Web Application DDoS Attack Defense Using Access Correlation
JST: Smart Systems and Devices
Volume 31, Issue 1, May 2021, 001-008
Web Application DDoS Attack Defense Using Access Correlation
Nguyen Thi Thanh Tu1* Nguyen Thanh Tung1 Tran Manh Thang2
,
,
1Hanoi University of Science and Technology, Hanoi, Vietnam
2Authority of Information Security, Ministry of Information and Communications, Hanoi, Vietnam
*Email: [email protected]
Abstract
Web application distributed denial-of-service attack (Web-App DDoS Attack) is a common dangerous
attack that hackers use to attack the information systems of organizations. Web application is often
hackers' target because this kind of application is an external interface of an organization to provide the
organization's activities services. In addition, due to the emergence of weaknesses and security holes in
applications and operating systems, hackers can easily create a large-scale botnet for more effective Web-
App DDoS Attack. In fact, there have been many research projects related to the defense against this type
of attack. However, DDoS attacks still cause serious damage to the systems of organizations due to the
fact that the attack methods are increasingly sophisticated and constantly changing. In this study, we
propose a method for Web-App DDoS Attack mitigation on the basis of analyzing the relationship among
the requests sent to the Web application to find out the source IP address of malicious requests and to
perform mitigation. Our method provides a set of criteria that allows determining whether a source IP
address is normal or malicious in a short period of time. The criteria also make it difficult for hackers to
change the attack methods to overcome the characteristics of the criteria.
Keywords: DDoS Attacks, Flood Attack, Web-App DDoS Attack
1. Introduction
between requests sent from a source IP. However, in
the previous research, we only proposed ideas and
solutions to implement them in a very basic and not
specific way.
attack that destroys the availability of a server or an
information system by depleting resources or
bandwidth of the system. DDoS attack is usually
performed through a collection of hunched computers
in a botnet and controlled by a C&C Server. Web
application is a popular application to provide online
services of many different types of industries and
fields such as Economy, Politics, Finance,
E-commerce, Transport ... Most Web applications
providing services over the Internet using the
HTTP/HTTPS protocol. Therefore, in addition to
dealing with other types of network attacks (SQL
Injection, XSS…), the Web application also has to
face the DDoS attack. To perform a DDoS attack on a
Web application, hackers take advantage of the botnet
to send overflowing requests to the server, which
would cause server overload and fall into a state of
denial-of-service. Hackers often intentionally make
real requests, like requests sent from a normal
computer to bypass security devices such as Firewall
and IDS/IPS.
In this study, we propose a specific solution to
solve the problem of detecting the source of the
attack request on a Web application using the
correlation criterion based on a basic idea: Requests
sent from attackers will have the same characteristics
and repeat because the attackers and the programs are
controlled by the same C&C server. Whereas access
machines normally have a high degree of randomness
in the frequency of incoming access and requests.
Here we call our recommended method AntiDDoS-
AC for short.
Therefore, we seek to build correlation datasets
among requests sent from the same machine in a
normal operating state. Correlated data set will be
used to detect the source of the attack request when a
DDoS attack occurs on a Web server. We also
propose different criteria to make it difficult for
hackers to falsify the correlated data set during its
construction.
In the previous study [1], we proposed a model
to quickly detect the source of DDoS attacks on the
Web application on the basis of combining two
criteria: the frequency of access and the correlation
2. Related Work
Authors Qin Liao et al. (2014) propose a set of
features used to detect App-DDoS attacks on the
basis of a Web Server access log analysis [5]. The
authors use 9 features retrieved from the access log.
The features are divided into groups of required time,
frequency, and length. For experimental evaluation
data, the authors use ClarkNet-HTTP datasets. This
ISSN: 2734-9373
Received: 15 July 2020; accepted: 3 September 2020
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JST: Smart Systems and Devices
Volume 31, Issue 1, May 2021, 001-008
data set are collected over 2 weeks from a Web
test data set [3,4] to evaluate experimentally the
Server with 3,328,587 normal requests. For the attack
data, the authors simulate the attacks and mixes them
with the ClarkNet-HTTP dataset.
proposed method.
