SPAM EMAIL DETECTION USING DEEP LEARNING MODEL
Rajnikant Narayan, Mr.Vinay Kumar · 2024
The persistent menace of spam emails poses a formidable cybersecurity threat, demanding the implementation of robust classification models for prompt detection and prevention. This research addresses the intricate challenge of classifying spam emails by evaluating diverse machine learning and deep learning models. The focal point of the identified problem lies in the inherent complexity of distinguishing spam emails from legitimate ones, leading to potential security vulnerabilities. Existing models' limitations, particularly in managing false positives and false negatives, underscore the imperative for enhanced techniques. The chosen methodology incorporates conventional machine learning models, such as Random Forest, Multinomial Naive Bayes (MNB), and Support Vector Machine (SVM), in conjunction with the Netcraft dataset. Additionally, deep learning models, including Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), and Bidirectional LSTM (BiLSTM), are deployed with meticulous hyperparameter tuning. An attention mechanism is introduced to dynamically allocate distinct weights to email headers and bodies based on their significance. The outcomes unveil the consistently superior performance of the Random Forest model, attaining a flawless F1-score of 1.0 for spam email classification. The SVM model also exhibits excellence with impeccable precision and recall across all folds. Nevertheless, the MNB model encounters difficulties in classifying legitimate emails, influencing overall metrics. The deep learning models, especially LSTM, CNN, BiL STM, and RNN, demonstrate exceptional efficacy with remarkably minimal test losses and elevated accuracies, underscoring their prowess in spam email classification tasks.