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title: Cashing Out the Great Cannon? On Browser-Based DDoS Attacks and Economics
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    title: Cashing Out the Great Cannon? On Browser-Based DDoS Attacks and Economics
    author: Giancarlo Pellegrino, Christian Rossow, Fabrice J. Ryba, Thomas C. Schmidt, Matthias Wählisch
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  - Christian Rossow
  - Fabrice J. Ryba
  - Thomas C. Schmidt
  - Matthias Wählisch
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# Cashing Out the Great Cannon? On Browser-Based DDoS Attacks and Economics

**Cashing Out the Great Cannon? On Browser-Based DDoS Attacks and Economics** - Giancarlo Pellegrino, Christian Rossow, Fabrice J. Ryba, Thomas C. Schmidt, Matthias Wählisch, usenix.org.

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Cashing out the Great Cannon?
            On Browser-Based DDoS Attacks and Economics

          Giancarlo Pellegrino                              Christian Rossow                                  Fabrice J. Ryba
             Saarland University                             Saarland University                           Freie Universität Berlin
        gpellegrino@mmci.uni-        crossow@mmci.uni-                  fabrice.
              saarland.de                 saarland.de              ryba@fu-berlin.de
                        Thomas C. Schmidt             Matthias Wählisch
                                      HAW Hamburg                                     Freie Universität Berlin
                                   t.schmidt@haw-                                       m.waehlisch@fu-
                                      hamburg.de                                           berlin.de

ABSTRACT                                                                      we review typosquatting and injection of malicious ads as potential
The Great Cannon DDoS attack has shown that HTML/JavaScript                   methods to acquire new browser-based bots. We then systematically
can be used to launch HTTP-based DoS attacks. In this paper, we               review features of modern browsers that could be used for attack,
identify options that could allow the implementation of the general           such as JavaScript APIs (e.g., XMLHttpRequest, WebSocket). Next,
idea of browser-based DDoS botnets and review ways how attackers              we measure how dangerous these features actually are in a DoS
can acquire bots (e.g., typosquatting and malicious ads). We then as-         attacks. That is, in a local experiment, we measure the potential
sess the DoS impact of browser features and show that at least three          request rates of these attacks and discuss the evasion flexibility they
JavaScript-based techniques can orchestrate clients to send thou-             offer (e.g., manipulation of HTTP headers).
sands of HTTP requests per second. Seeing the vats potential, we                 Second, we provide an economic comparison between traditional
evaluate the economics of browser-based botnets and show that their           DDoS botnets and advertisement-driven browser-based botnets (§ 3).
cost are about as high as traditional DDoS botnets—while giving far           We aim to answer the following research questions. What are the
less flexibility in terms of attack features and control over the bots.       costs for operating the two types of botnets? How long do bots stay
Finally, we discuss victim- and browser-side countermeasures.                 online, once acquired? And is it likely that profit-maximizing cyber
                                                                              criminals will jump on the wagon of attacks like the Great Cannon?
                                                                              We approximate the costs for both malware-driven and browser-
1.    INTRODUCTION                                                            based DDoS botnets. Our results show that costs are comparable,
    Distributed Denial-of-Service (DDoS) attacks continue to be a             ranging between $0.0006 and $0.02 per day and attack source.
severe problem to the Internet. In April 2015, researchers observed              We conclude with a discussion on defenses against browser-based
a new type of DDoS attack, coined the Great Cannon [1]. Here,                 botnets (§ 4). We discuss defenses from the victim’s point of view
a powerful attacker injected malicious JavaScript code into HTTP              (such as header-based filtering), and present ideas on how current
traffic. The malicious code turned browsers into DoS clients by               browsers can be adapted to mitigate such kind of attacks.
aggressively requesting web resources from victims. The Great Can-
non thus acted as man-in-the-middle and was reported to manipulate            Our summarized contributions are as follows:
web communication at Chinese ISPs to attack GitHub.
                                                                                   1. We systematically review browser features that may support
    We envision that even less powerful adversaries can launch sim-
                                                                                      browser-based DDoS attacks and measure their impact.
ilar browser-based DDoS attacks. The Great Cannon incident has
illustrated that web clients can serve as DoS bots—even without                    2. We approximate and compare the costs for malware- and
being compromised by malware. Attackers have abused the feature-                      browser-based DDoS botnets.
rich communication API of browsers to launch DoS attacks, similar
to how traditional DDoS botnet would operate. Actually, Grossmann                  3. We discuss potential client- and victim-side defenses against
and Johansen already presented this kind of threat at Black Hat USA                   browser-based DDoS attacks.
2013 [2], showing that attackers can inject malicious ads to launch
                                                                                 Our preliminary results show that there are still several open ques-
DDoS attacks. Kuppan mentioned HTML5 even at Black Hat 2010
                                                                              tions, which should be tackled by the research community. In this
as a potential vector for DDoS attacks [3]. However, while the gen-
                                                                              paper, we strongly advocate for continuing the systematic analysis
eral attack principle is already well-known [4], there is no systematic
                                                                              of this threat landscape to increase trust in the web ecosystem.
understanding of which browser features may be (ab)used in such
attacks. Right now, only anecdotal reports show that certain browser
APIs can be abused, but without giving sufficient detail. Moreover,           2.     BROWSER-BASED DDOS BOTNETS
browser-based DDoS attacks seem promising also to less powerful                  The Great Cannon attack has revealed the vast potential of abus-
actors, such as cyber criminals with economic incentives. However,            ing normal web clients as weapon in DDoS attacks. We will revisit
little is known about the usefulness (and costs) of browser-based             such browser-based DoS attacks from a different attacker model, in
DDoS attacks for “typical” cyber criminals.                                   that we assume an attacker with economical incentives. According
    In this paper, we aim to close this gap. We start by exploring            to most analyses, the Great Cannon attack was only possible be-
ways how cyber criminals may actually attract DoS clients, similar            cause ISPs actively manipulated web traffic. The attacker(s) injected
to their need to establish a traditional botnet (§ 2). For example,           JavaScript code to normal websites that would launch DDoS attacks