3. Web App-DDoS Attack Prevention Model and
Method
The authors compare the efficiency of the three
methods namely Naive Bayes, RBF Network, and
C4.5 with the data set obtained and with 14 features
that the authors proposed. Authors Ko Ko Oo et al
(2015) propose a method to prevent DDoS attacks on
the application layer using the Hidden Semi-Markov
model [6]. The authors use 03 groups of criteria on
frequency of sending requests to Web applications,
Web page reading time and order of received
requests.
3.1. Operating Model and Principle Of Operation
In this section, we propose the operating model
and principle of operation of the AntiDDoS-AC
method. The operation of this system is described
below.
The AntiDDoS-AC method has two main
functions: DDoS Detection (DDoS Detection) and
DDoS Prevention (DDoS Prevention) through finding
sources of sending attack requests to update to
BlackList. Separating the detection and prevention
functions into two independent functions allows
optimal performance and efficiency, because there are
less system resources devoted to the DDoS Detection
function than to the function DDoS Prevention. On
that basis, the components of the AntiDDoS-AC
method are built on two independent DDoS Detection
and DDoS Prevention functions whose principle of
operation is as following.
From these 3 groups of criteria, the authors
choose 7 specific criteria (the total number of
received packets, the total number of data sizes
received, the average size of the packet, the frequency
of packets received, data size per unit time, degree of
variation in duration and packet size) to provide input
to the authors method. The authors also proposed an
algorithm model including 5 steps to distinguish
attacks or normal packets.
The authors create test data to evaluate his
method using Hping3 tool. Authors K. Munivara
Prasad et al. (2018) propose a method to prevent
DDoS attacks on Web applications using the machine
learning method [2]. The authors use the K-Means
algorithm to group sessions connected to Web Server
into groups over time. In addition, the authors also
use the criteria of the request length, the proportion of
number of packets, and the time interval between
requests sent to the Web Server to establish attack
detection thresholds. The authors use LLDOS 2.0.2
When the system operates in a normal state, the
AntiDDoS-AC method will passively monitor the
network connection to collect Web application access
information and store it in the database. This database
is data input for two functions DDoS Detection and
DDoS Prevention. After a certain period of time, this
database is retrieved to check whether the system is
under a DDoS attack through the DDoS Detection
function.
Fig. 1. Functions in AntiDDoS-AC component
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Volume 31, Issue 1, May 2021, 001-008
Fig. 2. Basic model of AntiDDoS-AC method
In case, DDoS Detection detects an attack, it
3.3. Building Correlation Criteria in AntiDDoS-AC
Method
will set the Attack Status flag to 1 to start DDoS
Prevention function. DDoS Prevention function
works in an infinite loop and constantly check Attack
Status, if this value is set to 1, DDoS Prevention will
perform prevention function through finding the
source of the attack request and update to Blacklist.
After finding out the list of source IPs sending the
attack request, the AntiDDoS-AC method will send to
the network device (Router) / security device
(Firweall) to perform blocking. In case the DDoS
Detection function does not detect the attack, the
collected data set will be used to update the Whitelist.
The Whitelist list is used to allow the source IPs in
this list to have priority access to a Web application
when an attack occurs [8, 9].
The correlation criterion (we call it SAT2 for
short, as inconsistent with our previous research [1])
which is built on the basic idea that for normal users,
when accessing a Web application, they will access
various contents on the server (surf the web).
Therefore, when looking at the server side, we can
see the correlation of requests sent to the server from
each source IP. For example, when a user sends an rA
request to the server, he or she then sends an rB
request to the server as well. The question is how to
build correlated datasets to ensure that hackers can
hardly send false requests on purpose to the server
during normal operation and use the request set
during the attack.
