous findings that social
As discussed in the previous section, each individual AND engineering domains rotate quickly to avoid blacklists [27].
gate achieves only low recall. Our hypothesis was that dif- 0.02% of the sites transitioned from unknown to bad over the
ferent AND gates would combine to increase the overall 10 day period, 8% transitioned to unknown to clean, and theperformance of the classifier. To measure this assumption, we remaining stayed in the unknown state. We manually analyzed
plot how often an AND gate is responsible for detection by the top sites that transitioned to clean, and found that almost
itself and how often multiple AND gates detect bad content si- all of them were indeed hosting dangerous downloads, andmultaneously. The results are shown in Figure 10 as a stacked the clean classification was a false negative of our dynamic
graph. The graph has two interesting features. The majority analysis framework. Based on these results, we believe thatof detections are due to a single rule, i.e. each individual the unknown verdict is actually a very strong indicator that arule contributes to the overall detection. The other interesting binary is actually malicious.
observation is that the detection of unknown binaries accounts
D. Comparison to other systems
for a significant fraction of malicious verdicts. We show in
To determine if CAMP provides benefits beyond thatthe case study below how adversaries frequently rotate thedomains from which they are serving malware and thus never provided by other malware detection systems, we conductedbuild up any reputation. The drop in the detection of unknown two different measurements in which we compared reputation
binaries around March 24th is due to a change in data formats decisions from CAMP to results from Anti-Virus engines asthat resulted in dropping the contributions of the unknown rule well as web-based malware services.
Over a period of two weeks we collected a random sample
from the graph.
of
10, 000 binaries that were labeled as benign by CAMP
The rule for labeling binaries as unknown requires thataswell
as approximately 8, 400 binaries that were labeleda binary is not signed by a trusted signer. To understandasmaliciousby CAMP. To avoid bias in binary selection,
how often malicious binaries with trusted signatures occur inforexampledue
to popular download sites which might be
our analysis, we extracted the signature state from binaries
over
represented
ina completely random sample, we sampled
we analyzed from client requests post-facto. As shown inbysite
ratherthanbyindividual binaries. We compared the
Figure 11, the majority of requests to malware are for unsigned
resultsfrom
CAMP
againstthe results from four different AV
binaries. That makes a trusted signature a good predictor for1
enginesthatscannedthebinarieson the same day as CAMP
the likelihood that a binary is benign.
madeits
reputation
decision.
We
conducted
the AV scans on
We also wanted to understand how often a binary that is
the
same
day
to
approximate
theperformance
users mightlabeled as unknown transitions to clean or to malicious after
see
when
AV
enginesscan
theirdownloads.
The
AV engines
it has been evaluated by our dynamic analysis pipeline. Toraninasandboxedenvironment,
and
were
not
permitted
any
do so, we analyzed requests from clients post-facto, for aperiod of 10 days in November 2012. Of all the sites that
1 Due to licensing restrictions, we cannot disclose the specific AV engines
served binaries that were classified as unknown on the first we used in our measurements.

100

safe
bad

% of binaries scanned

80

60

40

20

0

AV

AV

AV

-4

-3

-2

P

-1

AM

AV

C

Fig. 12. The graph shows the results from AV engines for binaries flagged
as malicious by CAMP.

