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Detection of Deceptive Phishing Based on Machine Learning Techniques

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Smart Technologies in Data Science and Communication

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 105))

Abstract

Internet-based sources facing security problem because of the most sophisticated phishing attacks on online world. Advanced persistent threat attackers are using targeted emails, phishing websites and social engineering techniques to reach their goals. Deceptive Phishing targets confidential information using social engineering thefts online identity and uses spoofed emails and lure to be forged websites. In this paper we discussed about different classification and filtering methods to protect the cloud. An experimental approach is provided with implementation procedures using machine learning techniques to combat on malicious websites and email spams. We concentrated on different approaches, algorithms, techniques to detect the phishing attacks, and a new model is designed and implemented on the dataset and results are evaluated. The evaluation metrics are implemented on datasets based on different algorithms, and results are tabulated and graphical analysis is done.

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References

  1. F. Ghaffari, H. Gharaee, M.R. Forouzandehdoust, Security considerations and requirements for cloud computing, in 2016 8th International Symposium on Telecommunications (IST), Tehran, 2016, pp. 105–110

    Google Scholar 

  2. J.V. Chandra, N. Challa, S.K. Pasupuleti, A practical approach to E-mail spam filters to protect data from advanced persistent threat, in 2016 International Conference on Circuit, Power and Computing Technologies (ICCPCT), Nagercoil, 2016, pp. 1–5

    Google Scholar 

  3. W. Niu, X. Zhang, G. Yang, Z. Ma, Z. Zhuo, Phishing emails detection using CS-SVM, in 2017 IEEE International Symposium on Parallel and Distributed Processing with Applications and 2017 IEEE International Conference on Ubiquitous Computing and Communications (ISPA/IUCC), Guangzhou, 2017, pp. 1054–1059

    Google Scholar 

  4. J.V. Chandra, N. Challa, S.K. Pasupuleti, Advanced persistent threat defense system using self-destructive mechanism for cloud security, in 2016 IEEE International Conference on Engineering and Technology (ICETECH), Coimbatore, 2016, pp. 7–11

    Google Scholar 

  5. M.D. Ambedkar, N.S. Ambedkar, R.S. Raw, A comprehensive inspection of cross site scripting attack, in 2016 International Conference on Computing, Communication and Automation (ICCCA), Noida, 2016

    Google Scholar 

  6. S. Deepika, P. Pandiaraja, Ensuring CIA triad for user data using collaborative filtering mechanism, in 2013 International Conference on Information Communication and Embedded Systems (ICICES), Chennai, 2013, pp. 925–928

    Google Scholar 

  7. J.V. Chandra, N. Challa, S.K. Pasupuleti, Intelligence based defense system to protect from advanced persistent threat by means of social engineering on social cloud platform. Indian J. Sci. Technol. 8(28) (2015)

    Google Scholar 

  8. N. Abdelhamid, F. Thabtah, H. Abdel-Jaber, Phishing detection: a recent intelligent machine learning comparison based on model content and features, in 2017 IEEE International Conference on Intelligence and Security Informatics (ISI), Beijing, 2017, pp. 72–77

    Google Scholar 

  9. S. Patil, S. Dhage, A methodical overview on phishing detection along with an organized way to construct an anti-phishing framework, in 2019 5th International Conference on Advanced Computing & Communication Systems (ICACCS), Coimbatore, India, 2019, pp. 588–593

    Google Scholar 

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Correspondence to J. Vijaya Chandra .

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© 2020 Springer Nature Singapore Pte Ltd.

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Chandra, J.V., Challa, N., Pasupuleti, S.K. (2020). Detection of Deceptive Phishing Based on Machine Learning Techniques. In: Fiaidhi, J., Bhattacharyya, D., Rao, N. (eds) Smart Technologies in Data Science and Communication. Lecture Notes in Networks and Systems, vol 105. Springer, Singapore. https://doi.org/10.1007/978-981-15-2407-3_2

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  • DOI: https://doi.org/10.1007/978-981-15-2407-3_2

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-2406-6

  • Online ISBN: 978-981-15-2407-3

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