A Novel Machine Learning Approach to Detect Phishing Websites

被引:0
|
作者
Tyagi, Ishant [1 ]
Shad, Jatin [1 ]
Sharma, Shubham [1 ]
Gaur, Siddharth [1 ]
Kaur, Gagandeep [1 ]
机构
[1] Jaypee Inst Informat Technol, Dept CSE&IT, Noida, India
关键词
phishing; R; machine learning algorithms;
D O I
暂无
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Phishing can be described as a way by which someone may try to steal some personal and important information like login id's, passwords, and details of credit/debit cards, for wrong reasons, by appearing as a trusted body. Many websites, which look perfectly legitimate to us, can be phishing and could well be the reason for various online frauds. These phishing websites may try to obtain our important information through many ways, for example: phone calls, messages, and pop up windows. So, the need of the hour is to secure information that is sent online and one concrete way of doing so is by countering these phishing attacks. This paper is focused on various Machine Learning algorithms aimed at predicting whether a website is phishing or legitimate. Machine learning solutions are able to detect zero hour phishing attacks and they are better at handling new types of phishing attacks, so they are preferred. In our implementation, we managed an accuracy of 98.4% in prediction a website to be phishing or legitimate.
引用
收藏
页码:425 / 430
页数:6
相关论文
共 50 条
  • [1] An Approach to Detect Phishing Websites with Features Selection Method and Ensemble Learning
    Khatun, Mahmuda
    Mozumder, Md Akib Ikbal
    Polash, Md. Nazmul Hasan
    Hasan, Md Rakib
    Ahammad, Khalil
    Shaiham, Md Shibly
    INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS, 2022, 13 (08) : 768 - 775
  • [2] Intelligent analysis to detect phishing websites using machine learning ensemble techniques
    Mithilesh Kumar Pandey
    Rekha Pal
    Saurabh Pal
    Alok Kumar
    Arvind Kumar Shukla
    Dhyan Chandra Yadav
    Human-Intelligent Systems Integration, 2024, 6 (1) : 39 - 47
  • [3] Detection and Prevention of Phishing Websites using Machine Learning Approach
    Patil, Vaibhav
    Thakkar, Pritesh
    Shah, Chirag
    Bhat, Tushar
    Godse, S. P.
    2018 FOURTH INTERNATIONAL CONFERENCE ON COMPUTING COMMUNICATION CONTROL AND AUTOMATION (ICCUBEA), 2018,
  • [4] A Novel Approach to Detect Phishing Attacks using Binary Visualisation and Machine Learning
    Barlow, Luke
    Bendiab, Gueltoum
    Shiaeles, Stavros
    Savage, Nick
    2020 IEEE WORLD CONGRESS ON SERVICES (SERVICES), 2020, : 177 - 182
  • [5] A deep learning approach to detect phishing websites using CNN for privacy protection
    Zaimi, Rania
    Hafidi, Mohamed
    Lamia, Mahnane
    INTELLIGENT DECISION TECHNOLOGIES-NETHERLANDS, 2023, 17 (03): : 713 - 728
  • [6] A Feature Extraction Approach for the Detection of Phishing Websites Using Machine Learning
    Gundla, Sri Charan
    Karthik, M. Praveen
    Reddy, Middi Jashwanth Kumar
    Gourav
    Pankaj, Ashutosh
    Stamenkovic, Z.
    Raja, S. P.
    JOURNAL OF CIRCUITS SYSTEMS AND COMPUTERS, 2024, 33 (02)
  • [7] Prediction of phishing websites using machine learning
    Pandey, Mithilesh Kumar
    Singh, Munindra Kumar
    Pal, Saurabh
    Tiwari, B. B.
    SPATIAL INFORMATION RESEARCH, 2023, 31 (02) : 157 - 166
  • [8] Detecting Phishing Websites Using Machine Learning
    Alswailem, Amani
    Alabdullah, Bashayr
    Alrumayh, Norah
    Alsedrani, Aram
    2019 2ND INTERNATIONAL CONFERENCE ON COMPUTER APPLICATIONS & INFORMATION SECURITY (ICCAIS), 2019,
  • [9] Detection of phishing websites using machine learning
    Razaque, Abdul
    Frej, Mohamed Ben Haj
    Sabyrov, Dauren
    Shaikhyn, Aidana
    Amsaad, Fathi
    Oun, Ahmed
    Proceedings - 2020 IEEE Cloud Summit, Cloud Summit 2020, 2020, : 103 - 107
  • [10] Detection of Phishing Websites using Machine Learning
    Razaque, Abdul
    Frej, Mohamed Ben Haj
    Sabyrov, Dauren
    Shaikhyn, Aidana
    Amsaad, Fathi
    Oun, Ahmed
    2020 IEEE CLOUD SUMMIT, 2020, : 103 - 107