results are shown in Figure 13 and 14.
The URL classification services mostly agreed with CAMP
when presented with the set of clean URLs. TrendMicroflagged about 3.5% as malicious, Symantec about 2.5% andSite Advisor about 1.5%. Furthermore, many of the benign
URLs were unknown to these three services. For example,
TrendMicro did not know over 55% of the URLs. Neither the
Malware Domain List nor Safe Browsing flagged any of the
URLs are malicious.
The URL classification services mostly disagreed with
CAMP when presented with the set of malicious URLs. TrendMicro identified about 11% as malicious, Safe Browsing about
8.5%, Symantec about 8% and Site Advisor about 2.5%. The
Malware Domain List did not flag any of them as malicious.
However, as with the benign URLs, many of the malicious
URLs were not known to the web services. For example,
TrendMicro did not know 65% of the URLs that CAMP foundto be malicious.
Google Chrome asks for a reputation decision only if a
URL is not known by the Safe Browsing API. Therefore, it is
not surprising that many of URLs CAMP considers malicious
were not in the Safe Browsing list. Moreover, the Safe Browsing list primarily targets drive-by downloads, not intentionaluser installs. Although, the Malware Domain list did not return
any detections, we included it in our measurements as it isfrequently used as a base of comparison by other work in thisspace.
The results for the other web services seem to confirm oursuspicion that blacklist based approaches are not as effectivein the current environment of frequently changing malware
distribution domains. The majority of URLs identified as
malicious by CAMP are not known to be bad by any webservice. On the other hand, CAMP explicitly assigns negative
reputation to domains unknown to it. We could interpret theunknown results from the web services in a similar way. In thatcase, detection rates would increase noticeably. For example,
when combining unknown and known malicious results, thedetection rate for TrendMicro would be 76%, for Symantec
46% and for Site Advisor 45%. However, in that case, thepotential false positive rates as measured by comparing to
the benign URLs would increase significantly, too. From thatperspective, TrendMicro would flag 59% of the benign URLs,
Symantec 24% and Site Advisor 29.5%. As the potential
false positive rates are much higher than can be sustained inpractice, our original interpretation of the inherent drawbacks
in blacklists is a more likely explanation.

network access. Thus any cloud-based reputation data that may
have been provided by the AV companies was unavailable
to the AV engines. However, we proactively updated AV
signatures every two hours to ensure freshness.
For the 10, 000 binaries that CAMP labeled as clean, themaximum number of binaries labeled as malicious by a single
AV engine was only 83. Only 16 binaries were flagged as malicious by two or more AV engines. This implies that CAMP
has a very high True Negative rate relative to commercial
Anti-Virus products.
On the other hand, the majority of binaries that CAMP labeled as malicious were classified as benign by the AV engines(see Figure 12). The AV engine that agreed the most with
CAMP only flagged 25% of the binaries as malicious. When
combining the results from all four AV engines, less than 40%of the binaries were detected. One possible explanation forthese results is that CAMP might exhibit a high false positive
rate, but as shown in Figure 9 and discussed earlier, CAMP’s
false positive rate is quite low and thus false positives are
not a likely explanation. However, as observed by Oberheide
et al. [24] many AV engines exhibit poor detection rates forrecent malware and we believe that to be confirmed by our
measurements, too.
In addition to comparing CAMP’s detection results with
AV engines, we also consulted several web services to classify
URLs that hosted binaries. For this measurement, we consulted
the following services: Malware Domain List, McAfee’s Site
Advisor [22], Symantec’s Safe Web [23], Google’s Safe E. Case Study
Browsing [16] and TrendMicro’s Site Safety Center. We
CAMP provides an interesting vantage point into malware
selected 20, 000 URLs from a single day’s worth of requests; distribution across the web. In the following, we explore an
10, 000 URLs pointed to binaries that were classified as benign example of one of many malware campaigns discovered byby CAMP and 10, 000 URLs that pointed to binaries that were CAMP. This campaign distributes Fake Anti-Virus binaries
identified as malicious. We employed the same site-based and leverages frequent repacking of binaries as well as fastsampling strategy that was used for evaluating AV engines. domain rotation to evade blacklist-based defense mechanisms.
For each of the selected URLs, we consulted the web services We observed the campaign between February 13, 2012 andlisted above and compared their verdict with CAMP. The March 1, 2012.

