This is a computer science and engineering department's project presentation slide.

1. Fake Website Detection using Machine
Learning Algorithms
PresentedBy
MD SAJADUL ISLAM (193002138)
MST. NUSRAT JAHAN JYOTI (192002022)
1
SupervisedBy
MD. SOLAIMAN MIA
Assistant Professor
(Green University of Bangladesh)
GreenUniversityof Bangladesh
Department of CSE
1
Co-Supervised By
MD. GULZAR HUSSAIN
Lecturer, GUB

2. Contents
 Introduction
 Motivation
 Objectives
 Literature Review
 Problem Description
 Draft Plan
 Gantt Chart
 Proposed Budget Components
 Conclusion
 References
2

3. Introduction
 Phishing attacks are increasing every year by
200%
 Fake Website is the most common way to
conduct a phishing attack
 Multinational companies are losing 100
billion dollars per year because of phishing
attack
3

4. Motivation
4
 Desire to save internet users from the phishing attack
 Reduce the percentage of phishing attack
 Using URLs to find out Phishing Websites
 To save people’s personal information

5. Objectives
 Detecting fake website URLs using Machine Learning Algorithms
 To train machine learning models on the dataset and predict
phishing websites
 To use the efficient machine learning models
 Maintaining an accuracy rate of more than 90%
5

6. LiteratureReview
 Use the Neural Network to
detect URL
 Use binary visualization
 System can be applied to
Phishing
 And non-phishing website
classification.
 Limited Dataset to
conduct the experiment
 Limited Dataset affect the
prediction and efficiency
of the model
Working Process Usefulness Drawbacks Scopeof Improvement
 A Novel Approach to Detect Phishing Attacks using Binary Visualization and Machine
Learning [1]
 Adding more datasets for
both training and testing
 Using the different
models in making
predictions
6

7. LiteratureReview(Cont.)
 Phishing detection
 Mitigation in emails
and website
 Phishing detection
 And mitigation in
emails/website
 A lot phishing detection
method such as rule base
method, decision tree,
associative classification,
 SVM, NN were listed but
none have been
demonstrated in the
research.
Working Process Usefulness Drawbacks Scopeof Improvement
 Phishing attacks in Qatar: A literature review of the problems and solutions [2]
 Incorporate other
phishing detection
techniques for example
Random Forest, Light
GBM, and XG Boost.
 Improving the accuracy
level
7

8. LiteratureReview(Cont.)
 Use binary visualization
 User training to increase
awareness on phishing
 useful for Phishing
detection
 offensive defense
 No matrices for end-user
evaluation for each
website
 No features for user
involvement
Working Process Usefulness Drawbacks Scopeof Improvement
 Phishing detection: A literature survey[3]
 Adding some user
matrices for end-user
evaluation for each
website
 Increasing the accuracy
level
8

9. LiteratureReview(Cont.)
 Use Support vector
machine, Random forest
and CNN
 Machine learning and
deep learning
 CNN had given more
accuracy than SVM and RF
 Identifying Fake URL
 Accuracy was less for RF
and SVM 67% and 64%
accuracy
 Missing user involvement
Working Process Usefulness Drawbacks Scopeof Improvement
 Phishing Website Detection using Machine Learning Techniques and CNN [4]
 Using Random
Forest Light GBM XG
Boost.
 Expected accuracy of up
to 90%
9

10. LiteratureReview(Cont.)
 Use a Decision tree
and Support Vector
Machine.
 Proposed a deep
learning-based URL
detector. The authors
argued that the method
can produce insights
from URLs.
 Deep learning methods
demand more time to
produce an output. In
addition, it processes
the URL and matches it
with the library to
generate an output.
Working Process Usefulness Drawbacks Scopeof Improvement
 Phishing Website Detection using Machine Learning Techniques and CNN [4]
 Random Forest Light
GBM XG Boost for
producing an output in a
short time
 Increase the accuracy
10

