APPLICATION-LAYER DDOS DETECTION BASED ON A ONE-CLASS SUPPORT VECTOR MACHINE

International Journal of Network Security & Its Applications (IJNSA) Vol.9, No.1, January 2017
DOI: 10.5121/ijnsa.2017.9102 13
APPLICATION-LAYER DDOS DETECTION BASED ON
A ONE-CLASS SUPPORT VECTOR MACHINE
Chuyu She1, 2, 3
, Wushao Wen1,2
, Zaihua Lin1
, and Kesong Zheng1
1
School of Data and Computer Science, Sun Yat-Sen University, Guangzhou 510006, PR
China
2
SYSU-CMU Shunde International Joint Research Institute, Shunde 528300, PR China
3
School of Mathematics and Statistics, Guangdong University of Finance & Economics,
Guangzhou 510320, PR China
ABSTRACT
Application-layer Distributed Denial-of-Service (DDoS) attack takes advantage of the complexity and
diversity of network protocols and services. This kind of attacks is more difficult to prevent than other kinds
of DDoS attacks. This paper introduces a novel detection mechanism for application-layer DDoS attack
based on a One-Class Support Vector Machine (OC-SVM). Support vector machine (SVM) is a relatively
new machine learning technique based on statistics. OC-SVM is a special variant of the SVM and since
only the normal data is required for training, it is effective for detection of application-layer DDoS attack.
In this detection strategy, we first extract 7 features from normal users’ sessions. Then, we build normal
users’ browsing models by using OC-SVM. Finally, we use these models to detect application-layer DDoS
attacks. Numerical results based on simulation experiments demonstrate the efficacy of our detection
method.
KEYWORDS
Application-layer DDoS attack, One-Class Support Vector Machine, machine learning, feature, browsing
model
1. INTRODUCTION
Denial of Service (DoS) attack is a significant security challenge on the Internet. The ease of
conducting DoS attack comes from the existing limitations on the Internet protocols such as TCP,
UDP and the ready availability of attack tools [1]. In traditional DoS attacks, an attacker uses
only one node. However, attackers nowadays can control multiple nodes to launch attacks. These
attacks are classified as Distributed Denial-of-Service (DDoS) attacks [2]. DDoS attacks may be
carried out at the network layer. These kinds of attacks mainly exploit vulnerabilities of protocols
of network layer [3]. However, with the progress of defence method, the DDoS attackers are
gradually targeting the application layer. Application-layer DDoS attacks are more complicated to
detect. Lots of methods have been proposed to defend against DDoS, such as statistical
approaches, algorithms based on signatures and so on. Statistical approaches take account of
packet attributes such as source IP and destination IP address, time to live (TTL), and so on.
These methods often assume that the distribution of network traffic characteristics will change
when a DDoS attack happens. Actually, these methods are effective in protecting DDoS attack at
IP or TCP layers. However, they can’t distinguish the application-layer DDoS attack packets
from normal packets because they lack the analysis capability of application layers. Algorithms
based on signature may detect attacks on any communication layer. However, they can detect
known attacks only.