                                                                          1
towards certain targets. In contrast, we assume an attacker with sig-             per-click and not per-view basis. Pay-per-click ads are especially
nificantly less power, with the goal to assess if browser-based DDoS              appealing to an attacker, as this allows to inject malicious code that
attacks may even attract other types of attackers. In Section 2.1, we             is only viewed by clients and thus does not introduce additional
thus discuss how an attacker can rent or invest in clients—instead of             costs.
just hijacking them via traffic manipulation.
   Furthermore, in Section 2.2, we will investigate various methods               2.2     Browser as a Bot
how an adversary can leverage HTML or JavaScript code to perform                     Next, we are going to compare browser-based DDoS attacks with
DoS attacks. Then, we will compare these attacks with features of                 attacks that DDoS botnets can launch.
traditional malware-based DoS bots.                                                  DDoS botnets span malware-infected hosts that are specialized in
                                                                                  the execution of distributed denial of service attacks. Typically, the
2.1     Acquisition of Browser Bots                                               bot master dispatches the target coordinates and the type of attack to
                                                                                  each bot. Bots can perform a variety of attacks, such as SYN floods
   In order to launch a browser-based DDoS attack, the first step an              or requesting web resources via HTTP. Additionally, bots can vary
attacker has to perform is acquiring an army of bots. In our con-                 each attack to evade detection and increase its effectiveness.
text, bot refers to web clients that can be instructed using common                  In contrast to malware, browser-based DoS attacks have limited
HTML/JavaScript code. Note that we do not require to compromise                   flexibility. Here, we assume that the attacker does not exploit the
the host, i.e., the browser-based bot is different from the malware-              browser, but just uses some active code (such as JavaScript) to
infected host. Still, how can an adversary find bots to launch a                  launch an attack. In the following, we revisit communication fea-
DDoS attack? We imagine a few methods an attacker might use, as                   tures of modern web browsers and discuss to what extent they can
presented in the following:                                                       be leveraged in DoS attacks. We aim to reproduce HTTP-based DoS
Typosquatting—First, an attacker could leverage typosquatting [5,                 attacks that are also common in modern botnets, such as Yoddos
6] to obtain new clients that mistype a domain they wanted to visit,              or DirtJumper [7, 8]. That is, we will discuss how we can generate
i.e., registering domain names that are similar to well-known sites.              DoS-like request behaviors purely by using a non-compromised
Once the clients visit the web site, the attacker would try to increase           browser. We chose to focus on JavaScript-based attack code due to
the time the user stays on this website. For example, the adversary               its popularity and wide availability.
may show the correct content via HTML <iframe> tags, hiding
the fact from the user that she actually is on a fake website.                    2.2.1     DoS-Enabling JavaScript Features
   Popular typosquatting domains are already pre-registered by the                   JavaScript programs can issue HTTP requests via APIs designed
owners of the correct domains or by attackers. However, the web                   for the network communication such as XMLHttpRequest and Web-
domain space is large and an attacker may fish in the long tail. Do-              Socket. Furthermore, JS programs may use other APIs that impli-
main tasting (i.e., temporarily registering domains) is not prohibited            citly result in sending HTTP requests. For example, the JS program
in general. For country code top-level domains, ICANN charges                     can modify the content of the src attribute of the <img> tag, and
a surcharge of $0.20 per domain. For a domain at registrars such                  as a result, the browser will issue an HTTP request to fetch the con-
as Dynadot, this leads to overall costs of $1.00-$5.00. Assuming                  tent of src. In the following, we will describe four APIs that can be
$2.5 on average and 10 visitors per domain/day, a quick back-of-the-              abused to launch HTTP(S)-based DoS attacks: XMLHttpRequest
envelope analysis shows that an attacker could easily instruct 4,000              (XHR) API [9], the WebSocket (WS) API [10], and Server-Sent
clients with a budget of $1,000. Our preliminary measurements                     Event (SSE) [11], and the Image API [12].
indicate that 10 visitors per day are reasonable for typosquatting do-
                                                                                  XMLHttpRequest—This API is used to send asynchronous re-
mains that have been selected in a plain, non-sophisticated way—we
                                                                                  quests to the server side of a web application [9], i.e., XHR requests:
expect significantly more visitors with carefully selected domains.
   However, this field needs further research in several directions:          1 var target = "http://$target/";
How many users can be gathered in parallel and how long do the                2 var xmlhttp=new XMLHttpRequest();
users stay on such sites? Which domains should be registered? We              3 xmlhttp.open("GET", target);
                                                                              4 xmlhttp.send();
leave these questions to future work.
Instrumenting Machine-Generated Visits—Another way to at-                            As opposed to the other three JS features, the XMLHttpRequest
tract clients is making websites popular (e.g., using SEO) and then               API allows a JS program to control some headers and the request
try to catch machine-generated visitors, such as crawlers. As soon as             method of the HTTP request. For example, the JavaScript program
a webpage is registered with a search engine or linked to another site,           can set an arbitrary HTTP body and content type, and, it allows to
this page is likely to be scanned by crawlers or even attackers that              set a few HTTP request headers, e.g., the request content type [9].
scan for known vulnerabilities or backdoors. On arbitrary requests,               WebSocket API—WebSocket allows the client and the server side
the botnet controller can deliver pages including external references.            of a web application to establish a full-duplex stream-oriented com-
Hijacking Popular Websites—Since many years, attackers have                       munication channel [10], i.e., a web socket. WebSocket is an ex-
hijacked well-known websites to exploit their visitors via drive-by               tension of the HTTP protocol in which first the parties perform a
downloads. Instead of infecting the clients, attackers could just                 handshake and then can stream data over the web socket.
inject some JavaScript code to the HTML structure of the website,                    As the WebSocket handshake involves HTTP requests, an attacker
which renders the attacking code.                                                 may use XHR requests to initiate a web socket. To avoid that, the
Instrumenting Ad Networks—Finally, we discuss how to use ad                       handshake implements a protection mechanism which leverages
networks to obtain browser-based bots—a technique that we will                    on additional headers that cannot be modified by client-side pro-
analyze in more detail in this paper. Online ads are important                    grams [10]. Unfortunately, the WebSocket protocol specifications
building block in the business chain of the web ecosystem. Ad                     do not describe any mechanism to protect non-WebSocket servers
networks provide a convenient way to distribute advertisements to                 from malicious WebSocket clients. Malicious JS code may misuse
a large crowd of users. Beneficial for the potential attacker is that             the handshake by requesting resources hosted by a non-WebSocket
ad services such as the Google Display Network charge users only                  server. Such server may ignore the characteristic web socket HTTP


