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Spam Email Detection Using Machine Learning and Neural Networks

  • Conference paper
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Sentimental Analysis and Deep Learning

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1408))

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Abstract

Spam emails are junk emails which are unrequested deceptive emails sent or forwarded to any person or a company which may contain malware and has access to confidential information of any individual. A lot of research work has been done in this area of spam detection which is limited to some specific domains. Machine learning is generally used to classify whether an email is valid (ham) or unwanted (spam). Two feature sets are introduced namely stopwords and word count to determine an email is spam or ham on the basis of textual information and fields of an email file. The entire process involves the comparison of two different feature sets on Multinomial Naïve Bayes, Logistic Regression, Linear Support Vector Machine, and Artificial Neural Network Algorithms to determine a more reliable method for spam detection. For this purpose, we use benchmark datasets as well as real time evaluation to experimentally evaluate the proposed work. Detection of a spam email on basis of content, malware, and sender’s information can reduce the threat to user’s confidential information to a great extent.

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References

  1. Mohammed, M. A., Mostafa, S. A., & Obaid, O. I. An anti-spam detection model for emails of multi-natural language.

    Google Scholar 

  2. Mallampati, D., & Hegde, N. P. (2020). A machine learning based email spam classification framework model. IJITEE, ISSN, 9(4), 2278–3075.

    Google Scholar 

  3. Cormack, G. V. (2006). Email spam filtering: A systematic review. Foundations and Trends® in Information Retrieval, 1(4), 335–455.

    Google Scholar 

  4. Chen, J. I. Z., & Smys, S. (2020). Social multimedia security and suspicious activity detection in SDN using hybrid deep learning technique. Journal of Information Technology, 2(02), 108–115.

    Google Scholar 

  5. Siponen, M., & Stucke, C. (2006). Effective anti-spam strategies in companies: An international study. In Proceedings of the 39th Annual Hawaii international conference on system sciences (HICSS’06).

    Google Scholar 

  6. Mallampati, D., Chandra Shekar, K., & Ravikanth, K. Supervised machine learning classifier for email spam filtering, © Springer Nature Singapore Pte Ltd. 2019 and Engineering. https://doi.org/10.1007/978-981-13-7082-341.

  7. Gupta, H., Jamal, M. S., Madisetty, S., & Desarkar, M. S. (2018, January). A framework for real-time spam detection in Twitter. In 2018 10th international conference on communication systems & networks (COMSNETS) (pp. 380–383).

    Google Scholar 

  8. Mahmoud, T. M., & Mahfouz, A. M. (2012). SMS spam filtering technique based on artificial immune system. International Journal of Computer Science Issues (IJCSI), 9(2), 589.

    Google Scholar 

  9. Akinyelu, A. A., & Adewumi, A. O. (2014). Classification of phishing email using random forest machine learning technique. Journal of Applied Mathematics.

    Google Scholar 

  10. Yüksel, A. S., Cankaya, S. F., & Üncü, İ. S. (2017). Design of a machine learning based predictive analytics system for spam problem. Acta Physica Polonica, A., 132(3); Goodman, J. (2004, July). IP Addresses in Email Clients. CEAS.

    Google Scholar 

  11. Androutsopoulos, J. Koutsias, K. Chandrinos and C. D. Spyropoulos, “An experimental comparison of naive Bayesian and keyword-based anti-spam filtering with personal email messages,” Computation and Language, pp. 160–167, 2000.

    Google Scholar 

  12. Huang, L., Jia, J., Ingram, E., & Peng, W. Enhancing the naive bayes spam filter through intelligent text modification detection. In 2018 17th IEEE international conference on trust, security and privacy in computing and communications.

    Google Scholar 

  13. Apache. (2019). “open-source Apache SpamAssassin Dataset”, https://spamassassin.apache.org/old/publiccorpus/

  14. Vinodhini, M., Prithvi, D., Balaji, S. (2020, March). Spam detection framework using ML algorithm. IJRTE, 8(6). ISSN: 2277-3878.

    Google Scholar 

  15. Brownlee, J. (2016, April 1). Logistic regression for machine learning. The Machine Learning Mastery. https://machinelearningmastery.com/logistic-regression-for-machine-learning/

  16. Zavvar, M., Rezaei, M., & Garavand, S. (2016) Email spam detection using combination of particle swarm optimization and artificial neural network and support vector machine. International Journal of Model Education and Computer Science 68–74.

    Google Scholar 

  17. Gandhi, R. (2018, June 7). Support vector machine. The Machine Learning Mastery. https://towardsdatascience.com/support-vector-machine-introduction-to-machine-learning-algorithms-934a444fca47

  18. Smys, S., Basar, A., & Wang, H. (2020). Artificial neural network based power management for smart street lighting systems. Journal of Artificial Intelligence, 2(01), 42–52.

    Google Scholar 

  19. Li, X. M., & Kim, U. M. (2012, June). A hierarchical framework for content-based image spam filtering. In 8th international conference on information science and digital content technology (ICIDT) (pp. 149–155). Jeju.

    Google Scholar 

  20. Mukherjee, A., Venkataraman, V., Liu, B., & Glance, N. S. (2013). What yelp fake review filter might be doing? In ICWSM.

    Google Scholar 

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Sethi, M., Chandra, S., Chaudhary, V., Dahiya, Y. (2022). Spam Email Detection Using Machine Learning and Neural Networks. In: Shakya, S., Balas, V.E., Kamolphiwong, S., Du, KL. (eds) Sentimental Analysis and Deep Learning. Advances in Intelligent Systems and Computing, vol 1408. Springer, Singapore. https://doi.org/10.1007/978-981-16-5157-1_22

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