Mendeley
TY  _  JOUR
ID  -  202004208705
TI  -  An Improved Method for Detecting Phishing Websites Using Data Mining on Web Pages
JO  -  Journal of Information and Communication Technology
JA  -  ES
LA  -  fa
SN  -  2717-0411
AU  -  baharloo mahdiye
AU  -  Yari Alireza
AD  -  دانشجوی کارشناسی ارشد دانشگاه علوم تحقیقات 
AD  -  
Y1  -  2020
PY  -  2020
VL  -  43
IS  -  
SP  -  27
EP  -  38
KW  -  Phishing
KW  -   Data Mining
KW  -   Feature Selection and Feature Extraction
DO  -  
N2  -  Phishing plays a negative role in reducing the trust among the users in the business network based on the E-commerce framework. therefore, in this research, we tried to detect phishing websites using data mining. The detection of the outstanding features of phishing is regarded as one of the important prerequisites in designing an accurate detection system. Therefore, in order to detect phishing features, a list of 30 features suggested by phishing websites was first prepared. Then, a two-stage feature reduction method based on feature selection and extraction were proposed to enhance the efficiency of phishing detection systems, which was able to reduce the number of features significantly. Finally, the performance of decision tree J48, random forest, naïve Bayes methods were evaluated{cke_protected_1}{cke_protected_2}{cke_protected_3}{cke_protected_4} on the reduced features. The results indicated that accuracy of the model created to determine the phishing websites by using the two-stage feature reduction based Wrapper and Principal Component Analysis (PCA) algorithm in the random forest method of 96.58%, which is a desirable outcome compared to other methods.
UR  - rimag.ir/en/Article/8705
L1  - rimag.ir/en/Article/Download/8705
ER  -  
