Enhancing Spam Detection: A Crow-Optimized FFNN with LSTM for Email Security
DOI:
https://doi.org/10.31185/wjcms.199Keywords:
Unwanted emails, Cybersecurity threat, Crow Search Optimization (CSO), Advanced Neural Network (ANN)Abstract
Email security is paramount in today's digital landscape, as the proliferation of spam emails poses a significant threat to individuals and organizations alike. To combat this menace, this study introduces a novel approach that marries the power of Crow Search Optimization (CSO) with a Feedforward Neural Network (FFNN) and Long Short-Term Memory (LSTM) architecture to bolster spam detection. The proposed Crow-Optimized FFNN with LSTM (C-FFNN-LSTM) leverages CSO to fine-tune the neural network's parameters, optimizing its ability to distinguish between legitimate emails and spam. The CSO algorithm mimics the collaborative behavior of crows, thereby enhancing the model's convergence and robustness. Experimental results showcase the effectiveness of the C-FFNN-LSTM approach, achieving remarkable accuracy rates and reducing false positives. This innovation not only enhances email security but also offers a promising avenue for refining spam detection algorithms across various domains. In an era of ever-evolving cyber threats, the C-FFNN-LSTM framework stands as a beacon of improved email security, safeguarding digital communication channels.
In our methodology, we attained an outstanding accuracy level of 99.1% during the testing phase.
References
A. S. Mashaleh, N. F. B. Ibrahim, M. A. Al-Betar, H. M. J. Mustafa, and Q. M. Yaseen, "Detecting Spam Email with Machine Learning Optimized with Harris Hawks optimizer (HHO) Algorithm," Procedia Computer Science, vol. 201, pp. 659-664, 2022.
C. W. F. Parsonson, Z. Shabka, W. K. Chlupka, B. Goh, and G. Zervas, "Optimal Control of SOAs with Artificial Intelligence for Sub-Nanosecond Optical Switching," Journal of Lightwave Technology, pp. 1-1, 2020.
A. Graves, "Long Short-Term Memory," in Supervised Sequence Labelling with Recurrent Neural Networks, pp. 37-45, 2012.
J. Schmidhuber, "Deep learning in neural networks: An overview," Neural Networks, vol. 61, pp. 85-117, 2015.
N. Ullah et al., "A comprehensive survey of email spam detection techniques," Journal of Network and Computer Applications, vol. 60, pp. 52-73, 2016.
X. S. Yang, "A new metaheuristic bat-inspired algorithm," in Nature inspired cooperative strategies for optimization (NICSO 2010), vol. 284, pp. 65-74, 2010.
D. E. Rumelhart, G. E. Hinton, and R. J. Williams, "Learning representations by back-propagating errors," Nature, vol. 323, no. 6088, pp. 533-536, 1986.
S. Hochreiter and J. Schmidhuber, "Long short-term memory," Neural computation, vol. 9, no. 8, pp. 1735-1780, 1997.
X. Liu, H. Lu, and A. Nayak, "A Spam Transformer Model for SMS Spam Detection," IEEE Access, 2021.
M. Alauthman, "Botnet Spam E-Mail Detection Using Deep Recurrent Neural Network," International Journal of Emerging Trends in Engineering Research, vol. 8, no. 5, pp. 1979-1986, 2020.
Saif Wali Ali Alsudani, Adel Ghazikhani, "Enhancing Intrusion Detection with LSTM Recurrent Neural Network Optimized by Emperor Penguin Algorithm," Wasit Journal of Computer and Mathematics Science, vol. 2, no. 3, 2023.
A. Johnson and C. Brown, "Nature-Inspired Algorithms in Cybersecurity: A Comprehensive Review," Journal of Cyber Defense, vol. 12, no. 3, pp. 78-94, 2023.
S. Kim and M. Lee, "Crow-Optimized FFNN with LSTM for Email Security: A Case Study," Proceedings of the International Conference on Machine Learning and Cybersecurity, pp. 231-245, 2023.
Downloads
Published
Issue
Section
License
Copyright (c) 2024 Saif Alsudani

This work is licensed under a Creative Commons Attribution 4.0 International License.