14
Literature on detecting application-layer DDoS attacks can be also found. Ramamoorthi et al.[4]
proposed an anomaly detection mechanism to detect DDoS attacks by using Enhanced Support
Vector Machine (ESVM) with string kernels. SVM is a relatively new machine learning
technique based on statistics. Due to its excellent generalization ability compared with the
traditional intelligent methods, e.g. neural network, SVM has been famous and popular in many
areas [5]. However, the primal SVM only focuses on two class classification problem. When the
problem is imbalance, the performance deteriorates [6].
In this paper, we use One-Class Support Vector Machine (OC-SVM) to detect application-layer
DDoS attack. OC-SVM is a special variant of the SVM. OC-SVM which is proposed to deal with
the unbalanced problems of classification, intends to find the smallest hyper-sphere containing the
positive data. As for the test point, OC-SVM only judges it whether the test point belongs to that
class [7]. Since only the normal data is required for training, it is effective for detection of
application-layer DDoS. Our contributions in this paper are summarized as:
1) Seven features are proposed and extracted from users’ sessions based on the differences
between attack users and normal users.
2) A normal user’s browsing model is built by OC-SVM to detect application layer DDoS attack.
3) Numerical results demonstrate the effectiveness of the algorithm based on a real website.
The rest of this paper is organized as following. In Section 2, we describe the related work of our
research. In Section 3, we introduce our detection model and algorithm. In Section 4, we validate
the efficiency of our detection method. We conclude our work in Section 5.
2. RELATED WORK
Most studies about DDoS focus on IP layer or TCP layer [8]. Numerous variables such as IP
address, port, or TCP flags can be analysed to detect Net-DDoS [9]. Lakhina et al. [10] use traffic
feature distributions to detect anomalies. Xue et al. [11] present the histogram of the maxima of
bounded traffic rate on an interval-byinterval basis as a traffic feature for exhibiting abnormal
variation of traffic under DDOS flood attacks. Lee et al. [12] cluster IP addresses and TCP and
UDP ports on backbone routers to find DDoS attacks. Simmross et al. [13] proposed a method to
detect two anomaly types, namely floods and flash-crowds in network traffic, based on a non-
restricted α-stable first order model and statistical hypothesis testing. Stavros et al. [14] proposed
a method for DDoS detection by using fuzzy estimators. Neural networks [15] and [16] detect
DDoS attacks combine with machine learning.
Literature on detecting application-layer DDoS attacks can be also found. Some researchers have
used “puzzle” or identification codes that need users’ interaction, which may disturb users. Park
et al. [17] proposed that web servers can reply with probe scripts which detect whether there are
mouse motion and other normal user’s behaviours. Yu et al. [18] discriminated DDoS attack flow
from flash crowd by the similarity of flows. Since attack flows are generated by a fixed program,
attack flows are similar. Oikonomous et al. [19] found that request dynamics, request semantics
and ability to process visual cues can be used to differentiate humans and attack agents. Many
studies on web user behaviour mining formed four types. Type I is probabilistic model. For
example, Brklen et al. [20] characterized page jump-probability with Zipf-like distribution, set
double Pareto distribution to model the link-choice, and used log-normal distribution to capture
revisiting behaviour. Type II is click-streams and web content mining, e.g., Velsquez et al. [21]
used click-streams dataset and page content to mining user’s usage patterns. Type III is a kind
that detectors monitor user’s behaviour such as system-calls by user’s processes. Type IV is

15
based on classical pattern recognition model—Markov model. Xie et al. [3] used Hidden semi-
Markov Model to capture users browsing behaviours.
3. DETECTION MODEL AND ALGORITHM
In this section, we introduce OC-SVM algorithm to detect application-layer DDoS based on 7
features (CMBF) we selected.
3.1. Detection Algorithm
We use OC-SVM algorithm to train normal sessions and then build normal users’ behaviour
models. And new sessions that deviate from the normal users’ behaviour models are identified as
anomalies. OC-SVM which is proposed to deal with the problems of classification, intends to find
the smallest hyper-sphere containing the positive data. OC-SVM is to implicitly map the data
points from the input space to the feature space by means of a nonlinear kernel function [22].
After being mapped into the feature space, the training data will be treated as belonging to one
class [23]. Then a boundary is found in the feature space that separates the test points into normal
and anomalous measurements.
Now, we introduce the detail of our detection algorithms.
Before running the OC-SVM, dataset should be pre-processed. OC-SVM algorithm needs a data
structure, called data matrix.
















=
npnkn
jpjkj
pk
xxx
xxx
xxx
X
......
...............
......
...............
......
1
1
1111
(1)
The data matrix denotes the normal data set. It is a pn × matrix, which n means it has n objects,
and p means objects have p features. So we collect normal data and select features first.
3.1.1 Generate Sessions of Users
For a user, his or her browsing behaviour is a request sequence. We assume that one client IP,
two consecutive requests are less than m (default is set as 1800) seconds away are treated as in
the same session [24].
According to the definition above, we consider { }nn trtr ,,,,,IPS 11uu L= as a session
describing the interaction between user u and web server, where stt ii 18001 ≤− − .
3.1.2 Feature Selection
There are many differences between normal users’ sessions and attackers’ sessions, such as
request rate, requested resources, request order, session time and so on. Based on these
differences, we can select features from sessions. Before selecting session features, several
definitions need to be introduced.
1) Resource Popularity
Resources have different access frequency in a website. The word resource mentioned here
has a flexible meaning: it can be a webpage, a picture, a video, a voice or a text, etc. Jung et
al. [25] said that 10% of webpages account for approximately 80-90% of requests. They