                                                                          2
    headers, and thus it can accept WebSocket handshake HTTP re-                 and 3 web workers. For each test, we captured the network traffic
    quests as normal HTTP requests. A malicious JS program can                   for further analysis.
    misuse the WebSocket protocol as follows:                                    Results—The result of the analysis is shown in Tables 1 and 2.
1   var target = "ws://$target/";                                                Table 1a shows the aggregated results. Table 1b details the attacks
2   var websocket = new WebSocket(target);                                       via a single-threaded JavaScript program (no web workers). Table 2
                                                                                 shows the results with a varying number of web workers.
       The variable target contains the URL of the target. WebSocket                Table 1a shows the results of our tests per browser and per TCP
    URLs use the HTTP scheme ws:// or wss://. The scheme                         port states. The columns [Reqs/s] is the average number of HTTP
    ws:// resolves the default TCP port 80 and refers to a WebSocket             requests per second whereas [SYN/s] is the average frequency of
    server that does not use any secure transportation layers. Instead,          TCP SYN packets sent to the server.
    the scheme wss:// resolves the default TCP port 443 and relies                  When the TCP port is open, the XMLHttpRequest can generate
    on SSL/TLS. Our example code instantiates a WebSocket client and             1,000 and 2,100 HTTP requests per second with Chrome and Firefox,
    passes the variable target. As a result, the web browser will start          respectively. The WebSocket API turns out to generate less than
    the WebSocket handshake with the target. Although the web server             35 req/s with Chrome and almost zero request with Firefox. This
    is a non-WebSocket server, it will process the HTTP request as a             is caused by the behavior of Firefox upon failure in setting up a
    valid request.                                                               WebSocket. In our experiments, we use the WebSocket handshake
    Server-Sent Event API—SSE is a communication API which al-                   to request resources to a non-Websocket server. This causes the
    lows a JavaScript program to receive a stream of events from the             WebSocket handshake to fail. When this happens, Firefox introduces
    server side [11]. The channel is established similarly as seen before        delays between consecutive attempts. This delay reduces drastically
    for the WebSocket. First, the browser sends and HTTP request to the          the number of requests per second that the browser issues. Then,
    server, which dispatches server events. Then, the server responds            the Server-Sent Event API can produce 210 and 250 requests per
    with an HTTP response and a stream of events. SSE may also be                second for Chrome and Firefox, respectively. Last, the Image API
    abused by a malicious JS program in an attack:                               can generate about 80 and 750 requests per second. Table 1 shows
1   var target = "http://$target/";
                                                                                 also that the number of SYN packets rate is about the same for HTTP
2   var source = new EventSource(target);                                        requests. This behavior is caused by the fact that the server does not
                                                                                 use persistent TCP connections with the browser. This means that
    Image API—The Image API is a JavaScript-based interface to the               the server terminates the TCP connection after sending the HTTP
    <img> HTML tag [12]. A malicious JavaScript program can abuse                response. However, if the server supports persistent connections,
    the Image API. For example, instead of providing the URL of an               the number of SYN packets per second reduces drastically1 . In our
    image, it can provide the URL of the target to attack as follows:            experiments, we observed that the number of SYN packets reduces
                                                                                 of about x13 for the XHR, SSE, and Image API. However, in the
1   var img = new Image();
                                                                                 case of WS the number of SYN packets per second remains the same.
2   img.src = "http://$target/";
                                                                                 This is caused by the errors during the creation of a web socket. As
      In this fragment of code, the JavaScript code initializes a new            mandated by the WebSocket protocol, the party detecting the failure
    Image object. Then, it sets the src property of the Image class              terminates the TCP connection. As opposed to the frequency of
    with the absolute or relative URL of the image—the target. When              SYN packets, the number of sent requests slightly increases. For
    the browser interprets this fragment of code, it will issue an HTTP          example, in about 70% of our experiments the number of requests
    request for the resource / from $target.                                     per second increases of a multiplicative factor between x1.01 and
                                                                                 x2.
     2.2.2    API Aggressiveness                                                    When the TCP port is either in a packet drop or reject state, the
       To launch successful DoS attacks, an attacker needs to instrument         requests and SYN rates are negligible. However, when the kernel
    a client to send many HTTP requests. While we found that four                sends TCP RST packets for closed TCP ports, our the JavaScript
    JS APIs allow us to send HTTP requests in principle, we need to              APIs almost consistently exhibit a very aggressive behavior. Even as
    measure their “aggressiveness”. In this section, we will describe an         compared to the “Open” state, the SYN packets frequency is signific-
    experiment to measure the request rates that the four APIs offer.            antly higher. This is caused by the fact that browsers typically limit
    Testbed—In our experiments, we used a server (the target) and a              the number of parallel connections, whereas the connections (due to
    client (the HTTP bot) connected to a Gigabit LAN. The server is an           the RST) early leave this stage—and new connection attempts are
    Intel Xeon dual-core 2.50GHz with 8GB of RAM. The client is a                established. It is worth to point out that SYN floods are not really a
    quad-core Intel i7. We set up Lighttpd [13] on the server listening          security issue if the port is in a packet drop, reject, or reset state.
    to four TCP ports. We then configured the firewall to change the                Finally, Table 1a shows that Firefox, in general, performs better
    states of the four TCP ports to the following: open port (accept             than Chrome with speeds that are 2x and 9x faster than Chrome.
    connections), ignore incoming packets (drop), respond with ICMP                 Table 1b shows the results of our test when no workers are not
    host unavailable, and finally, with a TCP reset packet.                      used. The column Avg. [Reqs/s] is the average HTTP request per
       The client was set up with Mozilla Firefox 37.0.2 and Google              second when calling 1000, 2000, 3000, and 4000 times per second
    Chrome 42.0.2311.135. Then, we instrumented each browser with                the API functions. The column Max [Reqs/s] is for the maximum
    a JavaScript code that was constantly establishing requests for a            values. With exception of the WS API, Firefox achieved the highest
    run-time of 60 seconds. First, we wrote the malicious script to              request per second rate of our experiments. Firefox can sent requests
    continuously invoke API calls. However, this approach causes the             per second with a rate of about 2,800 Reqs/s with the XHR API,
    browser to stall or even crash. We modified this approach by setting a       1,900 Reqs/s with SSE API, and 1,900 Reqs/s with Image API.
    maximum number of API calls per second that the malicious scripts            These values are in average 7x faster than the speed of Chrome.
    intends to send. We considered four different API call frequencies:          By comparing the SSE results of Firefox between Table 1a and
    1000, 2000, 3000, and 4000 calls per second. In our tests, we also
    used web workers in order to parallelize the API calls. We used 0, 2,        1 These results are not shown in Tables 1 and 2



