Spam detection has become a critical challenge in modern communication
systems due to the rising volume of unsolicited messages across devices. This paper
proposes an efficient deep learning-based technique for detecting spam messages in real-
time, leveraging the power of neural networks to identify and classify harmful content.
By utilizing advanced models such as Long Short-Term Memory (LSTM) networks and
Convolutional Neural Networks (CNNs), the system can accurately distinguish between
legitimate and spam messages by analyzing both the textual content and contextual
features. The proposed method enhances detection accuracy, reduces false positives, and
is adaptable to various platforms, including mobile devices and web applications,
ensuring robust performance across diverse environments. Through extensive testing and
evaluation, the technique demonstrates significant improvements in spam detection speed
and efficiency, making it a promising solution for enhancing user experience and security
in modern communication systems.The proposed deep learning-based spam detection
technique leverages a multi-layered approach that combines natural language processing
(NLP) with advanced machine learning algorithms to effectively identify spam messages.
By training on large datasets of labeled messages, the model learns intricate patterns and
linguistic features commonly found in spam, such as misleading keywords, unusual
sentence structures, and semantic anomalies. The integration of attention mechanisms
further enhances the model's ability to focus on critical parts of the message, improving
classification accuracy.
Keyword: Spam detection, deep learning, neural networks, Long Short-Term Memory
(LSTM), Convolutional Neural Networks (CNN), natural language processing (NLP),