My students project paper

1. 53 Prof.T. Bhaskar, Aher Sonali, Bawake Nikita, Gosavi Akshada, Gunjal Swati
International Journal of Innovations & Advancement in Computer Science
IJIACS
ISSN 2347 – 8616
Volume 4, Issue 9
September 2015
A Survey on Detection of Website Phishing Using MCAC
Technique
*Prof.T.Bhaskar 1
Aher Sonali 2
Bawake Nikita 3
Gosavi Akshada 4
Gunjal Swati
*
Asst .Prof(Computer Engineering)
1,2,3,4
Students of BE Computer
Sanjivani Collage of Engineering,
Kopargaon, Savitribai Phule, Pune University
Abstract
One of the essential security challenges is website
phishing for the online community because of the larger
extends online transactions performed on a daily basis.
To gain important information from online users website
spoofing can be detailed as imitating an original website.
To reduce risk of phishing problem black lists, white lists
and the utilization of search methods can be used. Black
List is one of the popular and widely used search
methods into browsers, but they are less effective and
unclear. MCAC is one of the data mining approach
which used to find phishing websites with large amount
of accuracy. MCAC is a method which is developed by
AC method for detecting the issues of website phishing
and to recognize features that differs phishing websites
from trusted ones. In this paper, MCAC identify
untrusted websites with large amount of accuracy and
MCAC algorithm generates new hidden rules and this
has improved its classifiers performance.
Keywords
Classification, Data mining, websites, Phishing,
Internet security.
1. INTRODUCTION
For individual users and organizations doing
business online internet is essential. Number of the
organizations affords online selling and sales of
services [4]. Phishing is method to mimicking
official or original websites of any organizations
such as banks, institutes social networking websites,
etc. Mainly phishing is done to steal private
credentials of user such as username, passwords,
PIN no or any credit card details [8].
Phishing is an attack that target the weakness found
in system. These weaknesses are used by attacker to
harm system by inserting malicious content in to the
system. Phishing is an activity in which phisher
creates duplicate website of original website called
as website phishing. The phishing activity done by
user is known as phisher. Phishing is attempted by
trained hackers or attackers [2].
Now a day’s phishing attacks are increasing rapidly.
Phishing is an attempt to take victim's sensitive data
such as credit card numbers, usernames and
passwords. The victim's are the users who have
been suffered from the phishing attacks. Phishing
can be done with the help of instant messaging or
emails. Usually the attackers send the victim an
email that look to be from an authenticate
organization. These emails ask the victims to update
their information by providing a link in email. The
phishing websites look exactly similar to the trusted
websites. These phishy websites are made by
untrustworthy person with the intend of financial
damages or loss of personal information [6].
There are the two most popular approaches for
designing solutions for website phishing. Blacklist
approach: In which the entered URL is examined
with already defined phishing URLs. The weakness
of this approach is that the blacklist cannot involve
all phishing websites hence a newly created phishy
website requires a more time before it can be added
to the list. Search approach: The second approach is
based on heuristic methods. In which various
website features are gathered and that are used to
detect the type of the website. In comparison to the
blacklist approach, the heuristic approach can
identify newly created untrusted websites in real-
time.
We examine the issues of website phishing using
a originated AC method called Multi-label
Classifier based Associative Classification
(MCAC). We also want to recognize features that
differentiate phishing websites from legal ones.
MCAC algorithm identifies phishing websites with

