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Phishing Websites, Detection and Analysis: A Survey

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Contemporary Issues in Communication, Cloud and Big Data Analytics

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 281))

  • 796 Accesses

Abstract

Phishing is the despicable utilization of electronic interchanges to trick clients. Phishing assaults resolve to increase delicate data like usernames, passwords, MasterCard information, network qualifications, and the sky is the limit from there. Phishing assaults endeavor to increase touchy, secret data, for example, usernames, passwords, charge card data, network qualifications, and then some. Phishing Websites copy the first sites so clients believe that they are utilizing the first sites. On account of phishing assaults, each individuals and associations are at threat. Phishing assaults might be forestalled by identifying the sites and serving to clients to detect the phishing sites. To distinguish the phishing sites, there have been various strategies applied. Diverse machine learning methods, information mining procedures, neural organization and different calculations have been utilized for anticipating or ordering or distinguishing the phishing sites. This paper aims at surveying on recently proposed phishing detection techniques.

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References

  1. Feng, F., Zhou, Q., Shen, Z., Yang, X., Han, L., Wang, J.Q.: The Application of a Novel Neural Network in the Detection of Phishing Websites (2018)

    Google Scholar 

  2. Kaytan, M., Hanbay, D.: Effective Classification of Phishing Web Pages Based on New Rules by Using Extreme Learning Machines 2017.

    Google Scholar 

  3. Al-Sariera, Y.A., Adeyemo, V.E., Balogunand, A.O., Alazzawi, A.K.: AI Meta-Learners and Extra-Trees Algorithm for the Detection of Phishing Websites, 2017

    Google Scholar 

  4. Alseriera, Y.A., Elijah, A.V., Balogun, A.O.: Phishing Website Detection: Forest by Penalizing Attributes Algorithm and Its Enhanced Variations, 2020

    Google Scholar 

  5. Abutair, H.Y.A., Belghith, A.: Use Case Based Reasoning for Phishing Detection, 2017

    Google Scholar 

  6. Priya, A., Meenakshi, E.: Detection of Phishing Websites Using C4.5 Data Mining Algorithm, 2017

    Google Scholar 

  7. Kumar, M.S., Indrani, B.: Brain Storm Optimization based Association Rule Mining Model for Intelligent Phishing URLs Websites Detection, 2020

    Google Scholar 

  8. Riaty, S., Sharieh, A., Bdour, H.A., Jabri, R.: Enhance Detecting Phishing Websites Based on Machine Learning Techniques of Fuzzy Logic with Associative Rules, 2017

    Google Scholar 

  9. Babagoli, M., Aghababa, M.P., Solouk, V.: Heuristic Nonlinear Regression Strategy for Detecting Phishing Websites, 2018

    Google Scholar 

  10. Adewole, K.S., Akintola1, A.G., Salihu, S.A., Faruk, N., Jimoh, R.G.: Hybrid Rule-Based Model for Phishing URLs Detection, 2019

    Google Scholar 

  11. Ubing, A.A., Jasmi, S.K.B., Abdullah, A., Jhanjhi, N.Z., Supramaniam, M.: Phishing Website Detection: An Improved Accuracy through Feature Selection and Ensemble Learning, 2019

    Google Scholar 

  12. Sarhan, A.A., Jabri, R., Sharieh, A.: Website Phishing Detection Using Dom-Tree Structure and Cant-MinerPB Algorithm, 2017

    Google Scholar 

  13. Patil, V., Thakkar, P., Shah, C., Bhat, T., Godse, S.P.: Detection and Prevention of Phishing Websites using Machine Learning Approach

    Google Scholar 

  14. Robic–Butez, P., Win, T.Y.: Detection of Phishing Websites Using Generative Adversarial Network, 2019

    Google Scholar 

  15. Parekh, S., Parikh, D.: A New Method for Detection of Phishing Websites: URL Detection, 2018

    Google Scholar 

  16. Alnajim, A., Munro, M.: An Anti-Phishing Approach that Uses Training Intervention for Phishing Websites Detection, 2019

    Google Scholar 

  17. Dhanalakshmi, R., Prabhu, C., Chellapan, C.: Detection of Phishing Websites and Secure Transactions, 2018

    Google Scholar 

  18. Buber, E., Demir, Ö., Sahingoz, O.K.: Feature Selections for the Machine Learning Based Detection of Phishing Websites, 2018

    Google Scholar 

  19. Suleman, M.T., Awan, S.M.: Optimization of URL-Based Phishing Websites Detection Through Genetic Algorithms, 2019

    Google Scholar 

  20. Somesha, M., Pais, A.R., Rao, R.S., Rathour, V.S.: Efficient Deep Learning Techniques for the Detection of Phishing Websites, 2020

    Google Scholar 

  21. James, D.: An Innovative Framework for the Detection and Prediction of Phishing Websites, 2018

    Google Scholar 

  22. Kahksha, S.N.: Detection of Phishing Websites using Machine Learning Approach, 2019

    Google Scholar 

  23. James, J., Sandhya, L., Thomas, C.: Phishing Website Detection based on Supervised Machine Learning with Wrapper Features Selection, 2019

