COMPARATIVE ANALYSIS OF ANOMALY BASED WEB ATTACK DETECTION METHODS

International Journal on Cybernetics & Informatics (IJCI) Vol. 4, No. 6, December 2015
DOI: 10.5121/ijci.2015.4602 13
COMPARATIVE ANALYSIS OF ANOMALY BASED WEB
ATTACK DETECTION METHODS
Achin Jain and Vanita Jain
BharatiVidyapeeth College of Engineering, New Delhi, India
ABSTRACT
In the present scenario, protection of websites from web-based attacks is a great challenge due to the bad
intention of the malicious user over the Internet. Researchers are trying to find the optimum solution to
prevent these web attack activities. There are several techniques available to prevent the web attacks from
happening like firewalls, but most of the firewall is not designed to prevent the attack against the websites.
Moreover, firewalls mostly work on signature-based detection method. In this paper, we have analyzed
different anomaly-based detection methods for the detection of web-based attacks initiated by malicious
users. Working of these methods is in a different direction to the signature-based detection method which
only detects the web-based attacks for which a signature has been previously created. In this paper, we
have introduced two methods: Attribute Length Method (ALM) and Attribute Character Distribution
Method (ACDM) which is based on attribute values. Further, we have done the mathematical analysis of
three different web attacks and compare their False Accept Rate (FAR) results for both the methods.
Results analysis reveals that ALM is more efficient method than ACDM in the detection of web-based
attacks.
KEYWORDS
Web-Based Attacks, Anomaly Based Detection, Attribute Length Method, Attribute Character Distribution
Method, False Accept Rate.
1. INTRODUCTION
Web traffic is increasing day by day and, therefore, there are large numbers of attacks occurring
on the websites. In the present World Wide Web traffic scenarios, where everything is connected
to the Internet and everyone in the industry succeeds for their success on the strong presence in
the online market. The attack on websites is on the increase whether it has defaced the image of
the competitor or to hack website just for fun. But ultimately it is the loss of the owner of the
website. Attacks on business related websites are more threatening than on personal websites
because business websites contain all the financial information about the industry, employee
details and lot more things. Web applications are becoming more and more professional and
business oriented in all sorts of areas ranging from social media to the online shopping world. The
result of such rapid increase in the popularity of the website brings both positive and negative
sides to the picture. With the increase in traffic revenue of the business attackers also comes to
know about the site and they like to attack sites with heavy traffic, therefore, the majority of the
visitors are affected. The effects of the attacks are not only felt during the attack but also they
leave after attack effects also like identity disclosure, hijacking of sensitive data information and
unauthorized access to the information. Therefore, apart from running the website it is necessary
and important task to protect web applications and adopt suitable security methods.

14
One of the most promising solutions proposed for detecting web-based attacks is the use of an
anomaly-based intrusion detection system (IDS). In this paper, we have considered various
anomaly detection methods like “Attribute Length” and “Attribute Character Distribution”
proposed in [1]. The anomaly-based detection method is preferable over the signature detection
method because in the later detection of attacks depends only on the database of earlier occurred
attacks. If there is a new attack with new payload and functionality then the signature method will
not be able to detect the attack. Anomaly-based web attack detection method is a method for
detecting web attacks and malicious activities by monitoring system activities and classifying the
activities as either normal or malicious. The classification is based on rules that are generated by
working in the normal training dataset and the setting of the threshold value which is used in the
comparison process. This method works in the different direction to the signature-based detection
method which only detect the web-based attacks for which a signature has been previously
created. However, the major problem in anomaly-based approaches is that they tend to suffer
from a high rate of false alerts [2, 3].
The rest of the paper is organized as follows: Section 2 Related Works carried out by researchers
in web attack detection. Section 3 gives a brief overview of the different types of web attack on
web applications. In the section, 4 descriptions of anomaly-based web attack detection methods
are given in detail. Section 5 explains the proposed work. Experimental work and result analysis
of the proposed work is carried out in section 6. Finally, section 6 concludes this paper.
2. RELATED WORK
Bolzoni, Etalle and Hartel in [5] proposed a novel Anomaly-based NIDS system known as
POSEIDON. In this system the researchers modified original PAYL method and used
unsupervised classification technique for preprocessing task. In article [6] Shyu, Chen et.al used
robust principal component to classify Intrusion Detection problems. This modified approach is
better from its original method in its ability to differentiate the anomalies from its normal
instances in tens of tremendous values of various correlation structures.
In the past couple of years researchers are working very hard to find an optimum solution for the
detection of web based attacks. In the effort Kruegel et.al in [7] proposed an Intrusion Detection
System (IDS) based on Anomaly Detection techniques for the detection and prevention of web
attacks. In [8] Noble and Cook used graph-based statistics techniques for the detection of unusual
patterns. The authors used the concept of conditional entropy to calculate the regularity of the
graph leading to the detection of successful anomaly detection. The method is useful for ruling
out the anomalies in the set of web data.
Maxion and Tan in [9] have proposed a metric for the characterization formation in data
environments and found that fundamental structure greatly affect the probabilistic discovery. In
[10] Tapiador, Teodoro, and Verdejo used HTTP request monitoring to propose new approach
based on Markovian model for the detection of web attacks. Gomez and Dasgupta in [11] created
a fuzzy system using Genetic Algorithm that is able to detect anomalies and intrusions. The
authors in this approach design a classification system based on fuzzy logic to divide normal
request from malicious.
Tapiadoret. al in [12] used Markov chains model to propose a new solution for the identification
of attacks carried out over HTTP traffic. Mehta and Jamwal in [13] used QualysGuard WAS tool
to minimize the web vulnerabilities. In the paper authors focused on Cross Site Scripting (XSS)
code which is proven to be the main source of web based attacks. In the article they have shown