16
proved that resources popularity follow Zipf-like distribution. So we define popularity of
resourcei , as following:
all
i
i
CA
CA
POP = （2）
Here, iCA is the number of the resource i accessed in a period of time, while allCA is the
number of all the resources accessed in this period of time. Resource A’s popularity is higher
than B’s, if A’s access frequency is more than B’s.
2) Transition Probability
On a website, the transition probability between every two resources is different. The
following formula shows how to calculate this probability.
alli
ji
ij
Trans
Trans
P
→
→
= (3)
jiTrans → means transition times from resource i toj , and alliTrans → is transition times
from i to others.
3) History Transition Matrix
This matrix records transition times from one resource to any other resource, defining as
follow:
















=
nnnin
iniii
ni
trans
ttt
ttt
ttt
M
LL
MLLLM
LL
MLLLM
LL
1
1
1111
(4)
Here, ijt is a transition time from resource i to resourcej .
As concepts and definitions have been introduced above, the following features are selected
for OC-SVM algorithm. And these features can be extracted from web server logs.
1. sessionN : The total number of requests in a session.
2. Obviously, for HTTP flood application-layer DDoS, it’s very important that a session
should have enough requests for attack purpose. So we consider sessionN as a very
important feature for detection of this kind of application-layer DDoS attack.
3. sessionPOP : The average popularity of all requests in a session.
4. sessionPOP represents average popularity of all requests in a session. Some application-
layer DDoS use random request sequence to launch an attack because it is very easy to
implement. And random request attack is more complicated to detect than the attack of
simply request one or some particular resources. As study [25] shows that most resources
have relatively low popularity, the sessionPOP of a session generated by random request
attack is lower than normal users’ sessions. So this character is proper for detection of
random request attack.

17
5. sessionP : The average transition probability of all adjacent requests in a session.
6. sessionP represents average transition probability of all adjacent requests in a session.
For random request attack, its request sequence is randomly generated. Compared to
normal users’ sessions, generally, sessionP of attack session is lower than most normal
sessions’. That is to say, this feature is very useful to distinguish normal users’ session
and random attack session.
7. sessionSize : The total size of all request in a session.
8. An attacker can just request large resources such as a large video resource, which is a
simple request but may cost a large volume of bandwidth. We consider sessionSize as
session load. So session size is a very important feature for detection of attack focusing
on large resources.
9. sessionD : The duration of a session.
10. It’s the time range from the first request to the last request in a session. Generally, normal
users won’t stay in a website for a long time. For stealthy attacks, it may take longer time
to achieve attack effect. So this feature is useful for detection.
11. nreplycode : Reply code
12. Statistically, HTTP reply codes from web server have different frequencies. Generally, a
better attack effect can be gained by more effective request. So attack session may have
more reply code 200 than normal one, unless it simulates normal session reply code
distribution, but this may weaken the attack effect in turn.
13. dynamic : The times of appearance of dynamic pages.
14. Normally, dynamic pages involve database query or insertion, computation of
complicated scripts and other operations with heavy load. A little “smarter” attack tool
will attack this vulnerability of a website. So this feature is useful for detection.
15. Now all the features above can be combined into a vector, like this:
i. ),,,( dynamicPOPNFeatures sessionsession L= (5)
3.1.3 One-Class Support Vector Machine (OC-SVM)
As mentioned above, X represent training set consisting of n normal sessions. {xi,i = 1,2,···,n},
xi ∈ Rp
, (p is the dimension of xi).
Let Φ(xi) denote the image of xi in the feature space. For the classification problem of two
categories, the data sets are not always linearly separable in the original space. Φ projects the
original data sets into a higher dimensional space which called feature space and the non-
separable data sets become linearly separable in this space [5]. The training instances are mapped
into the feature space and separated from the origin by the hyperplane with the maximum margin
[26]. In order to obtain the boundary, an optimization model is considered as follows:
( ) 0,-.
1
2
1
min
1
2
≥≥•
−+ ∑=
ξξρφ
ρξ
ii
n
i
i
xwts
nv
w
（6）