3.2. AntiDDoS-AC Method
To solve this problem, during normal operation,
we provide a set of criteria that apply to each source
IP sending the request and the set of requests sent
from that source IP. Only when the source IP and the
requests simultaneously satisfy the conditions, can
they be included in building the correlated request set
using the Association Rule algorithm. From there, the
proposed method includes 02 processes of building
correlation sets and detecting attack requirements,
which are described in detail as below:
The basic idea of the AntiDDoS-AC approach is
that when the attack occurs, the requests sent to the
server will include both the attack requests and the
normal requests. In which, the requirements for the
attack make up the majority. Therefore, the problem
that the AntiDDoS-AC method has to deal with is
finding out the attack sender to put in the Blacklist
and the normal source to put in the Whitelist. The
basic idea of the AntiDDoS-AC method is described
as shown below.
a) Build correlation data set
In this study, the AntiDDoS-AC method uses
two correlation criteria. This criterion is built on the
basic idea that when observing the set of requests
received from the average user's computer, they will
have a certain correlation and randomness. While the
requests received from the attacker will have different
characteristics than usual (for example, these
machines send only one request repeatedly or a group
of requests according to the control command of the
C&C server). Therefore, when the system operates in
a normal state, we seek to construct a correlated data
set to identify the source of the attack request and
normal request when the attack occurs.
To determine whether a request is anomalous or
not, we cannot rely on each request individually,
because requests used in DDoS attack are generated
as normal requests. Therefore we need to find the
correlation between them to find the anomaly.
Through experimental observation, we found that
there is a difference between requests sent from a
normal computer and requests sent from an attacker.
Specifically: for a normal computer, when accessing
a Web page, the user will send different requests to
the Web server to access different information on a
Web page (illustrated as shown below). Whereas the
requests sent from the attacker are usually the same
requests or groups (because they are controlled by
the same C&C server) or having the same repetitive
pattern of change.
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JST: Smart Systems and Devices
Volume 31, Issue 1, May 2021, 001-008
Fig. 3. Illustration of correlation request submission
sampled the last k times. Let HT be the Entropy value
calculated after k sampling times. Therefore, we can
calculate the value.
k
HT =
H(Xn )
∑
n=1
From the analysis above, we realize that the
ability of randomness happening to the arrival of the
requests is higher than that towards the time of the
DoS / DDoS attack. Therefore, we choose threshold
value Thd = HT/α. Then, Entropy calculated with
sampled Hdata data will be calculated and compared
with Thd value. If Hdata < Thd, then DoS / DDoS
attack has occurred. α is the value applied by the
administrator in order to be able to change
accordingly protection-needed server.
Fig. 4. Illustration send request
As per our above analysis, the problem is how to
build clean correlation data sets that hackers can
hardly attack into the training phase with false
requests. In order to solve the problem, we build a set
of criteria applied to the source of requests and
corresponding requests to verify whether the requests
are sent from ordinary users. After specific period of
time, we pull the information out for verification. The
qualified requests and senders will be input to the
Association Rule algorithm to create correla data set.
The criteria we use are as following.
c) The validity of each source IP sending the request
In the next step, we need to determine whether
the source IPs which have satisfied the above
criterion meet the set of criteria below or not. In the
case that the source IPs satisfy the following set of
criteria, these IPs will be put on the Whitelist list and
requests sent from this source will be constructed
with the correlative data set. The criteria include:
b) Random criteria regarding time of access
This criterion is to figure out whether URL sent
from different source IP are eligible to build
correlated datasets. We observed that, if the system
operates smoothly, any requests from different IP
sources can be sent randomly in time to the server.
However, if the system is attacked, departure time of
requests will no longer be random because they are
all forced to send simultaneously as shown below.
- Requests that are not sent again in Δs time. We
find that the normal user when accessing a website
takes time to read the content of that page and do not
resubmit the request within a certain period of time.
However, with attacking computers, the same request
can be sent continuously during a small amount of
time. Therefore, we believe that the normal source
should satisfy the condition of not resending sent
requests within a certain period of Δs time. The Δs
value may vary from system to system based on
monitoring the system's performance without being
hacked. In this study, we choose Δs = 5s, based on the
collected attack log data to evaluate the proposed
solution.