15
that QualysGuard WAS tool is the best solution to satisfy client and server side security
requirements.
In [20], Patil et.al have proposed an effective concept using ID3 algorithm for detection of web
based attacks, using data set from „SmarSniff‟ tool. In the experimental results shown by the
author shows that improved algorithm is effective in decrease the data amount and reduce the
impact of data with poor quality.Snigdha et.al in [21] have implemented the Attribute Length of
Web Attack Detection method using Java Programming language. After training the model they
have tested the solution on real time data and from the results shown that attribute length method
is most suited in detecting web based attacks where parameters are fixed size tokens.
In [22] the authors have presented a framework for generating synthetic datasets with normal and
malicious data for web applications across multiple layers simultaneously. For the experimental
work they build a prototype data generator using to generate nine datasets with data logged on
four layers: network, ﬁle accesses, system calls, and database simultaneously and tested 19
security controls.In [23] the authors have proposed a fraud detection system that uses different
anomaly detection techniques to predict computer intrusion attacks in e-commerce web
applications.Han et. al in article [24] used Request Length Module using Regex Pattern Analysis
for the detection of three types of attacks namely SQl injection, XSS and Directory Traversal
Attack.
3. WEB BASED ATTACKS
An attack is defined as the unwanted intrusion to website resources. Attackers can attack the
website for different reasons and purposes. If the intention of the attacker is just to monitor the
traffic and information flowing in and out the web server then it is termed as “Passive Attack”.
When the attacker tries to modify or destroy the web resources then it is termed as “Active
Attack”. There are many solutions available to counter these attacks such as firewall etc. But
these solutions are not always enough to protect the users from being attacked. As a result users
are vulnerable to exploitations while performing basic functionalities (e.g., login) [4, 5]. In this
paper, we have restricted our research work to three well-known vulnerabilities explained in the
Open Web Application Security Project (OWASP) [14]. For experimental work, we have taken
three different types of the active attack i.e. Cross Site Scripting Attack (XSS), Buffer Overflow
Attack, and Path Traversal Attack. These are described below in brief.
3.1. Cross Site Scripting Attack (XSS)
Cross Site Scripting (XSS) attack refers to a range of attacks in which the attacker injects
malicious code mostly JavaScript into a web application [15, 16]. According to [17] more than
60% of websites are vulnerable to XSS attacks. The XSS attack can take on many forms like for
example execution of JavaScript commands on the web server by passing of script in place on the
parameter value in the URL. Here, we have used different XSS commands from OWASP for the
experimental work on attribute length and attribute character distribution method. A simple
example of XSS attack is shown below:
""" (" ") / "img script alert XSS script     
3.2. Buffer Overflow Attack (BOA)
In this attack, the attacker tries to exploit the very common vulnerability in the web server of not
validating the input properly. A buffer overflow occurs during program execution when a fixed-

