Enhancing Spam Detection: A Crow-Optimized FFNN with LSTM for Email Security

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.