Phishing website detection based on effective machine learning approach. Issue 1 (2nd January 2021)

• Record Type: Journal Article
• Authors: Harinahalli Lokesh, Gururaj; BoreGowda, Goutham
• Is Part Of: Journal of cyber security technology. Volume 5:Issue 1(2021)
• Page Start: 1
• Page End: 14
• Publication Date: 2021-01-02
• Subjects: Machine learning -- phishing -- legitimate -- random forest classification; Computer security -- Periodicals; Data encryption (Computer science) -- Periodicals; 005.805
• DOI: 10.1080/23742917.2020.1813396
• Languages: English
• ISSNs: 2374-2917

Abstract

ABSTRACT: Phishing a form of cyber-attack, which has an adverse effect on people where the user is directed to fake websites and duped to reveal their sensitive and personal information which includes passwords of accounts, bank details, atm pin-card details etc. Hence protecting sensitive information from malwares or web phishing is difficult. Machine learning is a study of data analysis and scientific study of algorithms, which has shown results in recent times in opposing phishing pages when distinguished with visualization, legal solutions, including awareness workshops and classic anti-phishing approaches. This paper examines the applicability of ML techniques in identifying phishing attacks and report their positives and negatives. In specific, there are many ML algorithms that have been explored to declare the appropriate choice that serve as anti-phishing tools. We have designed a Phishing Classification system which extracts features that are meant to defeat common phishing detection approaches. We also make use of numeric representation along with the comparative study of classical machine learning techniques like Random Forest, K nearest neighbours, Decision Tree, Linear SVC classifier, One class SVM classifier and wrapper-based features selection which contains the metadata of URLs and use the information to determine if a website is legitimate or not.