Summary of Explainabledetector: Exploring Transformer-based Language Modeling Approach For Sms Spam Detection with Explainability Analysis, by Mohammad Amaz Uddin et al.
ExplainableDetector: Exploring Transformer-based Language Modeling Approach for SMS Spam Detection with Explainability Analysis
by Mohammad Amaz Uddin, Muhammad Nazrul Islam, Leandros Maglaras, Helge Janicke, Iqbal H. Sarker
First submitted to arxiv on: 12 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper tackles the issue of Short Messaging Service (SMS) spam detection, a pressing concern in today’s digital landscape. To combat this problem, researchers employ optimized and fine-tuned transformer-based Large Language Models (LLMs), specifically RoBERTa, to achieve high accuracy rates of 99.84%. The model is trained on a benchmark SMS spam dataset, with preprocessing techniques used to handle noise and class imbalance issues. The study also explores the transparency of the model using Explainable Artificial Intelligence (XAI) techniques, calculating positive and negative coefficient scores. This work demonstrates the potential of LLMs in complex textual-based spam detection tasks, providing valuable insights for cybersecurity applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary SMS spam is a major issue that affects mobile phone users worldwide. To combat this problem, researchers have developed a new method using Large Language Models (LLMs) to detect spam messages. The model uses a special type of AI called RoBERTa, which is trained on a large dataset of SMS messages. This helps the model learn how to recognize patterns in language that are typical of spam messages. The results show that this method can accurately identify spam messages with an accuracy rate of 99.84%. The study also explores why this method works so well and what it means for cybersecurity. |
Keywords
» Artificial intelligence » Transformer