    Google Scholar 

  24. Tan, C.L., Chiew, K.L., Wong, K., Sze, A.: Enhanced Blacklist Method to Detect Phishing Websites, 2016

    Google Scholar 

  25. Chiew, K.L., Tan, C.L., Wong, K., Yong, K.S.C., Tiong, W.K.: Particle Swarm Optimization-Based Feature Weighting for Improving Intelligent Phishing Website Detection, 2019

    Google Scholar 

  26. Zhang, W., Jiang, Q., Chen, L., Li, C.: A Machine Learning Based Approach for Phishing Detection Using Hyperlinks Information, 2017

    Google Scholar 

  27. Lakshmi, V.S., Vijaya, M.S.: Detection of Phishing Websites Based on Probabilistic Neural Networks and K-Medoids Clustering, 2017

    Google Scholar 

  28. Zhuang, W., Ye, Y., Li, T., Jiang, Q.: An Intelligent Anti-phishing Strategy Model for Phishing Website Detection, 2017

    Google Scholar 

  29. Aburrous, M., Hossain, M.A., Dahal, K., Thabtah, F.: A New Method for Detection of Phishing Websites URL Detection, 2016

    Google Scholar 

  30. Chang, E.H., Chiew, K.L., Sze, S.N., Tiong, W.K.: Phishing Detection via Identification of Website Identity, 2018

    Google Scholar 

  31. Sharifi, M., Siadati: Phishing Websites Detection through Supervised Learning Networks, 2018

    Google Scholar 

  32. Konradt, C., Schilling, A., Werners, B.: Phishing: an economic analysis of cybercrime perpetrators. Comput. Secur. 58, 39–46 (2016)

    Article  Google Scholar 

  33. Jabri, R., Ibrahim, B.: Phishing Websites Detection Using Data Mining Classification. Trans. Mach. Learn. Artif. Intell. 3(4) (2015)

    Google Scholar 

  34. Ali, W., Malebary, S.: Particle Swarm Optimization-Based Feature Weighting for Improving Intelligent Phishing Website Detection, 2020

    Google Scholar 

  35. Singh, C., Meenu, S.: Phishing Website Detection Based on Machine Learning, 2020

    Google Scholar 

  36. Kelkar, R.A., Vijayalakshmi, A.: ML Based Model for Phishing Website Detection, 2020

    Google Scholar 

  37. Jain, A.K., Gupta, B.B.: Phishing Detection: Analysis of Visual Similarity Based Approaches, 2017

    Google Scholar 

  38. Abbasi, A., Zhang, Z., Zimbra, D., Chen, H., Nunamaker, J.F.: Detecting fake websites: the contribution of statistical learning theory. Mis Q. 435–461 (2010)

    Google Scholar 

  39. Das, R., Hossain, M.M., Islam, S., Siddiki, A.: Learning a Deep Neural Network for Predicting Phishing Website, 2019

    Google Scholar 

  40. Sampat, H., Saharkar, M., Pandey, A., Lopes, H.: Detection of Phishing Website Using Machine Learning, 2018

    Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Information Science and Engineering, SDM College of Engineering and Technology, Dharwad, India

    Leena I. Sakri, Pushpalatha S. Nikkam, Madhuri Kulkarni, Priyanka Kamath, Shreedevi Subrahmanya Bhat & Swati Kamat

Authors
  1. Leena I. Sakri
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  2. Pushpalatha S. Nikkam
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  3. Madhuri Kulkarni
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  4. Priyanka Kamath
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  5. Shreedevi Subrahmanya Bhat
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  6. Swati Kamat
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Editor information

Editors and Affiliations

  1. Department of Information Technology, Sikkim Manipal Institute of Technology, Majitar, Sikkim, India

    Hiren Kumar Deva Sarma

  2. Department of Automatics and Applied Software, Aurel Vlaicu University of Arad, Arad, Romania

    Valentina Emilia Balas

  3. Department of Information Technology, Sikkim Manipal Institute of Technology, Majitar, Sikkim, India

    Bhaskar Bhuyan

  4. Department of Computer Science and Engineering, Marwadi University, Rajkot, Gujarat, India

    Nitul Dutta

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Sakri, L.I., Nikkam, P.S., Kulkarni, M., Kamath, P., Bhat, S.S., Kamat, S. (2022). Phishing Websites, Detection and Analysis: A Survey. In: Sarma, H.K.D., Balas, V.E., Bhuyan, B., Dutta, N. (eds) Contemporary Issues in Communication, Cloud and Big Data Analytics. Lecture Notes in Networks and Systems, vol 281. Springer, Singapore. https://doi.org/10.1007/978-981-16-4244-9_2

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  • DOI: https://doi.org/10.1007/978-981-16-4244-9_2

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-16-4243-2

  • Online ISBN: 978-981-16-4244-9

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