11. ProblemDescription
 Fake URLs Detection
 Dataset Description
 Three Machine Learning
Classifiers
1st stage (URLs Detection) 2nd stage (Dataset)
(Expected)
 Phishing URLs
 Malware URLs
 Create lexical numeric features from input URLs
 Dataset from https://phishtank.org/
 Dataset from Kaggle
 4,28,103 benign or safe URLs
 96,457 defacement URLs
 94,111 phishing URLs
 32,520 malware URLs
11

12. ProblemDescription(cont.)
12
Random Forest Light GBM XG Boost
 Performs well even if
the data contains
null/missing values.
 Lower memory usage.  Effective for large
data sets
 Expected up to 90%
accuracy
 Desiring to get high
efficiency
Do not need
normalized features
 Random Forest performs both regression and classification tasks.
 Light GBM has faster training speed and higher efficiency
 XG Boost has an in-built capability to handle missing values.
 3rd Stage (Three Machine Learning Classifiers)

13. DraftPlan
Dataset
13
 How Phisher Conducts Phishing Attacks by Fake Website

14. DraftPlan (cont.)
Dataset
14

15. DraftPlan (cont.)
Dataset
Collect the URL
Lexical Feature
Extraction
Classifier Performance
Analysis
Create the URL into
lexical numeric
Evaluation using
Machine Learning
Evaluation the
accuracy
15
Random
Forest
Light
GBM
XG Boost

16. 16
GanttChart
SL.
No.
Months
Thesis Activities
Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun
1 Read Existing Papers
2 Finding Limitation
3 Planning
4 Fixed Objectives
5 Optimization Formulation
6 Design Learning Algorithm
7 Data Collection
8 Implementation
9 Comparison
10 Paper Writing & Publication

17. ProposedBudgetComponents
17
SL.
No.
Budget Title No.
of
Unit
Per Unit
Cost
Total Cost
(BDT)
1 Research Supervision 3 6000 18000
2 Data Collection - 12000 12000
3 Access Researching Website - 1200 3000
4 Environment Setup 2 5000 10000
5 Implementation and Testing - 15000 15000
6 Paper Publication Cost 2 7000 14000
7 Others - 4000 4000
Total: 76000 (BDT)
Table 1: Budget Plan for Research

18. Conclusion
18
 Creating a fake website detection system
 Identify a fake website URL with the best accuracy
 In the Future, System can upgrade to automatically Detect the web page and the
compatibility of the Application with the web browser.
 Additional work also can be done by adding some other characteristics to distinguish the
fake web pages from the legitimate web pages

19. References
[1] L. Barlow, G. Bendiab, S. Shiaeles, and N. Savage, “A Novel Approach to Detect Phishing Attacks using Binary
Visualization and Machine Learning,” in Proceedings - 2020 IEEE World Congress on Services, SERVICES 2020, Oct.
2020, pp. 177–182. doi: 10.1109/SERVICES48979.2020.00046.
[2] Y. Al-Hamar, H. Kolivand, and A. Al-Hamar, “Phishing attacks in Qatar: A literature review of the problems and
solutions,” in Proceedings – International Conference on Developments in eSystems Engineering, DeSE, Oct. 2021, vol.
October-2021, pp. 837–842. doi: 10.1109/DeSE.2020.00155.
[3] A. Basit, M. Zafar, A. R. Javed, and Z. Jalil, “A Novel Ensemble Machine Learning Method to Detect Phishing Attack,”
Nov. 2020. doi: 10.1109/INMIC50486.2020.9318210.
[4] Deepa Mary Vargheese, Sreelakshmi N R “Phishing Website Detection using Machine Learning Techniques and
CNN,” International Journal of Engineering Research & Technology (IJERT) ISSN: 2278-0181 Published by, www.ijert.org
ICCIDT - 2022 Conference Proceedings
[5] Gandotra E., Gupta D, “An Efficient Approach for Phishing Detection using Machine Learning”, Algorithms for
Intelligent Systems, Springer, Singapore, 2021, https://doi.org/10.1007/978-981-15-8711-5_ 12.
19

20. THANKYOU
20