2. 54 Prof.T. Bhaskar, Aher Sonali, Bawake Nikita, Gosavi Akshada, Gunjal Swati
International Journal of Innovations & Advancement in Computer Science
IJIACS
ISSN 2347 – 8616
Volume 4, Issue 9
September 2015
large amount of accuracy than other intelligent
algorithms. Further, MCAC produces new hidden
knowledge that other algorithms are not able to
recognize and this has enhance its classifiers
performance.
2. LITERATURE SURVEY
Current problem is website phishing, even though
due to its huge impact on the financial and on-line
retailing sectors and since preventing such attacks is
an important step towards defending against website
phishing attacks, there are several promising
approaches to this problem and a comprehensive
collection of related works[4][6]. Phishing is form
of creating a like legal website and confusing the
users to use their originality or authentication keys
such as online user name, passwords to contain the
control and then cheat the users by unlawful
activities such as clarify data, banking accounts
transfer etc. are mainly phishing is heavily seen in
portals like banking, mails etc. Phishing is a kind of
attack in which criminals use duplicate emails and
fraudulent web sites to dupe people into giving up
personal information. Victims identify these emails
as associated with a trusted brand, while in reality
they are the work of trick artists interested in
identity theft. These increasingly knowledgeable
attacks not only duplicate email and web sites, but
they can also fake parts of a user’s web browser.
One of the extremely important security challenges
for the online community is website phishing due to
the no of online transaction performed on a daily
basis [3].copying a trusted website to get private
information from online users such as usernames
and passwords it describes the website phishing.
Reduce the risk of this problem, black lists, white
lists and the utilization of search method are the
example of solutions. Effectively detect phishing
websites with high accuracy. One intelligent
approach based on data mining called Associative
Classification (AC). Phishing attacks, in which
attacker attract internet users to websites that act
like legitimate sites, are occurring with increasing
oftenness and are causing considerable harm to
victims. This system teaches people about phishing
during their normal use of email. This system
shown that people are vulnerable to phishing for
several reasons. First, people tend to judge websites
legitimacy by its look and feel, which attackers can
easily replicate. Second, many users do not believe
or trust the security indicators in web
browsers[6].AC repeatedly extracts classifiers
containing simple "If-Then" rules with a large
accuracy [1].
We search the problem of website which are
dummy using a developed AC method called Multi-
label Classifier based Associative Classification
(MCAC) to pursue its applicability to the phishing
problem. We also want to verify the features that
differentiate phishing websites from genuine
website. Besides, we analysis intelligent approaches
used to handle the anti-phishing. In addition,
MCAC generates new rules that other algorithms
are not able to find and this has improved its
classifiers predictive execution.
In this section, we analysis common smart phishing
classification approaches from the summary, after
dropping the light on the general steps required to
handle the anti-phishing and its general computing
approaches. The main steps that required to be
handle the anti-phishing are the following:
(1) Verification of the mandatory data: for any
given problem, we required a set of attributes,
which are already predefined. These should have
some impact on the desired output (classifier).
Thus, a set of input and output attributes should be
verified.
(2) Training set development: The training data set
consists of pairs of input or examples and desired
goal attribute (class). There are many inception of
phishing information such as Phish tank.
(3) Determination of the input factor: The classifier
sharpness depends on how the training instance is
described and how factors have been carefully
chosen. The factor chosen process should eliminate
not relevant features as possible in order to reduce
the dimensionality of the training data set so the
learning process can be effectively completed. We
display later the ways we fix the feature before
selecting them.
(4) Applying the classification algorithm: The
selecting of a mining algorithm is a critical step.
There are broad ranges of mining methods available
in the summary where each of these classification
approaches has its own advantages and
disadvantages. There are three main factors in
choosing a classification approach are (a) the input
data components, (b) the classifier predictive power

3. 55 Prof.T. Bhaskar, Aher Sonali, Bawake Nikita, Gosavi Akshada, Gunjal Swati
International Journal of Innovations & Advancement in Computer Science
IJIACS
ISSN 2347 – 8616
Volume 4, Issue 9
September 2015
uniformed by the accuracy rate, and (c) the
clearness and understandable of the output. Overall,
on all given data there is no individual classifier that
gives best performance, and classifier work largely
relies on the training data set components. For this
step, we chosen AC since it has many different
factors particularly the high predictive accuracy and
the understandable of output derived.
(5) Classifier evaluation: The last step is to test the
derived classifier performance on test data [1].
To handle phishing typically, the two most technical
methods in fighting phishing attacks are the
blacklist and the heuristic-based. In the blacklist
method, the entered URL is examined with already
defined phishing URLs. The downside of this
method is that it typically doesn’t deal with all fake
websites since a newly created fake website
requires a large amount of time before being added
to the list. In comparison to the blacklist approach,
the heuristic-based approach can identify newly
created illegal websites in real-time .Drawbacks that
appeared when depending on the above mentioned
solutions requires necessity to innovative solutions.
The favorable outcome of an anti-phishing
technique depends on recognizing illegal websites
and within moderate span of time. Even though a
number of anti-phishing solutions are designed,
most of these solutions were unable to make highly
accurate decisions causing a rise of false positive
decisions, which means labelling a legitimate
website as fake. We focus on technical solutions
proposed by scholars in the literature.
3. PROPOSED SYSTEM
The figure here shows the phishing attack process.
1. Firstly, the phisher creates the fake website which
looks exactly same as the original or the legitimate
website.
2. Then the phisher sends the mail to the victim and
provide a link in the email and asks to enter the
sensitive data such as user name and password to
the victim.
3. The victim enters all the information asked.
4. This information is accessed by the phisher.
5. And finally the phisher attacks the target website.
Fig. 1: Phishing Process
Our phishing detection system is used to detect
website is phishy or not. Phisher mimics a
legitimate website to gain personal information
from users such as usernames, passwords and credit
card number, etc. Our system goal is to detect
phishy website by using MCAC algorithm. The
MCAC algorithm generates rules further that rules
are sorted by using sorting algorithm. By using the
Feature Extraction algorithm we can extract the
features and store in training dataset. That features
are used to find out the website is phishy or not. If
the website is phishy then display warning message
to user.
Fig. 2: Proposed System Flow Diagram
Following steps used to find out phishy websites.
1. Feature Extraction
2 .Generate Classifier (By using MCAC)

