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image of Techniques for Detecting Intelligent Phishing Attempts in An Insecure Socially Engineered Network Environment
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Abstract

Phishing is an illicit attempt to obtain sensitive information from users, such as usernames, passwords, and credit card details. Attackers often use disguised URLs to deceive web users and steal private information. Attackers commonly employ social engineering tactics like email and text messaging to facilitate phishing attacks. Active learning techniques, including neural networks, can help identify and prevent phishing scams, though these methods have certain limitations. This study provides a comprehensive review of various Artificial Neural Network approaches that incorporate decision trees and optimal feature selection techniques to address these challenges. We propose a newly developed feature assessment index that we can combine with an optimal feature selection method. Techniques like decision trees, paired with local search methods, are used to prune unnecessary features, enhancing the effectiveness of phishing detection. The study also examines various social engineering attacks, particularly phishing websites, which often serve as entry points for numerous fraudulent activities.

© 2024 The Author(s). Published by Bentham Science Publishers
© 2024 The Author(s). Published by Bentham Science Publishers. This is an open access article published under CC BY 4.0 https://creativecommons.org/licenses/by/4.0/legalcode.
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/content/journals/eng/10.2174/0118722121325376241024100727
2024-12-09
2025-01-16

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References

  1. Sadiq A. Anwar M. Butt R.A. Masud F. Shahzad M.K. Naseem S. Younas M. A review of phishing attacks and countermeasures for internet of things‐based smart business applications in industry 4.0. Hum. Behav. Emerg. Technol. 2021 3 5 854 864 10.1002/hbe2.301
    [Google Scholar]
  2. Obaid A.J. Ibrahim K.K. Abdulbaqi A.S. Nejrs S.M. An adaptive approach for internet phishing detection based on log data. Period. Eng. Nat. Sci. 2021 9 4 622 631 10.21533/pen.v9i4.2398
    [Google Scholar]
  3. Ibrahim K.K. Obaid A.J. Fraud usage detection in internet users based on log data. Int. J. Nonlin. Anal. Appl. 2021 12 2 2179 2188
    [Google Scholar]
  4. Bragg T. Cognitive systems engineering models applied to cybersecurity. Doctoral dissertation, Rochester Institute of Technology 2021
    [Google Scholar]
  5. Paturi R. Swathi L. Pavithra K.S. Mounika R. Alekhya C. Detection of phishing attacks using visual similarity model. International Conference on Applied Artificial Intelligence and Computing (ICAAIC) Salem, India 09-11 May 2022 1355 1361 10.1109/ICAAIC53929.2022.9793231
    [Google Scholar]
  6. Viji D. Dixit V. Jha V. Phishing website detection and classification. Proceedings of International Conference on Deep Learning, Computing and Intelligence Springer Singapore 2022 401 411
    [Google Scholar]
  7. Aburrous M. Hossain M.A. Dahal K. Thabtah F. Intelligent phishing detection system for E-banking using fuzzy data mining. Expert Syst. Appl. 2010 37 12 7913 7921 10.1016/j.eswa.2010.04.044
    [Google Scholar]
  8. Jeeva S.C. Rajsingh E.B. Intelligent phishing url detection using association rule mining. Human Cent. Comp. Inform. Sci. 2016 6 1 10 10.1186/s13673‑016‑0064‑3
    [Google Scholar]
  9. Ali W. Malebary S. Particle swarm optimization-based feature weighting for improving intelligent phishing website detection. IEEE Access 2020 8 116766 116780 10.1109/ACCESS.2020.3003569
    [Google Scholar]
  10. Subasi A. Molah E. Almkallawi F. Chaudhery T. Intelligent phishing website detection using random forest classifier. International Conference on Electrical and Computing Technologies and Applications (ICECTA) Ras Al Khaimah, United Arab Emirates 21-23 November 2017 1 5 10.1109/ICECTA.2017.8252051
    [Google Scholar]
  11. Makkar A. Kumar N. Sama L. Mishra S. Samdani Y. An intelligent phishing detection scheme using machine learning. Proceedings of the Sixth International Conference on Mathematics and Computing Springer Singapore 2021 151 165 10.1007/978‑981‑15‑8061‑1_13
    [Google Scholar]
  12. Mishra A. Gupta B.B. Intelligent phishing detection system using similarity matching algorithms. Int. J. Inf. Commun. Technol. 2018 12 1/2 51 73 10.1504/IJICT.2018.089022
    [Google Scholar]
  13. Butt M.H.F. Li J.P. Saboor T. Arslan M. Butt M.A.F. Intelligent phishing URL detection: A solution based on deep learning framework. 18th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP) Chengdu, China 17-19 December 2021 434 439 10.1109/ICCWAMTIP53232.2021.9674162
    [Google Scholar]
  14. Kumar M.S. Indrani B. Brain storm optimization based association rule mining model for intelligent phishing URLs websites detection. Fourth International Conference on Computing Methodologies and Communication (ICCMC) Erode, India 11-13 March 2020 640 646 10.1109/ICCMC48092.2020.ICCMC‑000119
    [Google Scholar]
