Classification of phishing scam in website using vowpal wabbit algorithm (4)
- 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