Detection of Phishing Websites using Machine Learning
Karthik G R
, Chaithra G , Jhenkar SK , Chandraprabha K S
Random forest algorithm, machine learning, classification.
There are number of clients who purchase products online and make payment through various websites and also there are multiple websites who ask clients to provide sensitive data such as username, password or credit card details etc. often for malicious reasons. This type of websites is familiar as phishing website. So, to disclose and predict phishing website, we suggested a quick, flexible and effective system that is based on classification Random forest algorithm. We utilize the Random forest algorithm and techniques to extract the phishing data sets criteria to classify their legitimacy. The phishing website can be detected based on some important characteristics i.e URL, Domain Identity, Security and encryption criteria in the final phishing detection rate. Once user makes transaction through online when he makes payment through the website our system will use Random forest algorithm to detect whether the website is phishing website or not. This application can be utilized by many E-commerce enterprises in order to make the whole transaction process secure. Random forest algorithm used in this system provides better performance as compared to other traditional classifications algorithms, with the help of this system user can also purchase products online without any hesitation. Administrant can add fraud website URL into system where system could access and scan the fraud website and by using algorithm, it will add new suspicious keywords to database. System make use of machine learning technique to add new keywords into database.
"Detection of Phishing Websites using Machine Learning", IJSDR - International Journal of Scientific Development and Research (www.IJSDR.org), ISSN:2455-2631, Vol.6, Issue 6, page no.328 - 332, June-2021, Available :https://ijsdr.org/papers/IJSDR2106046.pdf
Volume 6
Issue 6,
June-2021
Pages : 328 - 332
Paper Reg. ID: IJSDR_193442
Published Paper Id: IJSDR2106046
Downloads: 000347040
Research Area: Engineering
Country: Tumkur, Karnataka, India
ISSN: 2455-2631 | IMPACT FACTOR: 9.15 Calculated By Google Scholar | ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 9.15 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publisher: IJSDR(IJ Publication) Janvi Wave