4. 56 Prof.T. Bhaskar, Aher Sonali, Bawake Nikita, Gosavi Akshada, Gunjal Swati
International Journal of Innovations & Advancement in Computer Science
IJIACS
ISSN 2347 – 8616
Volume 4, Issue 9
September 2015
3. Comparison (Training Dataset and Testing
Dataset)
3.1 Feature Extraction
Our system extracts the following features for
identifying phishy website.
1. IP address
2. Long URL
3. URL’s having @ symbol
4. Adding prefix and suffix
5. Sub-domains
6. Fake HTTPs protocol/SSL final
7. Request URL
8. URL of anchor
9. Server Form Handler (SFH)
10. Abnormal URL
11. Using Pop-up window
12. Redirect page
13. DNS record
14. Hiding the links
15. Website traffic
16. Age of domain
3.2. Generate Classifier (By using MCAC)
Input: Training data D, minimum confidence
(MinConf) and minimum support (MinSupp)
thresholds.
Output: A classifier
Preprocessing: Discretize continuous attributes if
any
The first step:
 Search training data set T to find the entire
set of repeated attribute values.
 Convert any repeated attribute values which
passes MinConf to a single label rule.
 Combine any 2 or more single lable rule
which have similar body and various classes to
obtain the multilable rules.
The second step:
 Sort the rule arranged based on Confidence
Support and also rule length.
 Create the classifier by testing rules for the
training data and preserving those in classification
method (Cm) which has data coverage.
The third step:
 Classify test data applying rules in
classification method (Cm).
Rule: use of https and trusted issuer and age
>=2
years→Legit
Using https and untrusted issuer
→Suspicious
else→Phishy.
4. CONCLUSION
Phishing websites as well as hackers can be
easily identified using our proposed system. Our
system defines the URL features and tests its
features, depend on that we check the probability of
that features and determines the webpage label and
provide the security. Our MCAC technique helps us
to determine the website is phishy or not.
5. REFERENCES
1. Abdelhamid, N., Ayesh, A., & Thabtah, F. (2013)
Associative classification mining for website
phishing classification. In Proceedings of the ICAI
‘2013 (pp. 687–695), USA.
2. Extraction of Feature Set for Finding Fraud URL
Using ANN Classification in Social Network
Services. iPGCON-2015,SPPU,PUNE.
3. Pallavi D. Dudhe, Prof. P.L. Ramteke, (2015)
Detection of Websites Based on Phishing
Websites Characteristics, International Journal of
Innovative Research in Computer and
Communication Engineering, april 2015.
4. Pallavi D. Dudhe et al, A review on phishing
detection approaches., International Journal of
Computer Science and Mobile Computing,Vol.4
Issue.2, February- 2015, pg. 166-170.
5. Vaibhav V. Satane, Arindam Dasgupta(2013)
Survey Paper on Phishing Detection:
Identification of Malicious URL Using Bayesian
Classification on Social Network Sites,
International Journal of Science and Research
(IJSR) 2013.
6. Sonali Taware, Chaitrali Ghorpade, Payal
Shah,Nilam Lonkar (2015) Phish Detect:
Detection of Phishing Websites based on
Associative Classification (AC), International
Journal of Advanced Research in Computer
Science Engineering and Information Technology,
Volume: 4 Issue: 3 22-Mar-2015,ISSN_NO: 2321-
3337.

5. 57 Prof.T. Bhaskar, Aher Sonali, Bawake Nikita, Gosavi Akshada, Gunjal Swati
International Journal of Innovations & Advancement in Computer Science
IJIACS
ISSN 2347 – 8616
Volume 4, Issue 9
September 2015
7. Komatla. Sasikala, P. Anitha Rani(2012) " An
Enhanced Anti Phishing Approach Based on
Threshold Value Differentiation", International
Journal of Science and Research (IJSR) 2012.
8. Mitesh Dedakia, Khushali Mistry, Phishing
Detection using Content Based Associative
Classification Data Mining Journal of
Engineering Computers & Applied
Sciences(JECAS) ISSN No: 2319-5606 Volume 4,
No.7, July 2015
6. BIOGRAPHIES
T.Bhaskar is currently working as
Asst. Professor in Computer
Engineering Department, Sanjivani
College of Engineering, Kopargaon
and Maharashtra India. His research
interest includes data mining, network
security.
Aher Sonali is pursuing B.E Computer
Engg in SRESCOE, Kopargaon. Her
areas of research interests include
Information Security, Data mining.
Bawake Nikita is pursuing B.E
Computer Engg in SRESCOE,
Kopargaon. Her areas of research
interests include Information Security;
Data Mining.
Gosavi Akshada is pursuing B.E
Computer Engg in SRESCOE,
Kopargaon. Her areas of research
interests include Information Security;
Data Mining.
Gunjal Swati is pursuing B.E
Computer Engg in SRESCOE,
Kopargaon. Her areas of research
interests include Information Security;
Data Mining.