This research evaluates multiple classifier-feature combinations to identify the most effective approach for detecting SMS spam messages.

• Compares six classification algorithms (Naive Bayes, KNN, SVM, LDA, Decision Trees, and Deep Neural Networks)
• Tests two feature extraction methods: bag-of-words and TF-IDF
• Identifies the optimal combination for accurate spam detection
• Demonstrates how context-aware text classification strengthens security

For cybersecurity professionals, this research offers practical insights into building more robust spam detection systems that can adapt to evolving threats in mobile communications.

Leveraging Large Language Models for Cybersecurity: Enhancing SMS Spam Detection with Robust and Context-Aware Text Classification