Detecting phishing websites: On the effectiveness of users' tips

被引:0
|
作者
Alnajim, Abdullah [1 ]
Munro, Malcolm [1 ]
机构
[1] Durham University, Department of Computer Science, South Road, Durham, DH1 3LE, United Kingdom
来源
关键词
Computer crime;
D O I
暂无
中图分类号
学科分类号
摘要
Phishing attacks have become a serious problem for users of online banking and e-commerce websites. Many anti-Phishing approaches have been proposed to detect and prevent Phishing. One such approach is the anti-Phishing tips published by many governmental and private organizations to help users themselves to detect and prevent the attacks. This paper examines the effectiveness of the most common of the many different tips for detecting Phishing websites. A novel effectiveness criteria is proposed and used to examine each tip and to rank it based on its effectiveness score, thus revealing the most effective tips to enable users to detect Phishing attacks. Thus, proponents of anti-Phishing tips can focus on those tips which are most helpful to users in detecting Phishing attacks.
引用
收藏
页码:276 / 281
相关论文
共 50 条
  • [1] An Evaluation of Users' Tips Effectiveness for Phishing Websites Detection
    Alnajim, Abdullah
    Munro, Malcolm
    2008 THIRD INTERNATIONAL CONFERENCE ON DIGITAL INFORMATION MANAGEMENT, VOLS 1 AND 2, 2008, : 65 - 70
  • [2] 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,
  • [3] PhishZoo: Detecting Phishing Websites By Looking at Them
    Afroz, Sadia
    Greenstadt, Rachel
    FIFTH IEEE INTERNATIONAL CONFERENCE ON SEMANTIC COMPUTING (ICSC 2011), 2011, : 368 - 375
  • [4] DeltaPhish: Detecting Phishing Webpages in Compromised Websites
    Corona, Igino
    Biggio, Battista
    Contini, Matteo
    Piras, Luca
    Corda, Roberto
    Mereu, Mauro
    Mureddu, Guido
    Ariu, Davide
    Roli, Fabio
    COMPUTER SECURITY - ESORICS 2017, PT I, 2018, 10492 : 370 - 388
  • [5] Intelligent Methods for Accurately Detecting Phishing Websites
    Abuzuraiq, Almaha
    Alkasassbeh, Mouhammd
    Almseidin, Mohammad
    2020 11TH INTERNATIONAL CONFERENCE ON INFORMATION AND COMMUNICATION SYSTEMS (ICICS), 2020, : 085 - 090
  • [6] Heuristic nonlinear regression strategy for detecting phishing websites
    Mehdi Babagoli
    Mohammad Pourmahmood Aghababa
    Vahid Solouk
    Soft Computing, 2019, 23 : 4315 - 4327
  • [7] Detecting phishing websites using machine learning technique
    Dutta, Ashit Kumar
    PLOS ONE, 2021, 16 (10):
  • [8] Heuristic nonlinear regression strategy for detecting phishing websites
    Babagoli, Mehdi
    Aghababa, Mohammad Pourmahmood
    Solouk, Vahid
    SOFT COMPUTING, 2019, 23 (12) : 4315 - 4327
  • [9] Detecting Phishing Websites through Deep Reinforcement Learning
    Chatterjee, Moitrayee
    Namin, Akbar Siami
    2019 IEEE 43RD ANNUAL COMPUTER SOFTWARE AND APPLICATIONS CONFERENCE (COMPSAC), VOL 2, 2019, : 227 - 232
  • [10] A Model of Detecting Phishing Websites Based on PHA and Web Noise
    Cui, Jing-shi
    Wang, Zi-jian
    Wang, Bai-ling
    Wang, Wei
    Xin, Guo-dong
    PROCEEDINGS OF THE 2017 2ND INTERNATIONAL CONFERENCE ON MATERIALS SCIENCE, MACHINERY AND ENERGY ENGINEERING (MSMEE 2017), 2017, 123 : 1084 - 1090