  15. Shaw J. Vincent P.M.D.R. Palaniappan S. Sangaiah A.K. Gopichand G. Intelligent phishing detection system using feature analysis. J. Comput. Theor. Nanosci. 2018 15 8 2533 2538 10.1166/jctn.2018.7493
    [Google Scholar]
  16. Odeh A.M.M.A.R. Alarbi A.B.D.A.L.R.A.O.U.F. Keshta I.S.M.A.I.L. Abdelfattah E.M.A.N. Efficient prediction of phishing websites using multilayer perceptron (MLP). J. Theor. Appl. Inf. Technol. 2020 98 16 3353 3363
    [Google Scholar]
  17. Yi P. Guan Y. Zou F. Yao Y. Wang W. Zhu T. Web phishing detection using a deep learning framework. Wirel. Commun. Mob. Comput. 2018 2018 1 4678746 10.1155/2018/4678746
    [Google Scholar]
  18. Adebowale M.A. Lwin K.T. Hossain M.A. Intelligent phishing detection scheme using deep learning algorithms. J. Enterp. Inf. Manag. 2020 36 3 747 766 10.1108/JEIM‑01‑2020‑0036
    [Google Scholar]
  19. Wei B. Hamad R.A. Yang L. He X. Wang H. Gao B. Woo W.L. A deep-learning-driven light-weight phishing detection sensor. Sensors 2019 19 19 4258 10.3390/s19194258 31575038
    [Google Scholar]
  20. Yao W. Ding Y. Li X. Deep learning for phishing detection. 2018 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Ubiquitous Computing & Communications, Big Data & Cloud Computing, Social Computing & Networking, Sustainable Computing & Communications (ISPA/IUCC/BDCloud/SocialCom/SustainCom). Melbourne, Australia 11-13 Dec 2018 645 650 10.1109/BDCloud.2018.00099
    [Google Scholar]
  21. Amen K. Zohdy M. Mahmoud M. Machine learning for multiple stage phishing URL prediction. International Conference on Computational Science and Computational Intelligence (CSCI) Las Vegas, NV, USA 15-17 December 2021 794 800 10.1109/CSCI54926.2021.00049
    [Google Scholar]
  22. Sahingoz O.K. Buber E. Demir O. Diri B. Machine learning based phishing detection from URLs. Expert Syst. Appl. 2019 117 345 357 10.1016/j.eswa.2018.09.029
    [Google Scholar]
  23. Azeez N. Identifying phishing attacks in communication networks using URL consistency features. Int. J. Electr. Sec. Dig. Foren. 2019 12 2 1 14
    [Google Scholar]
  24. Tehrani G.A.K. Pontell H.N. Phishing evolves: Analyzing the enduring cybercrime. Vict. Offend. 2021 16 3 316 342
    [Google Scholar]
  25. Alkhalil Z. Hewage C. Nawaf L. Khan I. Phishing attacks: A recent comprehensive study and a new anatomy. Front. Comput. Sci. 2021 3 563060 10.3389/fcomp.2021.563060
    [Google Scholar]
  26. Kathrine G.J.W. Praise P.M. Rose A.A. Kalaivani E.C. Variants of phishing attacks and their detection techniques. 3rd International Conference on Trends in Electronics and Informatics (ICOEI) Tirunelveli, India 23-25 April 2019 255 259 10.1109/ICOEI.2019.8862697
    [Google Scholar]
  27. Mughaid A. AlZu’bi S. Hnaif A. Taamneh S. Alnajjar A. Elsoud E.A. An intelligent cyber security phishing detection system using deep learning techniques. Cluster Comput. 2022 25 6 3819 3828 10.1007/s10586‑022‑03604‑4 35602317
    [Google Scholar]
  28. House D. Raja M.K. Phishing: Message appraisal and the exploration of fear and self-confidence. Behav. Inf. Technol. 2020 39 11 1204 1224 10.1080/0144929X.2019.1657180
    [Google Scholar]
  29. Trautman L.J. Hussein M. Opara E.U. Molesky M.J. Rahman S. Posted: No phishing. 8 Emory Corp. Gover. Account. Review 2021 8 1 1 37
    [Google Scholar]
  30. Desolda G. Ferro L.S. Marrella A. Catarci T. Costabile M.F. Human factors in phishing attacks: A systematic literature review. ACM Comput. Surv. 2022 54 8 1 35 10.1145/3469886
    [Google Scholar]
  31. Boasiako K.A. Keefe M.O.C. Data breaches and corporate liquidity management. Eur. Financ. Manag. 2021 27 3 528 551 10.1111/eufm.12289
    [Google Scholar]
  32. Bazzi A. Chafii M. Secure full duplex integrated sensing and communications. IEEE Trans. Inf. Forensics Security 2024 19 2082 2097 10.1109/TIFS.2023.3346696
    [Google Scholar]
  33. Sekban D.M. Yaylacı E.U. Özdemir M.E. Yaylacı M. Tounsi A. Investigating formability behavior of friction stir-welded high-strength shipbuilding steel using experimental, finite element, and artificial neural network methods. J. Mater. Eng. Perform. 2024 6 2 402 412 10.1007/s11665‑024‑09501‑8
    [Google Scholar]
  34. Garg Machine learning models for predicting the compressive strength of concrete containing nano silica. Comput. Concr. 2022 30 1 33 42
    [Google Scholar]
  35. Garg A. Belarbi M-O. Tounsi A. Li L. Singh A. Mukhopadhyay T. Predicting elemental stiffness matrix of fg nanoplates using gaussian process regression based surrogate model in framework of layerwise model. Eng. Anal. Bound. Elem. 2022 143 779 795 10.1016/j.enganabound.2022.08.001
    [Google Scholar]
/content/journals/eng/10.2174/0118722121325376241024100727
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Techniques for Detecting Intelligent Phishing Attempts in An Insecure Socially Engineered Network Environment
Bentham Science Publishers ; https://doi.org/10.2174/0118722121325376241024100727
/content/journals/eng/10.2174/0118722121325376241024100727
/content/journals/eng/10.2174/0118722121325376241024100727
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  • Article Type:     Review Article
Keywords: artificial intelligence ; social engineering attacks ; Phishing attacks ; machine learning

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