In the modern era, mobile phones have become ubiquitous, and Short Message
Service (SMS) has grown to become a multi-million-dollar service due to the
widespread adoption of mobile devices and the millions of people who use SMS
daily. However, SMS spam has also become a pervasive problem that endangers
users' privacy and security through phishing and fraud. Despite numerous spam
filtering techniques, there is still a need for a more effective solution to
address this problem [1]. This research addresses the pervasive issue of SMS
spam, which poses threats to users' privacy and security. Despite existing spam
filtering techniques, the high false-positive rate persists as a challenge. The
study introduces a novel approach utilizing Natural Language Processing (NLP)
and machine learning models, particularly BERT (Bidirectional Encoder
Representations from Transformers), for SMS spam detection and classification.
Data preprocessing techniques, such as stop word removal and tokenization, are
applied, along with feature extraction using BERT. Machine learning models,
including SVM, Logistic Regression, Naive Bayes, Gradient Boosting, and Random
Forest, are integrated with BERT for differentiating spam from ham messages.
Evaluation results revealed that the Na\"ive Bayes classifier + BERT model
achieves the highest accuracy at 97.31% with the fastest execution time of 0.3
seconds on the test dataset. This approach demonstrates a notable enhancement
in spam detection efficiency and a low false-positive rate. The developed model
presents a valuable solution to combat SMS spam, ensuring faster and more
accurate detection. This model not only safeguards users' privacy but also
assists network providers in effectively identifying and blocking SMS spam
messages.

提出了利用自然语言处理和机器学习模型（特别是 BERT）进行短信垃圾检测和分类的方法，结果显示使用朴素贝叶斯分类器 + BERT 模型在测试数据集上实现了最高 97.31% 的准确率和最快 0.3 秒的执行时间，从而在短信垃圾检测效率和误报率方面有显著的改进。该模型为对抗短信垃圾提供了有价值的解决方案，不仅保护了用户的隐私，还帮助网络提供商有效识别和屏蔽短信垃圾消息。

利用自然语言处理技术对电话网络进行短信垃圾检测和分类以打击滥用行为

SMS Spam Detection and Classification to Combat Abuse in Telephone  Networks Using Natural Language Processing

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.

使用优化和微调的基于 Transformer 的大型语言模型解决短信垃圾信息检测问题，通过预处理技术、文本扩增技术解决数据噪声和类别不平衡问题，实验结果显示我们的优化微调 BERT 模型 RoBERTa 在短信垃圾信息检测任务中获得 99.84% 的高准确率，并采用可解释人工智能（XAI）技术探索和解释模型的透明性，对比了传统机器学习模型和基于 Transformer 的模型性能，描述了大型语言模型在信息安全领域中对复杂文本垃圾数据的积极影响。

可解释检测器：探索基于 Transformer 的语言建模方法用于具有可解释性分析的短信垃圾检测

ExplainableDetector: Exploring Transformer-based Language Modeling  Approach for SMS Spam Detection with Explainability Analysis

In this study, we introduce SpamDam, a SMS spam detection framework designed
to overcome key challenges in detecting and understanding SMS spam, such as the
lack of public SMS spam datasets, increasing privacy concerns of collecting SMS
data, and the need for adversary-resistant detection models. SpamDam comprises
four innovative modules: an SMS spam radar that identifies spam messages from
online social networks(OSNs); an SMS spam inspector for statistical analysis;
SMS spam detectors(SSDs) that enable both central training and federated
learning; and an SSD analyzer that evaluates model resistance against
adversaries in realistic scenarios. Leveraging SpamDam, we have compiled over
76K SMS spam messages from Twitter and Weibo between 2018 and 2023, forming the
largest dataset of its kind. This dataset has enabled new insights into recent
spam campaigns and the training of high-performing binary and multi-label
classifiers for spam detection. Furthermore, effectiveness of federated
learning has been well demonstrated to enable privacy-preserving SMS spam
detection. Additionally, we have rigorously tested the adversarial robustness
of SMS spam detection models, introducing the novel reverse backdoor attack,
which has shown effectiveness and stealthiness in practical tests.

我们介绍了 SpamDam，一个 SMS 垃圾信息检测框架，旨在克服检测和理解 SMS 垃圾信息的关键挑战，包括缺乏公共 SMS 垃圾数据集，收集 SMS 数据的隐私问题，以及对抗对抗性检测模型的需求。