100

100

error
unknown
safe
bad

80

% of scanned URLs

% of scanned URLs

80

error
unknown
safe
bad

60

40

60

40

20

20

0

0

e
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ro

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The Fake AV binaries were primarily hosted on the freedomain provider uni.me. Free domain providers allow third
parties to register many domain names at low cost, and are
frequently abused by adversaries seeking to distribute malware.
This particular campaign registered domains matching thefollowing pattern[srv|www|server|update]NN.dict.uni.me
where dict corresponds to a random English word. Eachdomain was active for only a short period of time. To estimate
domain lifetime, we take the client|host 1 day aggregatesfrom February and compute the lifetime as the difference intime between the last observed download request and the first.
The median lifetime for a domain was 406 seconds, and the90th percentile was 1749 seconds. Some of the domains wereactive simultaneously. Table II provides examples of domains
employed in the campaign, along with the time of the first
request we observed for that domain and its estimated lifetime.
Over the two week period, we observed over 13, 000 uniquedomains on uni.me involved in this campaign.
We observe that the high frequency of domain changes
thwarts simple blacklisting-based approaches. The maintainersof the blacklist would need to be able to fetch the content,
analyze it, and push out list updates within minutes. We
noticed that even fetching the content would be challenging,as each domain stopping resolving properly after a short time
period.
The campaign not only switched domains, but also changes
the binaries that were served as indicated by their changing
content hash. We observed that the binaries served by eachhost changed approximately every 10 minutes. We fetched
and analyzed several samples, and they all offered the samefunctionality, a Fake Anti-Virus that hijacks the user’s system
and refuses to release it until a registration fee is paid. We
submitted one of the samples to VirusTotal and only one of
40 AV engines identified it as malware.
This particular malware hijacks the user’s machine bysetting the execution environment for all essential system

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Fig. 13. The graph shows how different web services classify URLs
flagged as benign by CAMP.

Fig. 14. The graph shows how different web services classify URLs
flagged as malicious by CAMP.

Time of first appearance
2012/02/18 15:02:00
2012/02/18 15:02:02
2012/02/18 15:02:05
2012/02/18 15:02:06
2012/02/18 15:02:15
2012/02/18 15:02:26
2012/02/18 15:02:38
2012/02/18 15:02:50
2012/02/18 15:02:53
2012/02/18 15:02:57

Life (sec)
632
330
629
587
246
25
560
693
575
1258

Host(.uni.me)
srv62.specialarmor
srv76.specialarmor
www12.fuelwire
server78.fuelwire
www82.fuelwire
update96.fuelwire
server45.specialarmor
server52.specialarmor
www77.fuelwire
www92.specialarmor

TABLE IIT HIS TABLE LISTS EXAMPLE DOMAINS FOR A F AKE AV CAMPAIGN AND
THEIR CORRESPONDING LIFETIME .

processes to run in a debugger that points to a dummy process.
This is achieved by setting the registry key
HKLM\software\microsoft\windows nt\
currentversion\image file execution
options\msa.exe\Debugger:svchost.exe
This prevents key processes from performing meaningful tasks
rendering the machine unusable. The Fake AV binary provides
a mechanism to ‘‘fix’’ the problem once the software isregistered for a fee.
As the domains for this campaign rotated quickly, ouranalysis pipeline fetched only a few samples. We observed
over 900 distinct content hashes resulting in binaries withidentical behavior. In total, we saw over 41, 000 different
binaries exhibiting similar behavior.
When the campaign first started and we had not yet fetchedthese samples, CAMP replied with an unknown verdict, thus
still protecting our users from these downloads. In general,
when we cannot fetch the binaries that our users download, due
to e.g. one-time URLs, the unknown verdict offers protection.
In the future, CAMP could be updated to use the lack ofability to fetch content from an URL as a feature by itself.
The browser could also be changed to allow users to upload
files. This would facilitate analysis of campaigns that host
executables on one-time URLs.

While this campaign aggressively switched domains and to any operating system and to any content type that can bechanges binaries, it did not rotate IP addresses as frequently. labeled. For example, with an accurate labeling mechanism
We observed it across only five IPs during a two week period: for browser add-ons, CAMP could render reputation-based
95.143.37.145, 46.21.154.155, 194.28.114.103, verdicts when a browser add-on is downloaded.
194.28.114.102, and 217.116.198.33. Our hypotheWhile CAMP is currently only available in Google Chrome,
sis is that the adversaries behind the campaign focused on we plan on making the service available to all web browsers
domain rotation to avoid widely-deployed blacklists and binary once we have gained more operational understanding of themutation to avoid AV engines, but were not concerned with IP system and have further improved CAMP’s detection rates.
rotation as there are few widely-deployed IP-based blacklists.
VI. D ISCUSSION