                                                                             3
                                           TCP port states                                                              TCP port state
                              Open               Drop       Reject       Reset                                           Open
                       [Reqs/s] [SYN/s]        [SYN/s]     [SYN/s]      [SYN/s]                               Avg. [Reqs/s] Max [Reqs/s]
  XHR      Chrome      1,005.30    1,012.47        0.60        2.76     2,102.14        XHR       Chrome              1,359.59       1,886.33
           Firefox     2,165.76    2,166.43        0.60        4.42     4,821.30                  Firefox             1,456.74       2,892.03
  WS       Chrome         34.65       34.65        0.09        1.45         37.45       WS        Chrome                58.31             73.47
           Firefox         0.04        0.04        0.19        0.04          0.04                 Firefox                0.12              0.13
  SSE      Chrome        210.69      211.12        0.60        2.82      529.27         SSE       Chrome               399.97          941.58
           Firefox       258.69      259.60        0.20        0.91      912.09                   Firefox              776.07        1,907.48
  Image    Chrome         84.60       84.65        0.63        2.73       161.40        Image     Chrome                84.60          109.38
           Firefox       751.15      751.21        0.60        5.43     2,237.81                  Firefox              751.15        1,916.28
(a) Results grouped by TCP port state. Values are the average values with 0, 2, (b) Excerpt of results only for TCP port open and no
and 3 web workers (except for the Image API), and with 1000, 2000, 3000, and workers.
4000 API calls per second.