16
size buffer has had too many data copied into it. This causes the data to overwrite into adjacent
memory locations and depending on what is stored in it. The behaviour of the program itself
might be affected [18]. Execution of this attack can have many consequences, but the most one is
non-availability of the web application. In this work, we have done the padding of the training
data set parameter value to increase the length so that we can test out both anomaly-based
detection methods. A simple example of BOA is shown below:
: / / / . ?http localhost Test php
yourname achinachinachi
nachaicnainciancainininivr
ivnrivnrivrnvirnvivnivrnvir
nvirnvrvnrvirnvrivnrivnriv

3.3. Path Traversal Attack (PTA)
PTA exploits the vulnerability which is related to the web server path directory. The intention of
using attack is to access the files and directories that are not meant to be accessed by unauthorized
persons. This attack works on applications that take user input and use it in a "path" that is used to
access a file system [19]. Modus Operandi of this particular attack is to insert special characters in
the URL to try to modify the meaning of the path, the result can be misbehaving of the
application and may allow accessing the private resource to the attacker. „.. /‟ Character is the
most basic special sequence that is used in PTA to alter the location of the request. A simple
example of PTA is shown below:
../../../ /item etc passwd
4. TECHNICAL APPROACH OF ANOMALY BASED DETECTION METHODS
Anomaly-based web attack detection method is a method for detection web attacks and malicious
activity by monitoring system activity and categorizing the activity as either normal or malicious.
The Categorization is based on rules that are generated by working in the normal training dataset
and the setting of the threshold value which is used in the comparison process. The anomaly
detection process uses a number of different models to identify anomalous entries within a set of
input request Ur associated with a program r. All the three attacks discussed above are analyzed
with attribute length and attribute character distribution method proposed in [1].
4.1. Attribute Length Method (ALM)
This method is based on the fact that the length of the attribute in the query string can be used to
detect malicious request. This method is most effective in cases where the parameter is set as
fixed-size tokens. In normal cases when the parameter value is passed for a certain case like
username (which we have used here for evaluation) does not deviate much. However, in case of
malicious input attackers tends to increase the parameter value. For example in BOA attackers
increase the length by padding with additional characters. The other two attacks XSS and PTA
which we have evaluated in this work can also be detected by the ALM.

17
4.2 Attribute Character Distribution Method
In this method frequency distribution of the characters is analyzed to calculate the Ideal Character
Distribution (ICD). This approach is based on the observation that attributes have regular
structure are mostly human readable but in case of attack that send large data (BOA), different
character distribution is obtained due to the padding of the extra characters. This method can also
be used to detect the XSS and PTA as we have carried out in the experimental work.
5. PROBLEM DEFINITION
This work mainly focuses on the analytical evaluation of three above discussed web-based
attacks, i.e. XSS, BOA and PTA attacks. In this work, we have evaluated the detection of web-
based attacks using “Attribute Length” and “Attribute Character Distribution” anomaly detection
techniques. We have divided the entire work in two different phases: the training phase and
testing phase. In the training phase which helps us in finding out the threshold value which will
be used as measurement to detect malicious and normal query. Kruegel and Vigna utilize
different models in [1] to detect the web attacks in HTTP request that contain a query section.
Also, we have computed the probability value for both the methods in the training phase and then
investigate the probability value in case of malicious parameter for the testing phase. By
analyzing the deviation, we have carried out the detection process of web-based attacks. We will
describe both the phases in details that we have done for our experimental work.
5.1. Attribute Length Method (ALM): Training Phase Analysis
We have captured the length of the parameter (username) for each HTTP request that is passed
during the training phase. Compute the mean length by adding all the length and divided by the
total number of parameters then we have calculated the threshold probability of using the
equation given below:
2
2
σ
µ( )
P
L


Where L (L≥µ) is the length of the string parameter and µ is the mean length of all the string
parameters during the training phase and σ2 is the variance calculated for all the inputs. The
experimental values are shown in Table 1.
Table 1. Parameters values
Parameters Values
Variance(σ2) 11.7313
Mean Length(µ) 8.68966
Length(L) of Parameter
‟Username‟
Vary in different query input
Threshold Probability 0.79
Threshold probability (Pth) is 0.79 for the rest of the work in Attribute Length Method.