According to the above observation, for this
criterion, we use Entropy to calculate the randomness
in time to send the requests. We use unit TT and this
unit is divided into smaller units ∆t (the value of ∆t is
set according to each specific protection-needed
system by the administrator). And the value of
Entropy H (X) is calculated based on the arrival
probability of each request being sent within a certain
time and divided as mentioned above. Let n be the
required number of submissions per sampling, so the
Entropy of the H (X) value is calculated as follows:
- Average arrival time deviation of requests will
be sent from a DTR source IP: The moment when
requests are sent from the same source IP from a
normal machine will have some degree of
randomness, while requests are sent from the attacker
has a certain degree of correlation (due to being sent
through a same malicious program). To identify clean
n
H X = − pilog2 pi
(
)
∑
i=1
We need to determine the Entropy value for the
point of arrival of normal mode requests. We have
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Volume 31, Issue 1, May 2021, 001-008
sending sources, we determine the total time
To determine the input parameters for the
Association Rule algorithm (level of support and
reliability), we collect logs of access data on the
actual network environment at Hanoi University of
Science and Technology when the system is not
under attack. Then, we apply the Association Rule
algorithm to the data set with different levels of
support and reliability to find out a suitable value for
the largest correlation set. Through experiments, we
determined the level of support equal 2 and reliability
greater than 50% are the appropriate values for the
largest number of correlation sets. However, for each
system that requires different level of protection,
those values may be changed accordingly.
deviation calculated by the time of arrival of each
request. With normal sending sources, this value will
have a certain magnitude, while with attackers this
value will below. We use an MR array of n elements
to store information about the duration of requests
received from a source IP. For example with normal
source MR = [12,3,5,10,…,n]), with attack source
MR = [2,2,3,2,…,n]). Let i be the ith request received
and Ti the time the request is received. From there the
total time deviation between the requirements is
calculated by the following formula:
n
T
i+1 −T
∑
=
i
i=1
DTR
n
3.4. Design the Data Structure for the Algorithm
A TTR threshold is set to determine whether the
arrival times of requests received from a source IP
satisfy a normal condition or not. If the total deviation
DTR > TTR, the arrival time of requests received from
an IP is then deemed to satisfy the condition. In this
study, we choose the value TTR = 10s, on the attack
log data we use the experimental evaluation model of
the proposed solution. This value may vary
depending on each protected system.
The second issue that comes along with this
criterion is how to quickly to detect whether requests
are offensive or normal ones, in the case that the
server receives a lot of requests when an attack
occurs. To solve this, we design 02 structured data
tables (TR và TA) with the algorithm that allows
storage and quick searching for a set of requests
satisfying correlated conditions when the server
receives many requests at the same time.
- DL url length average deviation criterion:
Normal machines when sending a request to the
server usually have different lengths (calculated by
the number of characters in the url) due to the
different content visited on the server. While the
lengths of the requests sent from attackers is usually
the same and repeatable. Therefore, the DL deviation
of the attack machines will be much smaller than the
normal ones.
- Similar to the above approach, we use the ML
array to store the length of n requests sent to the
server from a source IP. For example with normal
source ML = [12,30,50,120,…,n]), with attack source
MR = [30,30,55,55,…,n]). Let i be the ith request
received and Ci be the length of each request
received. Average deviation DL is determined by the
following formula:
Fig. 5. Structure of TR data sheet
TR datasheet has the structure as shown above,
used to store information about the hash value of each
url and corresponding index in the hash-table. We use
TR table separately to store index and hash
information (url) due to large number of urls which
need to store information. Storing that information
separately will optimize storage space and processing
time. We will present in details as shown in the
algorithm below.
n
C − D c 2
Ci
( )
∑
i
i=1
DL =
D c =
( )
n
n
From there, for each source IP with inbound
request set, we determined that this DL value must be
greater than a pre-set TDL threshold based on each
specific system that needs to be protected. In this
study, we choose TDL = 60 characters on the attack
log data, to evaluate the proposed solution
experimentally. After finding the source IPs and
requests sent from those IPs satisfing the same
conditions above, the source IP will be put into the
Whitelist and the data set including the IP and
requests will be used as input of the Association Rule
algorithm to construct the correlation data set.