                            Table 1: Aggressiveness of JavaScript APIs as implemented by Chrome and Firefox.


Table 1b, it emerges that the average number of requests per second                                       Workers       [Reqs/s]   [SYN/s]
in Table 1b is considerably greater than Table 1b. This difference is
                                                                                    XHR       Chrome              0     1,359.59   1,370.11
caused by the fact that in Firefox the Server-Sent Event API is not
                                                                                                                  2       966.69     973.51
available within Web Workers.
                                                                                                                  3       689.63     693.80
   Table 2 shows the results with different web workers. With the
XMLHttpRequest API, Chrome and Firefox present an opposite                                    Firefox             0     1,456.74   1,456.66
behavior. While with the increase of workers Chrome decreases the                                                 2     2,424.13   2,425.50
number of requests per second, Firefox slightly increases the packet                                              3     2,616.40   2,617.14
rate. Chrome also exhibits a similar behavior with the WebSocket                    WS        Chrome              0        58.31         58.31
and SSE API. As said before, Firefox has negligible request rates                                                 2        29.30         29.30
with WebSocket, whereas with SSE can generate about 800 Reqs/s                                                    3        16.33         16.33
without workers. As explained before, Firefox does not allow the
access to the SSE API within web workers. As a result, the number                             Firefox             0         0.12          0.12
of requests per second is zero. Finally, the results with the Image                                               2         0.00          0.00
API are the one showed in Table 1b with no workers. According to                                                  3         0.00          0.00
the Web Worker specifications, web workers have no access to the                    SSE       Chrome              0       399.97     400.92
DOM which includes the Image interface. As a consequence, we                                                      2       155.05     155.27
did not perform tests with web workers.                                                                           3        77.05      77.19
                                                                                              Firefox             0       776.07     778.81
2.2.3     Further Attack Features and Filter Evasion                                                              2         0.00       0.00
   DDoS attacks are more effective if adversaries can hide their                                                  3         0.00       0.00
malicious traffic within benign traffic. Whereas a traditional DDoS                 Image     Chrome              0        84.60         84.65
bot has all the flexibility to generate HTTP(S) traffic, this is not
the case for browsers. In this section, we will discuss if (and how)                          Firefox             0       751.15     751.21
evasion techniques could be implemented.
Arbitrary Referer and Host headers—One of the DDoS com-                      Table 2: Aggressiveness correlated with the number of workers.
mands of traditional bots is issuing HTTP requests with custom-              The average number of requests is calculated between the values
chosen Referer and Host header. JavaScript programs can                      when invoking 1000, 2000, 3000, and 4000 times per second the
modify HTTP request headers only with the XMLHttpRequest API.                API calls.
However, the JavaScript program cannot modify all the HTTP re-
quest headers. There is a blacklist of headers that cannot be modi-
fied, including Referer and Host. This may leave defenders are               and in different moments (e.g., before the reception of the HTTP
valuable angle to characterize malicious communication.                      response or after the reception of the first packet of the response).
                                                                             In this section, we details these two aspects.
Requests with no Response—Second, some DDoS bots feature an
                                                                                While a traditional bot has direct control of TCP connections
attack type that requests resources via HTTP, but does not wait for
                                                                             and it can terminate them in many ways (e.g., TCP RST, TCP
the responses. The motivation behind this CPU or memory exhaus-
                                                                             FIN), a client-side JavaScript program cannot directly setup TCP
tion attack is that the server has to fetch the requested resources
                                                                             connections2 and it relies on high-level communication APIs which
(which may be large), and the client does not need to receive it.
We thus inspected if JavaScript code can interrupt the TCP socket            2 The W3C is working on a draft to standardize TCP and UDP
before the HTTP response is entirely received. A bot can interrupt a         sockets [14]. Browsers supports TCP and UDP sockets however,
TCP socket in different ways (e.g., by sending an RST or by drop-            their access is limited to extensions or to privileged external applica-
ping incoming TCP packets without acknowledging their reception)             tions [15, 16].


