Phishing Website Detection Paradigm using XGBoost

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume 9 Issue 12, Dec 2022 www.irjet.net p-ISSN: 2395-0072
© 2022, IRJET | Impact Factor value: 7.529 | ISO 9001:2008 Certified Journal | Page 1364
Phishing Website Detection Paradigm using XGBoost
Sumera Anjum1, T. Uma Devi2, K.K. Namish3, B. Vasundhara Devi4
1,2,3Student, Dept. of Computer Science and Engineering, Sreenidhi Institute of Science and Technology
4 Assistant Professor, Dept. of Computer Science and Engineering, Sreenidhi Institute of Science and Technology,
Telangana, India
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Abstract - One of the largest and most potent cyber hazards
today is phishing, which costs thousands of millions of dollars
in damages resulting from data breaches that happen every
year. Due to the frequent change andshortlifespanofphishing
websites, several pattern recognition approaches have been
explored and developed to address phishing attacks, but none
of them are effective in detecting web phishing activities.
Among the most pragmatic ways to solvethischallengeis with
machine learning since it can attain statistics and handle the
changing nature of online fraud. In this project, we illustrate
using an ensemble machine learning technique, the Extreme
Gradient Boosting (XGBoost) Algorithm, to detect malicious
URLs with high precision and efficacy using the Uniform
Resource Locators. In XGBoost, the target variable yi is
predicted using training data xi repeatedly until the model's
parameters are improved by merging the trees and boosting.
As determined by the confusion matrix createdbytheXGBoost
model's performance, it accurately predicted 7393 positive
terms and 7930 negative terms with the set of features
identified from the Kaggle dataset. Its merits encompass
substantial regularisation capabilities thatreduceoverfitting,
great speed and performance since trees are created in
parallel, and flexibility because of costume optimization.
Key Words: Phishing, Website, XGBoost, ensemble,
Extreme Gradient Boosting, Uniform Resource Locator
1. INTRODUCTION
Upsurge in web users, phishing threats have grown to be a
serious problem. More than 80% of security incidents that
have been reported entail phishing attacks. These phishing
portals are cyber snoopers attempting to gather data
covertly by coercing usersintodivulgingprivateinformation
like their passwords and credit card details. Attackers
generally employ spoofing to lure consumers to malicious
websites by mimicking the names and designs of trusted
websites like Myntra, Flipkart, Amazon, and Zomato. Hence,
it is challenging for the common person to tell them apart
from legitimate websites.A UniformResourceLocator(URL)
incorporates different components, including the protocol,
domain name, port, path, query, etc. A phishing website's
URL may be differentiated from authentic ones by using a
few specific characteristics. Although, it may not be always
reliable to classify a website simply by looking at the URL.
Phishers have employed a variety of sophisticatedstrategies
to trick unsuspecting consumers, including the usage of
social engineering techniques and technology to offer
carefully designed URLs that lead users to believe that
websites are trustworthy. There are several approaches to
combat phishing, including technological, educational, and
legal means, and numerous research on the subject have
been conducted. A credible and plausible solution must be
provided to avoid jeopardizing the users' privacy. Since the
methodologies from machine learning can identify possible
threats by learning provided data and building predictive
models, it is a viable field to handle the problem in this case.
Single models that effectively process the training data and
produce substantially accurate predictions are most
commonly implemented. The algorithm predominantly is a
collection of Decision Trees, which are used by ensemble
machine learning approaches to train several categorization
models [4]. The final resultisgeneratedthrougha combining
method, such as voting (majority wins), weighted voting
(certain classifiers have more authority than others), and
averaging the results, as each constituentlearningalgorithm
will have its own separate output [4].
2. LITERATURE REVIEW
In this section, we have articulated several well-known
examples because extensive study and research have been
done on phishing detection. For detecting attacks, there are
several methods anda broadrange of data types in academic
researchandcommercialservices.URL-based,domain-based,
page-based, and content-based features gathered from
academic research for phishing domain identification
through machine learning approaches [10]. Traditional
machinelearning techniqueslikeNaveBayes,SupportVector
Machine, and Decision Tree were used in the majority of the
research on the topic. Software called "Anti Phishing
Simulator" was devisedatFiratUniversitytomakeiteasierto
identify phishing and spam emails by looking at the email
content [3]. As encouraged by Cisco, fog computing makes
use of features such as uniform resource locator (URL) and
internet activity to identify phishing websites based on a
designed neuro-fuzzy framework (dubbed Fi-NFN), and an
anti-phishing model was created to transparently monitor
and defend fog users from phishing attacks [1]. To some
extent, approaches based on visual resemblance can identify
phishing websites. The majority of web information is not
consistent, though, and when a web page's characteristics
change, the approach encounters a detection problem.
Blacklisting techniques are the basic and most commonly