We use the TA data sheet, structured as shown in
figure 6, to store information to be processed when
the first request from a source IP arrives. The
information about the correlated request set in the TR
table is then transferred to the TA table to verify
whether that subsequent requests from each incoming
source IP are normal or attack. Information in the
table only has a lifetime of Δtc based on the time the
test takes place and the time when the server received
the first request.
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Volume 31, Issue 1, May 2021, 001-008
required sending frequency greater FT, after each
interval check for the condition of DInp.
b) Set the input parameters for the algorithm:
Value ∆s = 0.5s, test time with new request for
each SrcIP; The value Count = 3, the value specifies
the number of repeated requests in a period of ∆s
from a srcIP; The above values are pre-set and may
vary by system.
Fig. 6. Structure of TA data sheet
In this study, we choose the value Δtc = 300s.
This means we assume that in about 5 minutes the
user sends another request to the server and we can
verify whether that the source IP is normal or not.
300 seconds is the amount of time required to verify
if a sender is normal or not. However, if the IP is
attacking, it will be verified as soon as the next
request is sent to the server without wasting Δtc
(300s).
3.5. Detect the Source of the Attack Using the
Correlation Data Set
a) Some concepts used in the method
FT threshold to detect suspect attack set: FT
attack detection threshold is pre-set threshold to
define the same requests that the server receives in a
unit of time.
Fig. 7. AntiDDoS-AC algorithm
The
AntiDDoS-AC-STA2
algorithm
is
performed with the following steps:
Set of suspected RA attack requests: A request
that satisfies a condition with the frequency of
sending a FT request from the same source IP
address.
Step 1: For each request in the DSus
corresponding to the corresponding source IP, define
the index and the corresponding set of that source
C(srcIP) in the TR, then input into the TA as well as
update the corresponding timestamp.
The BL list is used to store the source IP
addresses information identified as the attack sources
after requests sent from these source IP addresses
have been checked against the set of criteria. For each
different attack request R(i), we have a list of
different source IP addresses S(s,i) and corresponding
to the frequency of sending requests f(s,i).
Step 2: Periodically after each ∆s period, check
in the DInp table if there are new requirements for
each srcIP in TA. If yes, check the following steps.
Step 3: Calculate the hash value of the request.
If the hash value of the incoming request exists in the
C(srcIP) correlation set then put that IP in Whitelist.
The list of sources sending BlackList attack
requests is denoted BL = {B1, B2, …, Bn}, Bi =
[S(s,i),R(i),f(s,i)].
If the hash value matches R value then update
count value for that srcIP and check if count value
(srcIP) ≥ 3 or not. If so, put that IP in Blacklist.
DInp Input Data Set: Structured data set used to
store the request information sent to the server. For
each new request sent to the system, DInp will be
updated.
The IPs put in Whitelist and Blacklist will be
deleted in DInp and DSus correspondingly to release
memory.
6. Experimental and Result
Data set to store suspect attack requests DSus:
This data set has the same structure as the DInp, used
to store all information related to the request which
are considered suspected attack after satisfying the
6.1. Creat Experimental Data
For experimental evaluation, we have built a
botnet on a virtual environment on 03 highly
configurable physical servers. Experimental system
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Volume 31, Issue 1, May 2021, 001-008
with a scale of 13 Cisco routers and 45 client
6.3. Comparison of the effectiveness of the
AntiDDoS-AC Method with Other Methods
computers, installed with Win XP and Centos
operating systems. These computers are infected with
malicious code and under the control of a C&C server
to perform TCP Syn Flood attack on Web Server.
Routers are set up in a virtual environment using
GNS3 software [7] with the Cisco 7200 Router series.