18
where n is the number of the data points; iξ is the non-negative slack variable of xi; v is a
regularization parameter and v ∈ (0,1) controls the fraction of outliers. w and ρ are the parameters
which determine the decision boundary.
Φ is a kind of mapping. Φ is inexplicit, however, the inner product of the image Φ(xi) can be
computed via kernel function. Such as linear kernel, polynomial kernel, radial basis function
kernel and sigmoidal kernel [6]. These kernel functions are listed as:
1) Linear kernel: ( ) j
T
iji xxxxK =, (7)
2) Polynomial kernel: ( ) ( )p
j
T
iji xxxxK C, += γ (8)
3) Radial basis function kernel: ( ) ( )



 −−= 22
2exp, σjiji xxxxK (9)
4) Sigmoidal kernel: ( ) )tanh(, cxkxxxK j
T
iji −= (10)
Where i and j range over 1,...n; γ and c are constants; p is the degree of the polynomial and σ is
the width of radial basis function kernel. In this paper, we selected radial basis function kernel as
the kernel function K(xi,xj) because the radial basis function kernel can approximate most kernel
functions if the parameter σ is chosen appropriately [27].
In order to solve the optimization problem (6), Lagrange multipliers αi, βi ≥ 0 are introduced for
the constraints w·Φ(xi)−ρ + ξi ≥ 0 and ξi ≥ 0. The Lagrange equation is formed as:
( ) ( )( ) i
n
i
iii
n
i
i
n
i
i xw
nv
wwL ξβξρφρξβρξ ∑∑∑ ===
−+−•∂−−+=∂
111
2 1
2
1
,,,, (11)
In order to get the optimal value, the partial derivatives of the Lagrange equation (11) with
respect to w, ξ and ρ are computed and set to zero.
( ) 0
1
=•∂−=
∂
∂
∑=
i
n
i
i xw
w
L
φ (12)
0
1
=−∂−=
∂
∂
ii
i nv
L
β
ξ
(13)
01
1
=−∂=
∂
∂
∑=
n
i
i
L
ρ
(14)
Then, we can obtain the following formulas:
( )i
n
i
i xw φ•∂= ∑=1
(15)
ii
nv
β−=∂
1
(16)
1
1
=∂∑=
n
i
i (17)

19
Substitute formulas (15), (16) and (17) into Lagrange equation (11), and its dual form is presented
as follow:
1,
1
0.
min
1
=∂≤∂≤
∂∂
∑=
n
i
ii
T
nv
ts
Y
(18)
Where α = [α1, α2... αn] is the vector form of Lagrange multipliers for the constraints. Y is the
kernel matrix of the training set, it can be expressed as follow:
( ) ( ) ( )jijiij xxxxkY φφ •== , (19)
The instances {xi | αi > 0, i = 1... n} are called support vectors. The function f(x) is as follow:
( ) ( ) 





−∂= ∑∈SVi
ii xxKxf ρ,sgn (20)
Where SV is the indices of support vectors; ρ is computed by the following formula:
( ) ( )
{ }
∑
≠∂∈
∂==
0
,
jjj
jiji
T
xxKxw φρ (21)
For a new session x, f(x) returns 1 if x is a normal session; f(x) returns -1 if x is an abnormal
session.
Algorithm 1 shows that the set of normal sessions are trained by OCSVM based on the selected
features. Firstly, we extract features from the set of sessions. Secondly, we normalize the feature
vectors. Finally, we use OC-SVM method to train the normal behavior model.
3.1.4. Detection Process
As the normal users’ behavior models above have been built, these models can be used to detect
anomaly of behavior. For a new session x, detection algorithm calculates the result base on the
formula (20). If the result returns 1, the session will be recorded as normal; If the result returns 1,
the session will be recorded as abnormal, and the corresponding user IP will be added to blacklist.

