Izzaty Syahira Binti Kamaruddin Universiti Sultan Zainal Abidin

1. The attackers can steal a sensitive information and use it for dangerous
purposes. It can happen when the user click the malicious link and immediately
install the malware inside the user’s device.
Attackers usually use official logos from real organizations and other
identifying information by taken directly from legitimate Web sites including a
deceptive URL address linking to a scam web site.
With regard this matter, this research intends to leverage Random Forest,
Logistic Regression and Support Vector Machine algorithm to secure website
from phishing scams.
To study about Random Forest, Logistic Regression and Support Vector
Machine in order to secure websites in phishing scam.
To modify Random Forest, Logistic Regression and Support Vector Machine to
suit with datasets of phishing scam in website.
To test the data sets in Kaggle in order to detect phishing websites using
Random Forest, Logistic Regression and Support Vector Machine.
ABSTRACT
Phishing is a kind of attack which is attackers use spoofed email and
fraudulent web sites to trick people without their notice. Phishing websites
looks very similar in appearance to its corresponding legitimate website to
deceive users into believing that they are browsing in the correct website. The
attackers send a malicious links or attachments through phishing emails that
can perform various functions, including steal the login credentials or account
information of the victim. It can harm victims through of money loss and
identify theft. This paper main goal is to investigate the potential of Random
Forest, Logistic Regression and Support Vector Machine in order to protect
users from being hacked or deceived with stealing the personal access
and information.
PROBLEM STATEMENT
OBJECTIVE
FRAMEWORK
Kaggle machine learning is a public data platform that can research and analysis
data in more effectively.
Used Python language in Kaggle machine learning in order to classify phishing
website.
The classification technique in RF, SVM and Logistic Regression have.been used
to classify phishing and legitimate website on the dataset provided.
Ajlouni, M. I. A., Hadi, W. E., & Alwedyan, J. (2013). Detecting phishing websites using
associative classification. image, 5(23),36-40.
Nivedha, S., Gokulan, S., Karthik, C., Gopinath, R., & Gowshik, R. (2017). Improving
Phishing URL Detection Using Fuzzy Association Mining. The International Journal of
Engineering and Science (IJES), 6.
Salem, O., Hossain, A., & Kamala, M. (2010, June). Awareness program and ai based tool to
reduce risk of phishing attacks. In 2010 10th IEEE International Conference on
Computer and Information Technology (pp. 1418-1423). IEEE.
RESULT ANALYSIS
CONCLUSION
In conclude, the machine learning algorithms are fully functioning and produce the
better accuracy for classifying of phishing scam in website whereas the better
accuracy is Random Forest algorithm. It shows this algorithm is very suitable
to classify the data especially in phishing website dataset.
CONTRIBUTION
REFERENCE
CLASSIFICATION OF PHISHING SCAM IN WEBSITE
USING RANDOM FOREST, LOGISTIC REGRESSION &
SUPPORT VECTOR MACHINE
IZZATY SYAHIRA BINTI KAMARUDDIN
BTBL17046423
SIR AHMAD FAISAL AMRI BIN ABIDIN @
BHARUN
BACHELOR OF COMPUTER SCIENCE
(COMPUTER NETWORK SECURITY) WITH
HONOR