Towards Developing a Tool to Detect Phishing URLs: A Machine Learning Approach

被引:14
|
作者
Basnet, Ram B. [1 ]
Doleck, Tenzin [2 ]
机构
[1] Colorado Mesa Univ, Grand Junction, CO 81501 USA
[2] McGill Univ, Montreal, PQ H3A 2T5, Canada
关键词
machine learning; phishing; tools; phishing URLs;
D O I
10.1109/CICT.2015.63
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Despite efforts to curb online fraud, there continues to be a significant proliferation of fraud in the online space. In the same vein, Phishing attacks are a significant and growing problem for users, and carrying out certain actions such as mouse hovering, clicking, etc., on malicious URLs may cause unwary users to unwittingly fall victim to identity theft and problems. In this paper, we propose a methodology that could be used towards developing an anti-phishingURL tool to thwart a phishing attack by either masking the potentially phishing URL or by alerting the user about the potential threat.
引用
收藏
页码:220 / 223
页数:4
相关论文
共 50 条
  • [41] Machine learning approach for phishing website detection : A literature survey
    Patil, Rutuja R.
    Kaur, Gagandeep
    Jain, Himank
    Tiwari, Ayush
    Joshi, Soham
    Rao, Keshav
    Sharma, Amit
    JOURNAL OF DISCRETE MATHEMATICAL SCIENCES & CRYPTOGRAPHY, 2022, 25 (03): : 817 - 827
  • [42] Detection of Phishing in Internet of Things Using Machine Learning Approach
    Naaz, Sameena
    INTERNATIONAL JOURNAL OF DIGITAL CRIME AND FORENSICS, 2021, 13 (02) : 1 - 15
  • [43] 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,
  • [44] Feature Selection Approach for Phishing Detection Based on Machine Learning
    Wei, Yi
    Sekiya, Yuji
    PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON APPLIED CYBER SECURITY (ACS) 2021, 2022, 378 : 61 - 70
  • [45] A Machine Learning Based Approach to Detect Machine Learning Design Patterns
    Pan, Weitao
    Washizaki, Hironori
    Yoshioka, Nobukazu
    Fukazawa, Yoshiaki
    Khomh, Foutse
    Gueheneuc, Yann-Gael
    PROCEEDINGS OF THE 2023 30TH ASIA-PACIFIC SOFTWARE ENGINEERING CONFERENCE, APSEC 2023, 2023, : 574 - 578
  • [46] 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
  • [47] Advanced Learning for Phishing URLs Detection to Secure Consumer-Centric Applications
    Roy P.K.
    Kumar A.
    Singh A.
    IEEE Transactions on Consumer Electronics, 2024, 70 (03) : 1 - 1
  • [48] Classification of Malicious URLs Using Machine Learning
    Abad, Shayan
    Gholamy, Hassan
    Aslani, Mohammad
    SENSORS, 2023, 23 (18)
  • [49] Detection of malicious URLs using machine learning
    Reyes-Dorta, Nuria
    Caballero-Gil, Pino
    Rosa-Remedios, Carlos
    WIRELESS NETWORKS, 2024, 30 (9) : 7543 - 7560
  • [50] Towards Developing a Learning Tool for Children with Autism
    Zaki, Tarannum
    Islam, Muhammad Nazrul
    Uddin, Md Sami
    Tumpa, Sanjida Nasreen
    Hossain, Md Jubair
    Anti, Maksuda Rahman
    Hasan, Md Mahedi
    2017 6TH INTERNATIONAL CONFERENCE ON INFORMATICS, ELECTRONICS AND VISION & 2017 7TH INTERNATIONAL SYMPOSIUM IN COMPUTATIONAL MEDICAL AND HEALTH TECHNOLOGY (ICIEV-ISCMHT), 2017,