18
5.2 Attribute Length Method (ALM): Testing Phase
After computing Pth, we have followed the same process for three different web attacks discussed
above. Probability values for the above mentioned attacks are shown in Table 2.
Table 2: Probability Value (Malicious Data)
S.
No.
Threshold
Probability
Value
Probability
value in BOA
Probability
value in XSS
Probability
value in PTA
1 0.79 0.631 0.008 0.035
2 0.79 0.416 0.015 0.416
3 0.79 0.295 0.020 0.039
4 0.79 0.219 0.022 0.170
5 0.79 0.170 0.012 0.219
6 0.79 0.135 0.005 0.631
7 0.79 0.110 0.014 0.057
8 0.79 0.092 0.015 0.031
9 0.79 0.077 0.022 0.950
10 0.79 0.066 0.024 0.950
11 0.79 0.057 0.950 0.039
12 0.79 0.050 0.631 0.950
13 0.79 0.044 0.950 0.950
14 0.79 0.039 0.018 0.295
15 0.79 0.035 0.017 0.631
16 0.79 0.031 0.016 0.024
17 0.79 0.028 0.009 0.018
18 0.79 0.026 0.009 0.135
19 0.79 0.024 0.010 0.219
20 0.79 0.022 0.008 0.018
21 0.79 0.020 0.950 0.017
22 0.79 0.018 0.219 0.015
23 0.79 0.017 0.110 0.014
24 0.79 0.016 0.077 0.066
25 0.79 0.015 0.050 0.031
5.3 ALM: Result Analysis
In this section, we have compared the observed probability values with the threshold probability
value (Pth) experimentally for testing phase as shown in the fig. 1. The threshold probability
value is shown with a blue line in the graph and the value that lies behind this trend line are the
values in web-based attacks that are detected and the value above blue line are the attacks that are
not detected by the attribute length method.

19
Fig. 1: Threshold probability value and web-based attacks probability value
The False Accept Rate (FAR) for ALM is calculated using the equation given in the box and
results are shown in the Table 3.
Numberof maliciousqueryaccepted
FAR
Total numberof queries

Table 3: FAR calculation in ALM
Variables Values
Total Number of Queries 75
No. of Malicious Queries
accepted
7
FAR 9.3%
From above table 3, it is clear that only 9.3% web-based attacks are not successfully detected in
the area above the red line in the graph. It means that attribute length method successfully
detected 90.7% web attack in the area below the red line as shown in the graph.
5.4 Attribute Character Distribution Method (ACDM): Training Phase Analysis
In this work, we have introduced the training model for ACDM. In this model each HTTP request
that is passed during the training phase. We have computed the character distribution of each
alphabet from A-Z. However, in real situation all the characters including special character may
be considered but for this work we have considered only the alphabets for constructing the

20
training data set. After finding out the distribution of each character we have divided them into
six different bins that are shown in the table 4, below.
Table 4: Distribution of characters in six bins
Bin Generated Characters in the
Bin
Bin1 {A, Z, X, W}
Bin2 {I, Q, P, G}
Bin3 {N, B, F, J}
Bin4 {H, M, S, D}
Bin5 {R, T, V, Y, C}
Bin6 {E, O, L, K, U}
After dividing the characters into six bins, we have computed the threshold probability value
(Pth) for training data using the equation given below.
2
2
( )i i
i
O E
X
E



Where, Oi is the observed frequency of characters of same bin and Ei is the expected frequency
of the bin which is calculated by the formula given below.
 .iE L x F D bin
Where, L is the length of the query parameter and F. D (bin) is the frequency distribution of the
bin in which the character lies. The probability is calculated using the chi square table with degree
of freedom as 5. Threshold probability Pth computed in the training phase is 0.041.
5.5 ACDM: Testing Phase Analysis
After computing the Pth, we have carried out the same work for three web-based attacks that are
discussed above. Probability values for different malicious parameter values in BOA, XSS and
PTA are shown in table 5.
Table 5. Threshold probability values against probability in attacks for malicious parameter value
S. No. Threshold
Probability Value
Probability in
BOA attack
Probability in XSS
attack
Probability in
PTA attack
1 0.040 0.052 0.044 0.047
2 0.040 0.016 0.012 0.010
3 0.040 0.020 0.018 0.035
4 0.040 0.040 0.040 0.022
5 0.040 0.019 0.020 0.035
6 0.040 0.050 0.056 0.040
7 0.040 0.004 0.002 0.026
8 0.040 0.016 0.014 0.046
9 0.040 0.039 0.032 0.027

