# Spam Email Detection Using Long Short-Term Memory and Gated Recurrent Unit
## 2 Related Works  
A current threat to digital communication is nevertheless email spam, and such a form of communication needs constant enhancement of the spam filtering mechanism. In the last ten years, analysis of SARs has employed diverse methods, starting with simple rule-based approaches and ending with more complex machine learning ones. However, in the recent past, there has been remarkable concern in applying deep learning architectures in improving the functioning of the spam detection systems. Another survey [16] established that several approaches to spam filtering had numerous drawbacks. The authors examined how idea drift affects spam in the following classifications: sudden, reoccurring, gradual, and incremental. This problem was thus discovered to have numerous difficulties by their research. The different types of concept drift in their findings included concept drift in ham messages, various categories of concept drift in both ham and spam messages, and topics that contain more than one or several categories of concept drift. The sources of notion drift within this study were identified as internal, including changes in company activity, marketing interest shift, communication, language, and economic factors. The authors [PERSON], [PERSON], and [PERSON], in a blend of CNN and LSTM, suggested techniques for classifying the emails. It showed the effectiveness of their proposed hybrid model through a comparison of results with that of several commonly used techniques, including Gaussian Naive Bayes (GNB) and decision trees [17]. The authors of [18], including [PERSON] et al., have reviewed the strengths and limitations of machine learning models with references to the spam email dataset as well as with reference to the alternative methodologies of hyper-parameter tweaking. The authors considered possible difficulties and limitations and proposed options for further research, such as the combination of two or more approaches. [PERSON] et al. [19] for learning non-stationary data streams put forward the fast adaptive stacking of ensembles (FASE) approach. Their method from the real-time input has constant time and space complexity. It showed that their trials were more accurate in the prediction compared to other classical machine learning techniques. [PERSON] et al. [20] have tracked volunteer emails using email spam classification approaches such as Naive Bayes, SVM, decision trees, and meta-classification with an offered precision estimation of 95.67%. [PERSON] et al. applied a stacked CNN for the discovery of the false and spam tweets [21]. In the study by [PERSON] [22], a deep learning model for email classification is presented with a focus on spam and phishing emails. These findings provide a higher level of accomplishment than previous works by using other benchmark datasets besides GDSR, various feature selection techniques, and an efficient model structure. Furthermore, it also discusses time complexity and offers a critical comparison with prior work. Email spam detection has become a focus of great interest for deep learning architectural designs. [PERSON]. [3] proposed the RNN-GRU classification model for the selective spam detection mainly highlighting the evolution of spam detection techniques from simple filters to refined category classification such as the GRU schemes. The study concerns itself with just phishing emails, making it relevant to exert the accurate detection to secure the communication channels and personal emails. Apart from the application of individual models, ensemble learning strategies have also dealt with different ways of combining models with the aim of increasing the throughput of filtering spam messages. In [23], the authors developed an elaborate ensemble of classifiers that consisted of deep neural classifiers and the aplex support vector machine with the intentions of enhancing the reliability and the flexibility of the detection system.