A Content-Based Phishing Email Detection Method

被引:7
|
作者
Che, Hongming [1 ]
Liu, Qinyun [1 ]
Zou, Lin [1 ]
Yang, Hongji [1 ]
Zhou, Dongdai [2 ]
Yu, Feng [3 ]
机构
[1] Bath Spa Univ, Ctr Creat Comp, Bath, Avon, England
[2] Northeast Normal Univ, Coll Informat & Software Engineer, Changchun, Jilin, Peoples R China
[3] Changchun Inst Architecture, Sch Elect & Informat Engn, Changchun, Jilin, Peoples R China
关键词
phishing email; social engineering; semantic web; fuzzy control; cybernetics;
D O I
10.1109/QRS-C.2017.75
中图分类号
TP31 [计算机软件];
学科分类号
081202 ; 0835 ;
摘要
Phishing emails have affected users seriously due to the enormous increasing in numbers and exquisite camouflage. Users spend much more effort on distinguishing the email properties, therefore current phishing email detection system demands more creativity and consideration in filtering for users. The proposed research tries to adopt creative computing in detecting phishing emails for users through a combination of computing techniques and social engineering concepts. In order to achieve the proposed target, the fraud type is summarised in social engineering criteria through literature review; a semantic web database is established to extract and store information; a fuzzy logic control algorithm is constructed to allocate email categories. The proposed approach will help users to distinguish the categories of emails, furthermore, to give advice based on different categories allocation. For the purpose of illustrating the approach, a case study will be presented to simulate a phishing email receiving scenario.
引用
收藏
页码:415 / 422
页数:8
相关论文
共 50 条
  • [1] Detection method of phishing email based on persuasion principle
    Li, Xue
    Zhang, Dongmei
    Wu, Bin
    PROCEEDINGS OF 2020 IEEE 4TH INFORMATION TECHNOLOGY, NETWORKING, ELECTRONIC AND AUTOMATION CONTROL CONFERENCE (ITNEC 2020), 2020, : 571 - 574
  • [2] Content-based concept drift detection for Email spam filtering
    Zi Hayat M.
    Basiri J.
    Seyedhossein L.
    Shakery A.
    2010 5th International Symposium on Telecommunications, IST 2010, 2010, : 531 - 536
  • [3] Content-Based Email Classification at Scale
    Early, Kirstin
    O'Hare, Neil
    LuVogt, Christopher
    PROCEEDINGS OF THE 32ND ACM INTERNATIONAL CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT, CIKM 2023, 2023, : 4559 - 4566
  • [4] Phishing Email Detection Based on Hybrid Features
    Yang, Zhuorao
    Qiao, Chen
    Kan, Wanling
    Qiu, Junji
    2018 4TH INTERNATIONAL CONFERENCE ON ENVIRONMENTAL SCIENCE AND MATERIAL APPLICATION, 2019, 252
  • [5] Overconfidence in Phishing Email Detection
    Wang, Jingguo
    Li, Yuan
    Rao, H. Raghav
    JOURNAL OF THE ASSOCIATION FOR INFORMATION SYSTEMS, 2016, 17 (11): : 759 - 783
  • [6] Email Embeddings for Phishing Detection
    Gutierrez, Luis Felipe
    Abri, Faranak
    Armstrong, Miriam
    Namin, Akbar Siami
    Jones, Keith S.
    2020 IEEE INTERNATIONAL CONFERENCE ON BIG DATA (BIG DATA), 2020, : 2087 - 2092
  • [7] LSTM Based Phishing Detection for Big Email Data
    Li, Qi
    Cheng, Mingyu
    Wang, Junfeng
    Sun, Bowen
    IEEE TRANSACTIONS ON BIG DATA, 2022, 8 (01) : 278 - 288
  • [8] Phishing Email Detection based on Named Entity Recognition
    Listik, Vit
    Let, Simon
    Sedivy, Jan
    Hlavac, Vaclav
    PROCEEDINGS OF THE 5TH INTERNATIONAL CONFERENCE ON INFORMATION SYSTEMS SECURITY AND PRIVACY (ICISSP), 2019, : 252 - 256
  • [9] Cue Utilization, Phishing Feature and Phishing Email Detection
    Bayl-Smith, Piers
    Sturman, Daniel
    Wiggins, Mark
    FINANCIAL CRYPTOGRAPHY AND DATA SECURITY, FC 2020, 2020, 12063 : 56 - 70
  • [10] In-depth Evaluation of Content-Based Phishing Detection to Clarify Its Strengths and Limitations
    Komiyama, Koichiro
    Seko, Toshinori
    Ichinose, Yusuke
    Kato, Kei
    Kawano, Kohei
    Yoshiura, Hiroshi
    U- AND E-SERVICE, SCIENCE AND TECHNOLOGY, 2010, 124 : 95 - +