ExplainableDetector：探索基于Transformer的语言建模方法用于可解释性分析的SMS垃圾短信检测
ExplainableDetector: Exploring Transformer-based Language Modeling Approach for SMS Spam Detection with Explainability Analysis
AI总结：
针对SMS垃圾短信检测难题，提出基于优化微调Transformer大模型的检测方法，结合文本增强解决类别不平衡，RoBERTa达99.84%准确率，并用XAI技术实现模型可解释性分析。
摘要：
SMS（短消息服务）是一种被广泛使用且成本低廉的通信媒介，遗憾的是它已成为无用消息（即SMS垃圾短信）的滋生地。随着智能手机和互联网连接的快速普及，SMS垃圾短信已成为一种普遍存在的威胁。垃圾短信发送者已经注意到SMS对手机用户的重要性。因此，近年来随着新型网络安全威胁的出现，SMS垃圾短信的数量大幅增长。SMS数据的非结构化格式给SMS垃圾短信检测带来了巨大挑战，使得在网络安全领域更难成功抵御垃圾短信攻击。在本研究中，我们采用经过优化和微调的基于Transformer的大语言模型（LLMs）来解决垃圾短信检测问题。我们使用一个基准SMS垃圾短信数据集开展本次垃圾短信检测研究，采用多种预处理技术获得干净无噪声的数据，并通过文本增强技术解决类别不平衡问题。整体实验表明，我们优化后的微调BERT（Bidirectional Encoder Representations from Transformers，双向编码器表征Transformer）变体模型RoBERTa取得了99.84%的高准确率。我们还运用可解释人工智能（XAI）技术计算正负系数得分，以此探究并解释微调模型在这项基于文本的SMS垃圾短信检测任务中的透明度。此外，我们还检验了传统机器学习（ML）模型，将其性能与基于Transformer的模型进行对比。该分析阐明了大语言模型如何在网络安全领域对复杂的基于文本的垃圾短信数据发挥良好作用。
英文摘要：
SMS, or short messaging service, is a widely used and cost-effective communication medium that has sadly turned into a haven for unwanted messages, commonly known as SMS spam. With the rapid adoption of smartphones and Internet connectivity, SMS spam has emerged as a prevalent threat. Spammers have taken notice of the significance of SMS for mobile phone users. Consequently, with the emergence of new cybersecurity threats, the number of SMS spam has expanded significantly in recent years. The unstructured format of SMS data creates significant challenges for SMS spam detection, making it more difficult to successfully fight spam attacks in the cybersecurity domain. In this work, we employ optimized and fine-tuned transformer-based Large Language Models (LLMs) to solve the problem of spam message detection. We use a benchmark SMS spam dataset for this spam detection and utilize several preprocessing techniques to get clean and noise-free data and solve the class imbalance problem using the text augmentation technique. The overall experiment showed that our optimized fine-tuned BERT (Bidirectional Encoder Representations from Transformers) variant model RoBERTa obtained high accuracy with 99.84\%. We also work with Explainable Artificial Intelligence (XAI) techniques to calculate the positive and negative coefficient scores which explore and explain the fine-tuned model transparency in this text-based spam SMS detection task. In addition, traditional Machine Learning (ML) models were also examined to compare their performance with the transformer-based models. This analysis describes how LLMs can make a good impact on complex textual-based spam data in the cybersecurity field.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
