Phishing Websites Classification using Association Classification (PWCAC)

被引:5
|
作者
Alqahtani, Mohammed [1 ]
机构
[1] Imam Abdulrahman Bin Faisal Univ, Coll Comp Sci & Informat Technol, Dept Comp Informat Syst, POB 1982, Dammam, Saudi Arabia
关键词
Phishing; Web-Security; Rule Induction; Association Classification; Data Mining; SYSTEM;
D O I
10.1109/iccisci.2019.8716444
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Phishing is one of the cybercrime methods which seems to be increasing steadily targeting unsuspecting users. Nowadays, the impact of phishing extended to include organizations that provide services via the Internet. Such organizations become more susceptible to lose their reputation and their competitive edges because of phishing. Classifying a website into a phishing or legitimate depends mainly on the status of a set of significant attributes that exist in the website. Various solutions have been developed to mitigate phishing attacks. Yet, there is no solution that was able to solve the problem completely. One of the encouraging methods that can be utilized is the data mining. Specifically, the "induction of classification rules". In this article, a novel association classification algorithm is suggested and applied to the well-known phishing websites dataset in the UCI repository. The experimental results were promising with respect to several evaluation criteria that are commonly utilised in evaluating classification data mining domains.
引用
收藏
页码:112 / 117
页数:6
相关论文
共 50 条
  • [21] Adversarial classification using signaling games with an application to phishing detection
    Figueroa, Nicolas
    L'Huillier, Gaston
    Weber, Richard
    DATA MINING AND KNOWLEDGE DISCOVERY, 2017, 31 (01) : 92 - 133
  • [22] Adversarial classification using signaling games with an application to phishing detection
    Nicolas Figueroa
    Gastón L’Huillier
    Richard Weber
    Data Mining and Knowledge Discovery, 2017, 31 : 92 - 133
  • [23] 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
  • [24] Webpages Classification with Phishing Content Using Naive Bayes Algorithm
    Rodriguez Rodriguez, Jorge Enrique
    Medina Garcia, Victor Hugo
    Perez Castillo, Nelson
    KNOWLEDGE MANAGEMENT IN ORGANIZATIONS, KMO 2019, 2019, 1027 : 249 - 258
  • [25] 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,
  • [26] Detection of Phishing Websites Using Machine Learning
    Abbas, Ahmed Raad
    Singh, Sukhvir
    Kau, Mandeep
    INVENTIVE COMMUNICATION AND COMPUTATIONAL TECHNOLOGIES, ICICCT 2019, 2020, 89 : 1307 - 1314
  • [27] Phishing Websites Detection using Machine Learning
    Kulkarni, Arun
    Brown, Leonard L., III
    INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS, 2019, 10 (07) : 8 - 13
  • [28] Phishing interrupted: The impact of task interruptions on phishing email classification
    Slifkin, Elisabeth J. D.
    Neider, Mark B.
    INTERNATIONAL JOURNAL OF HUMAN-COMPUTER STUDIES, 2023, 174
  • [29] 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
  • [30] 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