# Spam Email Detection Using Long Short-Term Memory and Gated Recurrent Unit
## 1 Introduction
In recent years, numerous scholars have concentrated on developing phishing detection algorithms based on deep learning (DL) and machine learning to address existing methods' limitations and enhance detection accuracy [13]. These deep learning models are adept at identifying patterns and detecting data anomalies, thus improving the effectiveness of phishing detection. As a subset of machine learning, deep learning encompasses models such as Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM) networks, and Convolutional Neural Networks (CNNs). These highly flexible algorithms can learn data patterns, features, and email structures, resulting in promising outcomesfor identifying phishing emails [14]. Furthermore, deep learning techniques can efficiently classify phishing attacks by integrating feature extraction and selection processes typically performed manually by human experts [15]. This research examines the challenges of detecting spam emails through a hybrid model that combines Long Short-Term Memory (LSTM) networks and Gated Recurrent Units (GRUs). The authors employ this integrated learning framework to mitigate the vanishing gradient problem prevalent during the training of lengthy text sequences to ensure a stable training process and effective gradient propagation. The model is designed to maintain relevant information across necessary time steps for processing large texts while preserving contextual integrity without incurring high memory usage. Enhancing contextual awareness, the model addresses two relationships within extensive text datasets: long-term dependencies and complex syntactic and semantic interconnections. These elements encompass scalability and variability for real-world applications, enabling the implementation to function robustly across diverse text domains, lengths, and multiple languages. The proposed system for spam email detection follows a structured process to determine whether a given email message falls into the spam category. The system begins by collecting email data from public repositories and datasets. Following data collection, it undergoes an email preprocessing phase that includes three key operations: HTML cleaning, URL extraction, and text normalization. Next, a transformation phase converts text data into vectorized values before additional metadata is incorporated into the dataset. The foundational structure of the system comprises three main components: an embedding layer, GRU, and LSTM layers, which collaborate to produce a final output via a dense layer for the classification process. The model employs training methods such as cross-validation and grid search to optimize performance. Finally, the system evaluates spam detection capabilities using metrics like accuracy, F1-score, and ROC/AUC to categorize emails and assign confidence scores. Having stated that, it can be said that the proposed study is original in its own way since it suggests a hybrid deep learning model, and the concept of incorporating LSTM and the Gated Recurrent Unit (GRU) networks to identify spam email can be regarded as original as well. This type of model has the vanishing gradients and complexity problems that are attributed to deep sequence modeling. The emails also have features that define the text, which are improved through the employment of good preprocessing techniques in the model. Overall, the work offers a scaleable, efficient, and deployable resolution in reality to bridge the gap between the theory of deep learning and the reality of spam detection needs. The rest of the paper is organized as follows: Section 2 discusses the previous work, and Section 3 provides detail about the dataset collection and preprocessing. Section 4 discusses the proposed method; Section 5 evaluate the performance of the proposed model, and Section 6 concludes the paper.