20
Algorithm 2 shows the detection process
3.2. Detection Architecture
This paper trains normal sessions and build normal users’ behaviour models by OC-SVM. As we
have built the normal users’ behaviour models, we can use these models to detect anomaly of
behaviour. Fig.1 shows the detection architecture. Firstly, we select some features according to
the diﬀerent browsing behaviour between attack users and normal users. Secondly, we use OC-
SVM to get the boundary of normal sessions set and build the normal users’ behaviour models
based on the features we selected. When the detection begin, the system constantly gets requests
from the HTTP request queue, and adds the requests to the corresponding user’s session. And
then detection algorithm calculates whether the current session is in a normal session class. If the
session is found to deviate from the normal session class, we record the session as abnormal, and
add the corresponding user IP to blacklist. If the session is in the normal session class, we record
the session as normal one.
Figure 1. Detection architecture

21
4. NUMERICAL RESULTS
4.1. Datasets
To validate our defence method, we used the web-logs of the website of Sun Yat-sen University.
The logs were collected on 10/4/2013. There are 20978 IP, 1,281,876 requests, and 2480
resources through the whole day. Timestamps have one-second precision.
In this experiment, the dataset above are seen as normal dataset. The requests of normal datasets
last from 0s to 86382s. We launch a random attack from 40000s to 55000s. So attackers’ requests
are mixed with the normal users’ requests from 40000s to 55000s. Fig.2 shows the arrival rate of
normal traffic. And Fig.3 shows the arrival rate of attack traffic.
Figure 2. Normal traffic arrival rate
Figure 3. Attack traffic arrival rate
As mentioned above, request distribution is very important for our detection system. When
random attack begins, request distribution will change. Let’s compare the entropy of request
distribution between normal datasets and attack datasets. As the definition above, the popularity
of resource i is POPi. So, the entropy of request distribution is calculated as following.
( )ii POPPOPentropy 2log∑−= (22)

22
We compute the entropy every 10 seconds. As we can see in Fig.4 Clearly, entropy changes after
40000s, and returns to be stable after 55000s. It matches the period of our simulation attack.
Figure 4: Normal and attack request entropy
4.2. Detection results
Our detection system uses OC-SVM to train the normal data from 0s to 40000s and then build
normal users’ model.
Based on the normal users’ model we built, the system is ready to detect attacks. If a user is
detected as abnormal, the system add this user to blacklist and block this user’s requests.
otherwise, the system record it as normal. Fig.5 is the Receiver Operating Characteristics (ROC)
curves showing the performance of our detection model on application-layer DDoS attack.
Figure 5: ROC of detection model

23
5. CONCLUSIONS
This paper proposed an application-layer DDoS detection method based on user behaviour model.
To build user behaviour model, we extract features from users’ sessions and cluster these sessions
by OC-SVM method. And then, we use the model to detect anomaly of user behaviour.
Numerical results based on real-traffic simulations demonstrate the efficiency of our detected
strategy.
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Authors
Chuyu She is a Ph.D candidate in the School of Data and Computer Science at Sun Yat-Sen University.
She is also working as a lecturer in the School of Mathematics and Statistics at Guangdong University of
Finance & Economics. Her research interests include network security and software engineering.
Wushao Wen is a professor in the School of Data and Computer Science at Sun Yat-Sen University. He is
co-appointed as an adjunct professor at SYSU-CMU Shunde International Joint Research Institute. His
research interests include computer and network security, universal threat management, telecom networks
and cloud computing security.
Zaihua Lin is a student in the School of Data and Computer Science at Sun Yat-Sen University. His
research interests include computer and network security, big data, and social engineering.
Kesong Zheng is a graduate student in the School of Data and Computer Science at Sun Yat-Sen
University. His research interests include computer and network security, software engineering, big data,
spark and data mining.

APPLICATION-LAYER DDOS DETECTION BASED ON A ONE-CLASS SUPPORT VECTOR MACHINE

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