Table 2. Comparison of experimental testing results
among AntiDDoS-AC, KNN, and NB
Detection
Methods
KNN [10]
False Positive
Rate
After performing a simulated attack for a period
of 3 minutes, we obtained a data set stored as PCAP
which contained 66,851 packets. The resulting
database got 1,310 requests, including both attack and
normal requests, and was stored in MySQL database.
89.03%
1.03%
1.47%
0.89%
NB [9]
92.47%
93.75%
AntiDDoS-AC
We perform log sampling accessing website of
Graduate Training Institute (Hanoi University of
Science and Technology) from October 17, 2018,
18:21:22 to October 18, 2018, 09:20:35. As a result,
we obtained 6,686 requests from 372 different srcIPs.
Results of comparison regarding processing time is
shown in figure 8:
6.2. Experimental Evaluation
We perform an experimental testing evaluation
with a dataset using the following parameters used as
follows:
-
Threshold of detection of suspicious FT attack
requests (requests / second).
-
-
The quantity required in the DSus.
The quantity of suspicious requests to attack
RA.
Fig. 8. Test data processing time of AntiDDoS-AC,
KNN, and NB
-
-
The correct detection rate of the sources
sending attacks: Detection Rate - DR.
The results showed that, on the same test data
set, the AntiDDoS-AC method has a higher rate of
detecting attack requests (Detection Rate - 93.75%)
and the false detection rate (False Positive) is 0.89%,
compared to the two compared methods.
The false detection rate (define a normal
request as an attack one): False Positive - FP.
Experimental testing results prove a high
detection rate of our method as shown below:
7. Conclusion
Table 1. Experimental testing results of the
AntiDDoS-AC method
In this study, we propose a specific solution to
solve the problem of detecting the source of the
attack request on the Web application using the
correlation criterion with the proposed method,
AntiDDoS-AC. Experimental testing results show
that our proposed method has a higher rate of
detection of attack sources with less amount of time
needed. However, the experimental results are only
evaluated on the data set generated from a self-built
test environment.
FT
DSus
RA
DR
FP
50
250
500
185
365
668
897
863
75.32%
92.56%
94.08%
88.75%
67.89%
1.28%
1.11%
0.89%
1.47%
1.49%
100
150
200
300
750
1000
1310
In our next research, we will continue to study
new criteria to make our methods more accurate and
effective. In addition, we will keep on studying
experimental evaluation on many different data sets
which will surely enhance the degree of objectivity in
evaluating the proposed method.
From the above experimental testing results, it
shows that the rate of detecting the source of the
attack request depends on the FT value. This value
should be adjusted to suit each server that needs to be
protected. If the FT value is set too small, the false
detection rate will increase. If the FT value is set too
large, attack senders with low request frequency will
be omitted and the rate of detecting DR attack
sources will decrease as well.
Acknowledgements
We would like to express our sincere thanks to
Assoc. Prof. Nguyen Khanh Van and Assoc. Prof.
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Volume 31, Issue 1, May 2021, 001-008
[4]. D.M. Powers, in Evaluation: from Precision, Recall
Nguyen Linh Giang for their valuable suggestion.
Our gratitude extends to colleagues of the
Information Network Center for coordinately
implementing this experimental research. Besides, we
are thankful for the support from the Department of
Information Security - Ministry of Information and
Communications through the National Science and
Technology Mission: “Research and develop a
Reference Framework for Information Security for e-
government”, code: KC.01.07 / 16-20.
and F-measure to ROC, Informedness, Markedness
and Correlation, 23rd international conference on
machine learning (Pitsburg, 2006).
[5]. Qin Liao, Hong Li, Songlin Kang, Chuchu Liu,
Feature extraction and construction of application
layer DDoS attack based on user behavior,
Proceedings of the 33rd Chinese Control Conference,
July 2014.
[6]. Ko Ko Oo, Kyaw Zaw Ye, Hein Tun, Kyaw Zin Lin
and E.M. Portnov, “Enhancement of Preventing
Application Layer Based on DDOS Attacks by Using
Hidden Semi-Markov Model”, Genetic and
Evolutionary Computing pp 125-135, August 2015.
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Retrieved from Lincoln Laboratory:
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