Abstract
The problem of spam emails is a widespread issue that creates a lot of inconvenience for individuals and organizations. According to statistics, approximately 84% of emails received on a daily basis are recognized as spam. This paper aims to present a solution to this problem by proposing the use of a neural network capable of identifying and classifying potential spam emails. The neural network was developed using Python, TensorFlow, Keras, Google Colaboratory, and Jupyter. These tools were chosen because they are widely used and well-suited for the task of creating a deep learning model. The results of the network were found to be satisfactory, with an accuracy rate of approximately 99%. This is comparable to the results achieved by large companies such as Google and Yahoo! who are known to use similar methods to combat spam. Overall, this paper demonstrates that neural networks can be a powerful tool for addressing the problem of spam emails and that the proposed solution has the potential to improve the efficiency and effectiveness of spam filtering for individuals and organizations.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
A Complete Guide to LSTM Architecture and its Use in Text Classification. https://analyticsindiamag.com/a-complete-guide-to-lstm-architecture-and-its-use-in-text-classification/. Accessed 20 Oct 2022
Abraham, A.: Artificial Neural Networks, vol. 17 (2005)
Activation Functions in Neural Networks. https://towardsdatascience.com/activation-functions-neural-networks-1cbd9f8d91d6. Accessed 8 Dec 2022
Average daily spam volume worldwide from October 2020 to September 2021. https://www.statista.com/statistics/1270424/daily-spam-volume-global/. Accessed 16 Oct 2022
Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473 (2014)
Butt, U.A., Amin, R., Aldabbas, H., Mohan, S., Alouffi, B., Ahmadian, A.: Cloud-based email phishing attack using machine and deep learning algorithm. Complex Intell. Syst., 1–28 (2022)
Dada, E.G., Bassi, J.S., Chiroma, H., Abdulhamid, S.M., Adetunmbi, A.O., Ajibuwa, O.E.: Machine learning for email spam filtering: review, approaches and open research problems. Heliyon 5(6), e01802 (2019). https://doi.org/10.1016/j.heliyon.2019.e01802. https://www.sciencedirect.com/science/article/pii/S2405844018353404
Devi, K.K., Kumar, G.: Stochastic gradient boosting model for twitter spam detection. Comput. Syst. Sci. Eng. 41(2), 849–859 (2022)
Dietterich, T.: Overfitting and undercomputing in machine learning. ACM Comput. Surv. 27(3), 326–327 (1995). https://doi.org/10.1145/212094.212114
Email Length Best Practices for Email Marketers and Email Newbies. https://www.campaignmonitor.com/blog/email-marketing/email-length-best-practices-for-email-marketers-and-email-newbies/. Accessed 28 Oct 2022
Gmail Spam Filter: When It Is Not Enough to Stop Spam. https://clean.email/gmail-spam-filter. Accessed 18 Oct 2022
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)
How to Choose an Activation Function for Deep Learning. https://machinelearningmastery.com/choose-an-activation-function-for-deep-learning/. Accessed 19 Nov 2022
Hrinchuk, O., Khrulkov, V., Mirvakhabova, L., Orlova, E., Oseledets, I.: Tensorized embedding layers. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp. 4847–4860 (2020)
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization (2014). https://doi.org/10.48550/ARXIV.1412.6980. https://arxiv.org/abs/1412.6980
Mohamed, I.S.: Detection and tracking of pallets using a laser rangefinder and machine learning techniques. Ph.D. thesis, European Master on Advanced Robotics+(EMARO+), University of Genova, Italy (2017)
Saidani, N., Adi, K., Allili, M.S.: A semantic-based classification approach for an enhanced spam detection. Comput. Secur. 94, 101716 (2020)
Spam or Ham: Convolutional Neural Networks for SMS Classification. https://www.linkedin.com/pulse/spam-ham-convolutional-neural-networks-sms-maggie-lavery/. Accessed 12 Nov 2022
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(1), 1929–1958 (2014)
Tensorflow. https://www.tensorflow.org/api_docs/python/tf/keras/layers/LSTM. Accessed 16 Oct 2022
The Spam Assassin Email Classification Dataset. https://www.kaggle.com/datasets/ganiyuolalekan/spam-assassin-email-classification-dataset. Accessed 05 Nov 2022
Understanding embedding layer in Keras. https://medium.com/analytics-vidhya/understanding-embedding-layer-in-keras-bbe3ff1327ce. Accessed 10 Oct 2022
Understanding LSTM Networks. https://colah.github.io/posts/2015-08-Understanding-LSTMs/. Accessed 25 Oct 2022
Yacim, J., Boshoff, D.: Impact of artificial neural networks training algorithms on accurate prediction of property values. J. Real Estate Res. 40, 375–418 (2018). https://doi.org/10.1080/10835547.2018.12091505
Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2. IEEE (2018)
Zhao, C., Xin, Y., Li, X., Zhu, H., Yang, Y., Chen, Y.: An attention-based graph neural network for spam bot detection in social networks. Appl. Sci. 10(22), 8160 (2020)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Varga, L., Livada, Č., Baumgartner, A., Šojo, R. (2023). Implementation and Evaluation of a Deep Neural Network for Spam Detection: An Empirical Study of Accuracy and Efficiency. In: Ademović, N., Kevrić, J., Akšamija, Z. (eds) Advanced Technologies, Systems, and Applications VIII. IAT 2023. Lecture Notes in Networks and Systems, vol 644. Springer, Cham. https://doi.org/10.1007/978-3-031-43056-5_28
Download citation
DOI: https://doi.org/10.1007/978-3-031-43056-5_28
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-43055-8
Online ISBN: 978-3-031-43056-5
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)