21
10 0.040 0.002 0.001 0.068
11 0.040 0.016 0.013 0.013
12 0.040 0.025 0.036 0.029
13 0.040 0.054 0.032 0.017
14 0.040 0.035 0.004 0.038
5.6 ACDM: Results Analysis
As shown in the fig. 2, the threshold probability value (Pth) can be compared with observed
probability values computed during testing phase. In the figure, threshold probability value is
shown with a red line and the area that lies below this trend line are web-based attacks that are
detected and values above the red line are the attacks that are not detected by the ACDM.
The FAR for ACDM can be calculated using the equation given below and results are shown in
the Table 6.
Numberof maliciousqueryaccepted
FAR
Total numberof queries

Fig. 2: Threshold probability values against attacks probability values
Table 6: FAR calculation in ACDM
Variables Values
Total number of queries 42
Number of malicious queries
accepted
11
FAR 26.1%

22
As FAR shown in the table for ACDM, it is clear that only 26.1% web-based attacks are not
successfully detected the area above the red line in the figure. It means, we can say that the
ACDM successfully detected 73.9% web attacks the area below the red line in the figure.
5.7Comparison between the ALM and ACDM
In this paper, we have analyzed the anomaly based web-based attack detection methods for three
different attacks BOA, XSS and PTA. In fig. 3, the FAR for both the methods ALM and ACDM
are shown for each of the three web attacks.
The FAR comparison for all the three attacks has shown in fig. 3. As shown in the figure, ALM is
best suited for the detection of all three web attacks that are considered in this work. In case of
BOA, ALM has successfully detected all the malicious queries whereas FAR for ACDM is 28%.
For PTA, FAR is 16% for ALM and FAR is 28% for ACDM. It means ALM is better than
ACDM to detect the malicious queries. Similarly in case of XSS attack, FAR is 12% for ALM
and 21% for ACDM. Therefore, after analysis of all these web attacks, we can say that ALM is
more efficient method than ACDM in detection of web based attacks.
Fig. 3: ALM and ACDM FAR Comparison
5. CONCLUSION
In this paper we have analytically compared the two anomaly detection methods i.e. ALM and
ACDM. We have done the mathematical analysis of both the methods on three different web
attacks i.e. BOA, XSS and PTA. Also we have compared their False Accept Rate (FAR) results
for both the methods. Experimental results shows that both the methods are capable of detecting
web based attack very efficiently as in both the methods the FAR is less than 30% and further
results analysis revels that ALM is more efficient method than ACDM in detection of web based
attacks.

23
REFERENCES
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24
AUTHORS
Achin Jain, working as Assistant Professor in BharatiVidyapeeth College of Engineering,
New Delhi (Affiliated to Guru Gobind Singh Indraprastha University, Delhi) in the
Information Technology Department. He received his M.Tech(Computer Science and
Technology) &B.Tech(Information Technology) from the Guru Gobind Singh Indraprastha
University, Delhi. He has rich experience teaching B.Tech students and has published more
than 6 Research Papers in International Journals and Conferences. His area of interest
includes Web Usage Mining, Web Attacks.
Vanita Jain received her Ph.D. degree in Electrical Engineering from V.J.T.I.,Mumbai,
India , M.Tech. degree in Control Systems from National Institute of Technology,
Kurukshetra and B.E. degreen from Punjab Engineering College, Chandigarh, India. From
1998 to 1999, she worked at National Institute of Technology, Kurukshetra as a lecturer.
She joined ThadomalShahaniEngineeringCollege, Bandra, Mumbai and served in the
Department of Electronics & Telecommunication Engineering for 15 years. Currently, she
is Dean (Academics) at BharatiVidyapeeth‟s College of Engineering, New Delhi. She is having more than
24 years of teaching experience. Her field of interest includes Soft Computing, Control, Optimization
Techniques and System Engineering and is actively involved in the research in these areas.

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