Enhancing Spam Detection: A Crow-Optimized FFNN with LSTM for Email Security
الباحث الأول:
Saif Wali Ali Alsudani
الباحثين الآخرين:
Hussein Ali Manji Nasrawi
Muntadher Hasan
Shattawi
Adel Ghazikhani
المجلة:
Wasit Journal of Computer and Mathematics Science
تاريخ النشر:
1 إبريل، 2024
مختصر البحث:
ABSTRACT: In the contemporary digital landscape, safeguarding email communication against the omnipresent
threat of spam remains a critical concern. This study introduces a novel approach, the Crow-Optimized Feedforward
Neural Network with Long Sh…
ABSTRACT: In the contemporary digital landscape, safeguarding email communication against the omnipresent
threat of spam remains a critical concern. This study introduces a novel approach, the Crow-Optimized Feedforward
Neural Network with Long Short-Term Memory (C-FFNN-LSTM), designed to enhance spam detection capabilities.
By integrating the collaborative behavior of crows through Crow Search Optimization (CSO) into the fine-tuning
process of neural network parameters, the model aims to fortify email security. The combination of Feedforward
Neural Network (FFNN) and Long Short-Term Memory (LSTM) architecture ensures a robust system for accurate
spam detection.
Efficiency is evaluated through established standards, including accuracy rates and false positive reduction.
Experimental results demonstrate the efficacy of the C-FFNN-LSTM framework, showcasing exceptional accuracy
levels and a notable decrease in false positives during testing. The proposed algorithm not only contributes to
strengthening email security but also offers a promising avenue for refining spam detection algorithms in diverse
domains. In the face of evolving cyber threats, this innovative approach represents an improved email security
paradigm, emphasizing its robustness and reliability with an outstanding accuracy level of 99.1% during testing.
Keywords: Unwanted emails, Cybersecurity threat, Crow Search Optimization (CSO), Advanced Neural Network
(ANN), Memory-Augmented Neural Network (MANN), Unwanted communication detection, and Communication
privacy.