used strategies in the business to stop phishing assaults [6].
Checking whether the URL of the matched website is on the
blacklist is one of the phishing detection techniques used by
Google Safe Browsing [6]. CANTINA is a content-based
phishing detection method that was proposed by Zhangetal.
The first five phrases based on TF-DF are forwarded to the
search unit for comparison with the results returned by the
search unit utilizing linkable links in the authors' approach
for identifying phishing websites [5].
3. PROPOSED SYSTEM
In this part, the working of the proposed solution is
explained in which the data collection and its processing is
the first step. The processed data is then used to train the
model with an ensemble algorithm. Testing data is used to
test the accuracy and precision of the model developed
which is displayed through the confusion matrix. In the end,
the user can enter any URL to classify it as a phishing
website or a legitimate website which is generatedasoutput
by the XGBoost paradigm.
3.1 DATASET PRE-PROCESSING
The dataset which is used in this project is obtained from
Kaggle. Kaggle provides the public dataset consisting of
71677 unique values. This data is fetched from google's
whois API which tells us more about the current status of the
URL's registration [2]. The first step following decidingonan
algorithm is data collection,oftenknownastherequirements
stage. Despite, the fact that this step isonlybeginning,itisthe
most important and time-consuming. Because the module's
main objective is to learn about and apply cutting-edge
technology, this section pays particular focus to this
component of the project. From four primary categories, 17
factors are taken out and incorporated into the system. The
features are extracted andstoredintheCSVfile.Theresulting
CSV file is uploaded to this notebook and stored in the data
frame.
3.2 MODEL DEVELOPMENT
It's appropriate to construct the model when the essential
data has been obtained and examined. The development of
the model's architecture, the creation of orderly yet secure
codes, and model training comprise the design portion of the
project as it is now being presented. Python is being
leveraged throughout the project, thus important libraries
that are mostly used for data science are imported, and the
scripts are either created from scratch or drawn from the
web. Extreme Gradient Boost, often known as XGBoost, is a
machine learning technique that employs extreme gradient
boosting and is based on Decision Trees. The gradient
boosting method was improved by integrating parallel
processing, tree pruning, missing value handling, and
normalization to get rid of errors and inaccuracies [11]. It's a
lethal combination of hardware and softwaremetaheuristics
that uses the least amount of processing resources while
achieving bettersignificantresults.Thefundamentalpurpose
of this work is to establish certain dataset parameters that
the model will use in the future to determine whether a URL
is genuineor not. Here, eachparametertransformsintoatree
and increases the deciding factor [11]. Although these trees
might not perform as well as anticipated, by merging these
trees and boosting them, the prediction might noticeably
enhance. In XGBoost, the target variable yi is predicted using
training data xi repeatedly until the model's parameters are
improved.
3.3 PHISHING WEBSITE DETECTION
The developed paradigm is saved and tested for accuracy
with the testing data. This paradigm can be used in real-time
to classify the URLs into legitimate or phishing, given by the
user as input.
4. RESULTS
The output screenshots display the userinputsclassification
and the confusion matrix shows the performance of the
XGBoost phishing website identification model.
Fig -1: Phishing website detection output 1
Fig -2: Phishing website detection output 2

Fig -3: Confusion matrix of the developed XGBoost model
Fig -4: Precision graph for developed XGBoost model
5. CONCLUSIONS
Today there are more uncontrolled websites than ever
before due to a mammoth increase in internet users.
Phishing variegates over time since fraudulent websites are
often updated and do not last forever. With the use of the
Ensemble Algorithm XGBoost and a feature set well
stipulated, phishing detection using website URLs is
predicted to generate highly accurate results with a
reasonable bias-variance trade-off in a robust and efficient
manner. According to the above models'assertions,XGBoost
Classifier has the finest model performance at 86.4%. The
Python pickle module has been used to retain this model as
the regression design and demonstrates how reliable and
accurate the model is at intercepting web phishing.
REFERENCES
[1] Chuan Pham, Luong A. T. Nguyenz, Nguyen H. Tran, Eui-
Nam Huh, Choong Seon Hong, “Phishing-Aware: A
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