                                                                        4
abstract away the details of the underlying TCP connection. These                  To evaluate the complexity for an attacker to inject a malicious
APIs provide primitives to abort a request or to close the connection.          advertisement, we tried to derive a basic understanding of the veri-
For example, the XMLHttpRequest API allows to abort an XHR                      fication process, i.e., if the verification is handled manually or auto-
request via the abort function. Similarly, the WebSocket API and                matically. For this we uploaded complementary advertisements
the Server-Sent Event API have a close function. Protocol and                   that are copies of Ad 1-4 but refer to a different landing page in
API specifications do not mandate the specific technique to terminate           case a user clicks the ad. This landing page reflects the content of
a TCP connection upon the call of these functions. However, in                  the original landing page but makes the content invisible. These
our experiments we observed an uniformity of behavior between                   incorrect advertisements have also been accepted. We suppose that
Chrome and Firefox—both browsers terminates the TCP connection                  the verification process is performed by rather simple processes
with a RST packet.                                                              such as pattern matching rules. Any advanced check, in particular a
   After clarifying how to close connections via JavaScript, we now             verification by a human, could easily reveal our trap.
elaborate when this can be done. A traditional bot can terminate the            Client Statistics—Over one week of measurements, the ad network
TCP connection at any point in time, e.g., right after sending the last         generated 32,932 requests to our external server. Those requests res-
TCP packet of the HTTP request, or right after receiving the first              ult from only presenting the embedded advertisement on a customer
TCP packet with the HTTP response. In contrast, the JavaScript                  page of the ad network. In addition, we measured 174 requests that
communication APIs do not allow a direct way to control in which                result from clicks. It is worth noting two observations. First, Google
point the connection can be closed. However, an attacker can con-               Display follows a pay-per-click (ppc) model, leading to very low
trol disconnects by scheduling timeouts (e.g., via setTimeout).                 costs of ≈$1.23 in our case, i.e., four advertisements initiated overall
While a short timeout can cause to reset the connection right after             33k requests to an external server. Second, we could easily increase
the TCP handshake, a longer timeout can cause the browser to re-                the number of requests by changing our HTML/JavaScript code. An
ceive the entire HTTP response. In order to send the RST packet in              increased number of requests will increase the attack potential but
the right moment, an attacker may need to estimate the timeout by               not affect the costs as requests initiated by our ads are independent
monitoring the response time of the target.                                     of clicks.
IP Spoofing—IP source address spoofing is frequently used by                       Surprisingly, the number of requests varies significantly per day
attackers to hide their identity or to launch amplification attacks [17].       and advertisement type (HTML, JavaScript, and XHR). Content is
While potentially possible for a traditional DDoS bot, it is not                not loaded via XHR requests (Ad 4), and content requests using
possible to send IP-spoofed traffic via JavaScript, though.                     plain HTML (Ad 1) is more evenly distributed (cf., Table 3).
                                                                                   In the next step, we focus on dedicated users by analyzing client
3.    DDOS BOTNET ECONOMICS                                                     IP addresses in more detail. Figure 1 shows how long a client was
                                                                                viewing an advertisement on average, based on the data Ad 3 creates.
  In this section, we will measure the costs of running browser-                The box plot visualizes the mean (square), median (line), and the
based as compared to the costs of traditional malware-infected bot-             25- and 75-percentiles of the gathered data. Note that we cut the
nets. This will help to understand if the attackers may have an                 y-axis for visibility reasons. The maximum value for May 10 is
economical incentive to resort to browser-based DDoS attacks (as                785 minutes.
opposed to buying malware installations).                                          Overall, a significant distribution among the clients is visible,
                                                                                which is not surprising for two reasons. First, users behave quite
3.1     Costs for Browser-Based Bots                                            differently when viewing web content. Second, when a user changes
   For our preliminary measurements, we deployed four advertise-                a web site depends also on the presented content (e.g., news site
ments in the Google Display Network from May 10-17, 2015. We                    versus search website). However, in our current setup we cannot
explicitly followed a conservative model in the sense of simple ad-             control on which website the advertisement is embedded.
vertisements and a non-sophisticated attacker strategy. Using this                 We compared the number of sessions per client with the number
approach we gain insights into the ad network without assuming ex-              of impressions provided by Google and found that Google indicates
perienced attackers, which is in line with our perspective of attacks           much higher number of visits. It is rather unlikely that this is due
for the mass.                                                                   to ad blockers because those tools use black lists and thus do not
   Each advertisement includes HTML or JavaScript code to request               prefetch code. For Ad 2-4 this might be due to disabled JavaScript
resources from an external monitor server, in detail:                           at the client-side, which then leads to less external requests at our
                                                                                monitoring server. For Ad 1, which is using plain HTML, we would
Ad 1 requests a URL in the static structure of the HTML page;                   expect less deviation. Using the Google impression statistics for the
                                                                                estimation of the attack impact leads to overestimated results. This
Ad 2 requests a URL via the JavaScript interface of HTML tags;
                                                                                observation nicely illustrates that the design of our methodology
Ad 3 requests every five seconds a resource as in Ad 2;                         (i.e., relying on an external monitor) was crucial.
                                                                                   More surprisingly is that the number of unique clients heavily
Ad 4 sends a single content request using API designed for com-                 depends on the day (cf., Table 3). For an attacker, this complicates
     munication, i.e., the XMLHttpRequest API.                                  predictions about the size of the botnet.
                                                                                   Finally, we analyze the distribution of the geographic location of
   The different access mechanisms allow us analyze both the local              the clients using the MaxMind IP to country mapping. Around 80%
configuration of the users as well as protection mechanisms of the              of the IP addresses viewing our ads are assigned to Russia. Among
ad network. Ad 3 enables us to measure the session time, i.e., how              the remaining top ten countries are also Germany, Switzerland, UK,
long a user stays on the site that shows the advertisement.                     and France. All of these countries provide good Internet connection,
Deployment Experiences—Advertisement are verified by Google                     which will allow the attacker to initiate even large volume content
before they are officially published. Our advertisements have been              access.
accepted within 30-40 minutes. Deploying a malicious ad campaign                   Our current results can be considered as the minimum attack
is thus possible on short notice.                                               potential, which is already high. Only less than 1% of the users of


