SpotSpam: Intention Analysis–driven SMS Spam Detection Using BERT Embeddings

With the overflowing of Short Message Service (SMS) spam nowadays, many traditional text classification algorithms are used for SMS spam filtering. Nevertheless, because the content of SMS spam messages are miscellaneous and distinct from general text files, such as more shorter, usually including mass of abbreviations, symbols, variant words and distort or deform sentences, the traditional classifiers aren't fit for the task of SMS spam filtering. In this paper, the authors propose a Short Message Biterm Topic Model (SM-BTM) which can be used to automatically learn latent semantic features from SMS spam corpus for the task of SMS spam filtering. The SM-BTM is based on the probability of topic model theory and Biterm Topic Model (BTM). The experiments in this work show the proposed model SM-BTM can acquire higher quality of topic features than the original BTM, and is more suitable for identifying the miscellaneous SMS spam.

With the prosperity of SMS (short message service), there are more and more SMS spam. This paper proposes a method for filtering group sending SMS Spam, which combines black-and-white list technology and CAPTCHA (completely automated public Turing test to tell computers and human apart) technique. At first, it utilizes black-and-white list technique to check out the SMS account. If the SMS account is on the blacklist or white list, the SMS will be disposed directly without using CAPTCHA technique. In contrast, if the SMS account is not on the black-and-white list, then it makes use of CAPTCHA to handle it. If the SMS sender can not pass the CAPTCHA, the SMS will be tagged as s SMS spam and deleted. Finally, it has been simulated with Smartphone of Microsoft Visual Studio 2005.

Short Message Service (SMS) spam is increasingly becoming a problem for many telecommunication service providers. Not only do SMS spam messages use mobile network resources abusively, but also in many cases they represent malware propagation vectors for mobile devices. In this work, we propose a network-based online detection method for SMS spam messages. The proposed scheme uses robust text signatures to identify similar messages that are sent excessively in the SMS platform and is robust against slight modifications in SMS spam messages. Additionally, the method uses a fast online algorithm which can be deployed in large carrier networks to detect spam activities before too many spam messages are delivered. It does not store SMS message contents, therefore it does not compromise the privacy of mobile subscribers.

Short Message Service (SMS) spam classification systems based on machine learning and deep learning have achieved high accuracy under standard evaluation settings; however, their vulnerability to adversarial attacks poses significant risks to real-world deployment. Attacks on SMS spam classifiers are realized as precision-level adversarial examples, where carefully crafted perturbations (e.g., character substitution/reordering, word disguising and synonym injection, benign token insertion) maintain human readability while drawing models to misclassify. In this paper, we study universal adversarial attacks on SMS spam clasification models and their adaptation to various feature representations and learning architectures: from classic machine learning models to deep neural networks and transformer-based solutions. And it introduces defense terms like adversarial training, data augmentation, ensemble learning, input sanitization and defensive distillation. Primary challenges such as trade-offs between robustness and accuracy, language diversity, and limited computation in mobile contexts are considered. The findings of the paper show possible future research challenges worth addressing for building robust SMS spam detection systems that are still performing effectively under adversarial conditions.

TruSMS: A trustworthy SMS spam control system based on trust management

Short Message Service (SMS) is the most important communication tool in recent decades. With the increased popularity of mobile devices, the usage rate of SMS will increase more and more in years. SMS is a practical method used to reach individuals directly. But this practical and easy method can cause SMS to be misused. The advertising or promotional SMS of the companies are an examples of this misuse. In this study, a spam SMS detection technique is proposed using SVM. SMSSpamCollection dataset, which is contain 747 spam SMS and 4827 ham SMS, is used. 10 fold cross-validation technique is used to evaluate prediction of Spam SMS in the dataset. Therefore, proposed approach achieved 98.33 % true positive rate and 0,087 false positive rate for SVM classification algorithm.

Short Message Service (SMS) has evolved in the last few decades. The simplicity of SMS makes this short message service attractive to use as a direct communication service on mobile devices. As the popularity of this service increases, it also harms attacks on mobile devices such as SMS spam. Spam SMS are short messages that the recipient doesn't want, such as advertisements and scams. Spam SMS can overwhelm your inbox and make your mobile device experience less good. One way to overcome this problem is to implement a machine learning model to automatically recognize and filter Spam SMS. This research aims to build a machine learning model that provides higher accuracy for detecting SMS spam in Indonesian using the TF-IDF method and the Stochastic Gradient Descent Classifier. Based on the test results, the model built can detect SMS spam and not spam with an accuracy of 97%.