VII. C ONCLUSION

Blacklist-based approaches in which web browsers block
Although browsers have become more secure, the worldcontent from known malicious sites offer some protection wide web continues to be a significant contributor to malware
against malware, but suffer from a knowledge gap when ad- infections. Many of the defenses available to users such asversaries frequently switch to new domains or repack binaries. blacklists or AV engines face challenges as adversaries canBlacklists are still effective in protecting web browsers in evade detection by frequently changing hosting domains orsituations where it is not possible to quickly rotate domains, mutating their malware binaries until they are no longer
e.g., when using a compromised web site to drive traffic. detected.
A potentially more resilient approach leverages whitelists
This paper introduced CAMP, a content-agnostic malware
so that web browsers download content only from trusted
protection system, which employs a reputation system thatsites. Unfortunately, such a whitelist is never going to be
detects malware independently of the actual binary contents.
complete either, resulting in legitimately benign content not
CAMP protects browser users from malware downloads while
being available to web users. CAMP bridges the gap between
also minimizing the impact on user privacy. To get a reputablacklists and whitelists by augmenting both approaches withtion decision for a binary download, the web browser contacts
a reputation system that is applied to unknown content. As our
CAMP’s servers which automatically build reputation forevaluation has shown, CAMP does not suffer from significant
downloads and render reputation-based verdicts. If a download
false positives, but could benefit from higher detection rates.
is deemed malicious, the web browser displays a warning to
Utilizing more traditional machine learning approaches in
the user and offers to delete the downloaded file.
addition to the binary circuit currently employed by CAMP
We provided a detailed overview of CAMP’s design andmay improve detection. However, the ability for humans toarchitectureand discussed in detail all the components thatreason about detection verdicts is important to our deployment
constitute the reputation system. At its core, the reputation metand additional research is required to better reason about theric is calculated via a binary circuit that receives its input fromlarge models generated by machine learning approaches.
statistical aggregates. The statistical aggregates are computed
The performance of CAMP depends significantly on thebased on features derived from web browser requests andmechanism used for labeling binary samples. Any improvecontain information on how often they occurred in a malicious
ment to detection rates in the binary classifier will directly
context compared to the total number of occurrences.
translate to improved detection in CAMP. Our current binary
In this paper, we performed an extensive six month evaluclassifier is conservative and has the explicit goal of not
tolerating any false positives. However, it is conceivable that ation of CAMP consisting of over 200 million unique users
in the context of CAMP, we could tolerate a small number of of Google Chrome and millions of daily reputation decisions.
false positives to improve overall detection. Instead of using We showed that our content-agnostic detection approach isa binary analysis platform, we posit that binaries could also both accurate, with an accuracy of close to 99% relative to
be labeled by AV engines, for example, by taking a majority proprietary VM-based dynamic analysis, and well performing,
processing requests in less than 130 ms on average.
vote to determine if a binary is malicious or not.
In comparing CAMP with the current state of practice, we
One of CAMP’s important properties is to minimize theimpact on user privacy while still providing protection. To demonstrated that CAMP outperforms Anti-Virus, as well as
achieve this goal, the browser leverages a whitelist to limit various web services, e.g. McAfee’s Site Advisor, Symantec’s
the number of decisions which require server interaction. Even Safeweb, etc. Furthermore, CAMP augments Google’s Safe
when the browser does ask the server for a decision, only Browsing API, flagging 5 million malware downloads pera small set of features is sent. These features are stored for month that were not previously identified.
up to two weeks, and afterwards only aggregated informationis stored, but no longer than a few months. Despite severely
A CKNOWLEDGMENTS
limiting the data available to the system, our evaluation showsthat CAMP exhibits high accuracy rates.
The authors would like to thank Michael Bailey for helpful
While the content-agnostic nature of CAMP helps to reduce suggestions for this paper. We also thank our shepherd Lenx
its privacy impact, it also means that CAMP can be applied Wei for his valuable suggestions to improve this paper.

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