                                                                            5
                                                   External Requests [# GET and HEAD]                              Clients [# Unique IP Addresses]                Budget [$]
                                     Day           Ad 1     Ad 2              Ad 3       Ad 4            Sum       Ad 1     Ad 2    Ad 3       Ad 4     Ad 1    Ad 2     Ad 3    Ad 4     Sum
                                    05/10        243                 2      24,076            0      24,321           122       2     69           0     0.05       0        0       0    0.05
                                    05/11        232                 0         182            0         415           116       0      2           0        0       0        0       0       0
                                    05/12        262                 1        3129            0       3,399           169       1      3           0     0.23       0        0       0    0.23
                                    05/13      2,170                 8          80            0       2,252           774       3      5           0     0.59       0        0       0    0.59
                                    05/14      1,112                 2         459            0       1,573           759       2      2           0     0.07       0        0       0    0.07
                                    05/15        515                 0           0            0         515           384       0      0           0     0.05    0.03        0       0    0.08
                                    05/16        412                 2           0            0         414           318       2      0           0     0.06       0     0.02       0    0.08
                                    05/17         43                 0           0            0          43            40       0      0           0     0.11       0     0.02       0    0.13

                                                     Table 3: Overview of attack potential per advertisement and day measured at our external monitor.


                           2 5 0                                                                                                these bots once he bought them. We thus measured how long a
                                                                                                                                traditional malware-infected bot would stay online. To this end, we

                           2 0 0
                                                                                                                                leverage our data set from our infiltration of the Zeus P2P botnet
                                                                                                                                with sensors in October 2013. This data set constitutes one of the
                                                                                                                                very few sources to measure the uptime of malware-infected hosts,
S e s s io n T im e [m ]




                           1 5 0
                                                                                                                                in particular since Zeus-infected host have a unique identifier that
                                                                                                                                allows us to track individual bots. Albeit Zeus P2P has not been
                                                                                                                                used for DDoS attacks frequently—in fact it had the capability to
                           1 0 0
                                                                                                                                perform such attacks—we assume that populations of other botnets
                                                                                                                                behave similar. On average, a bot stays online for 11.9 hours per day,
                                                                                                                                i.e., about half a day. In addition, we observed that 63.5% of the bots
                             5 0                                                                                                are still infected after 1 week. This is along the lines of our previous
                                                                                                                                observation that the Zeus P2P botnet population fluctuates about
                                                                                                                                5% [19] per day. We thus estimate that—with a single infection—a
                                0                                                                                               bot remains operative for about 20 days, resulting in about 10 days
                                        0 5 /1 0          0 5 /1 1            0 5 /1 2        0 5 /1 3         0 5 /1 4         of an online bot. Combining both observations, we conclude that
                                                                         D a te [m m /d d ]
                                                                                                                                traditional malware costs between $0.0006 and $0.014 per day and
                                                                                                                                source—assuming of full utilization of the bot whenever it is online,
        Figure 1: Statistical overview: Duration in minutes a client sees Ad 3                                                  and considering infection costs of $0.006 and $0.14 per bot.

