Open AccessA Survey on Phishing Detection and The Importance of Feature Selection In Data Mining Classification Algorithms
Publication year - 2020
Publication title -
journal of science and technolgy
Language(s) - English
Resource type - Journals
ISSN - 2456-5660
DOI - 10.46243/jst.2020.v5.i6.pp11-18
Subject(s) - phishing , c4.5 algorithm , naive bayes classifier , feature selection , computer science , random forest , decision tree , support vector machine , statistical classification , the internet , data mining , feature (linguistics) , machine learning , feature extraction , internet security , artificial intelligence , computer security , information security , world wide web , linguistics , philosophy , security service
: In this era of Internet, the issue of security of information is at its peak. One of the main threats in thiscyber world is phishing attacks which is an email or website fraud method that targets the genuine webpage or anemail and hacks it without the consent of the end user. There are various techniques which help to classify whetherthe website or an email is legitimate or fake. The major contributors in the process of detection of these phishingfrauds include the classification algorithms, feature selection techniques or dataset preparation methods and thefeature extraction that plays an important role in detection as well as in prevention of these attacks. This SurveyPaper studies the effect of all these contributors and the approaches that are applied in the study conducted on therecent papers. Some of the classification algorithms that are implemented includes Decision tree, Random Forest ,Support Vector Machines, Logistic Regression , Lazy K Star, Naive Bayes and J48 etc.