                                                                                                                                3.3     Economics Analysis
        Ad 3 click the advertisement, where each click costs ≈1 cent. The                                                          We now compare the costs for the deployment of malicious ads
        attacker is able to attract up to 69 users per day. In the best case a                                                  with the deployment of traditional malware by a brief back-of-the-
        web client was under the control of the attacker for up to 13 hours.                                                    envelope calculation. In fact, we found that the costs for both
        These preliminary results indicate that an attacker may achieve much                                                    botnets are comparable—between $0.006 and $0.014 per day and
        higher impact with more sophisticated malicious advertisements.                                                         attack source. Browser-based botnets are cheaper than infections
        On the client recruiting side, an attacker could try to create less                                                     in high-cost countries (like the US), but are more expensive than
        attractive ads to reduce the probablity of clicks (i.e., costs) or try to                                               botnets in countries for which pay-per-install (PPI) installation cost
        control ad replacement with respect to more frequently visited pages.                                                   less [18]. However, our economic analyis is clearly limited. First, we
        On the ad programming side, the attacker could simply increase the                                                      only compared the prices of one PPI network with one ad network.
        number of initiated requests. By comparing Ad 1 with Ad 2 and 3                                                         Second, attacks may actually build up DDoS botnets for free (e.g.,
        we already found that the amount of clients differs. Implementing a                                                     by infecting embedded devices with default logins). Last, we did
        much more aggressive request scheme in Ad 1 could lead to higher                                                        not try to improve our ad to make it less attractive to being clicked
        attack potential but we do not have experiences how the ad network                                                      on in order to reduce the pay-per-view price. Still, we show that the
        react on this. We will focus on a more complete anatomy of ad                                                           costs are largely similar.
        networks in future work.                                                                                                   However, the functionality of browser-based bots is limited com-
           To summarize, we found varying viewing behaviors for the ads we                                                      pared to traditional bots, as the common web API exposes less func-
        injected. When computing the costs for brower-based botnets, we                                                         tionality. For example, malware may monetize in more ways than
        focus on the results of Ad 3, as this advertisement allowed us to track                                                 just DDoS attacks (e.g., ID theft or spamming), whereas browser-
        the viewing time of the instrumented clients. For the accumulated                                                       based botnets are most suitable for DDoS. Then again, considering
        online time of all clients (2,327 minutes), this specific ad cost $0.04.                                                that new web technologies, such as WebRTC, offload system func-
        On average, an attacker has to pay a risk budget of $0.02 per day                                                       tionality into the web browser, we can expect a rich set of interfaces
        and source.                                                                                                             in the near future. Having a feature set comparable to malware
                                                                                                                                within a browser will increase the revenue of browser-based botnets.
        3.2                         Costs for DDoS Malware                                                                         Finally, the level of control for browser-based DDoS botnets is
           Previous studies found out the price for malware installations                                                       limited. Most importantly, ad campaigns introduce a delay between
        in the underground range between $6 and $140 per 1000 installa-                                                         issuing and viewing the ad, whereas an attack using a DDoS botnet
        tions [18]. Still, it is unclear for how long an attacker can abuse                                                     can be started immediately via C&C commands. Another drawback


                                                                                                                            6
of ad networks is that they are less predictable how many bot clients            network). To the best of our knowledge, the attacking code cannot
are recruited, and the number of simultaneously-running bots is low.             overwrite the Origin header with an arbitrary URL. Still, such
Our preliminary results showed that there is room for optimization,              filters may be too coarse-grained (e.g., blocking requests from entire
e.g., by making the ad more attractive to be displayed by investing              ad networks) and may also block benign clients. In addition, if
higher ad costs or spreading the ad among multiple ad networks.                  ad networks allow ads to be loaded from any external hosts, then
                                                                                 the Origin can be chosen by the attacker—while still requiring
                                                                                 multiple hosts or domains to vary the value.
4.    CONCLUSION AND OUTLOOK                                                         Furthermore, servers can deploy rate limiting based on the HTTP
   We have discussed browser-based DDoS botnets, a serious threat                Referer header values. This header is inserted by the web browser
to the Internet. We have shown that the attack does not introduce                and frequently used. However, this might change in the future, as
higher costs at the side of the adversary. Instead, the attacker model           the Referer conflicts with privacy concerns. Second, the browser
is in line with requirements (expertise, money, etc.) of our threat              must not send a Referer field if the previous page was accessed
model. However, we have also shown certain limitations to browser-               via HTTPS [20]. Third, with HTML 5, a website may include an
based botnets, both regrading the attack flexibility and the way the             attribute that instructs the browser not to send the Referer field.
bots can be controlled. In the following, we will nevertheless discuss
ideas to mitigate some of the problems of DDoS-based botnets. We                 4.2     Future Work
will finally conclude this paper with an outlook to future work.
                                                                                    In the future, we aim to improve our measurement on the at-
4.1     Attack Mitigation                                                        tack economics. We will add experiments of other ways to acquire
                                                                                 browser-based bots, such as typosquatting. In addition, to improve
Rate Limiting—We analyzed for two common browsers (Chrome,                       the statistical significance of our cost estimations, we will expand
Firefox) how many media items are allowed to be loaded in parallel               our measurements to multiple PPI and ad networks. Second, we in-
(e.g., img src=""). All of them had a limit of six but we also                   tend to analyze the potential of a peer-to-peer control layer between
found that loading via JavaScript is less limited. Such a limit needs            web browser based on WebRTC. Third, we will investigate the
always to be considered with respect to the quality of experiences for           solution space to the browser abuses. So far, our ideas are just
a user. It is very likely that this limit will be increased in the future,       hypothetical, and we plan to design more detailed schemes and
in particular with an increased deployment of multipath transport.               thoroughly evaluate them.
Partial Cross-Origin Resource Sharing—Disabling cross-origin
resource sharing (CORS) prevents a client from loading resources
located under a different origin than the origin of the webpage. All             5.    REFERENCES
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                                                                           8