Over the years, communication through Short Message Service (SMS) has been a primary tool for mobile subscribers. SMS has varied applications in health, industry, finances, education and social networking among others. The growth of mobile devices and SMS usage has consequently increased the attack surface for cyber-criminals culminating to the proliferation of malicious activities introduced using SMS spam, phishing, spyware, malware etc. Ham messages are normal messages people trade with one another and are usually not unwanted by the recipient, while spam messages are unsolicited junk and redundant messages that may be sent to a large number of people at once and are usually unwanted. Various spam detection models have been developed using various traditional machine and deep learning techniques. However, most studies where comparison between deep and traditional machine learning algorithms is done, have unfortunately omitted K-Nearest Neighbors and Support Vector Machine (SVM) which are empirically deemed as the most popular traditional machine learning algorithms. In this study, therefore, we develop a deep learning model based on Recurrent Neural Network (RNN) for Spam and Ham SMS classification and compare its performance against SVM model for the same University of California (UCl) SMS dataset. The results show that RNN has a slightly higher training and validation accuracy of 0.98 compared to SVM at 0.94, however, the false positive rate of SVM is marginally lower. Exploring application of deep learning with better optimization algorithms such as RNN improves accuracy, reduces computational complexities i.e. memory consumption and speed, and thus minimizing false positive rates. For future work, we suggest the use of varied performance metrics to validate the model in a distributed dataset environment.

Short Message Service (SMS) still continues its existence despite the emergence of different messaging services. It plays a part in our lives as a communication service. Companies use SMS for advertisement purposes due to the fact that e-mail filtering systems have rooted, short message systems are being undersold by the operators, and spam detection and blocking systems used for short messages are ineffective. Individuals falling victim to SMS spam messages sent by malevolent persons incur pecuniary and non-pecuniary losses. The aim of this study is to present a hybrid model proposal with the intention of detecting SMS spam messages. This detection model uses a gated recurrent unit (GRU) and convolutional neural network (CNN) as two deep learning methods. However, the fact that both algorithms require high memory capacities is a limitation. The design for this model was laid out by using two different datasets containing combined text messages written in the Turkish and English languages. The datasets used in the study are TurkishSMSCollection and the SMS Spam dataset from the UCI database. The testing process was performed on the dataset through benchmarking as well as other machine learning algorithms. It was revealed in the study that the hybrid CNN + GRU approach attained an accuracy of 99.07% by demonstrating a better performance compared to the other algorithms.

In the modern life, SMS (Short Message Service) is one of the most necessary services on mobile devices. Because of its popularity, many companies use SMS as an effective marketing and advertising tool. Also, the popularity gives hackers chances to abuse SMS to cheat mobile users and steal personal information in their mobile phones, for example. In this paper, we propose a method to detect spam SMS on mobile devices and smart phones. Our approach is based on improving a graph-based algorithm and utilizing the KNN Algorithm - one of the simplest and most effective classification algorithms. The experimentation is carried out on SMS message collections and the results ensures the efficiency of the proposed method, with high accuracy and small processing time enough for detecting spam messages directly on mobile phones in real time.

Short message service (SMS) is considered as one of the most popular means of communication, it allows to the mobile phone users to exchange a short text message with a low cost. Its growing popularity and its dependence on mobile phone has increased the number of attacks, caused by sending an unsolicited message like SMS spam. In this paper, we address a comparative study, between multilayer perceptron (MLP), support vector machine (SVM), random forest and k-nearest neighbors (KNN). For extracting the feature vectors the bag-of-words (BOW) and the TF-IDF methods are applied. These feature vectors are used as input for training and testing the different machine-learning classifiers mentioned above. The results of different machine-learning classifiers, based on their accuracy, precision, recall, F-measure, and ROC (receiver operating characteristic) curve have shown that the MLP outperforms SVM, random forest and KNN in SMS spam detection. Although the MLP has achieved the highest accuracy by using the BOW, than by using the TF-IDF method.

Contemporary spam filters increasingly rely on resource-intensive, deep learning models. This study evaluates the performance, robustness, and deployability of lightweight learning models. It provides a head-to-head evaluation of probabilistic (Naïve Bayes) and margin-based (Support Vector Machine) classifiers on three feature spaces derived from the 5574-message UCI Short Message Service (SMS) spam collection. Our primary finding shows that a hybrid model, a fusion of bag-of-words (BoW) representation with 22 handcrafted metadata features, achieves the highest accuracy, with SVM peaking at 98.3%. To assess the resilience, the model was tested against adversarial attacks. The hybrid SVM model exhibited strong robustness when faced with altered data and maintained 72.41% accuracy against challenging semantic attacks. Furthermore, the hybrid SVM model demonstrated strong cross-dataset generalization, achieving 74.38% accuracy when trained on the original UCI data and tested on a modern, diverse dataset of SMS, Telegram, and email messages. Deployment analysis confirmed the efficiency of the framework, with processing of ~ 200 requests/s (fastest model) at less than 10ms latency and ~ 1.5% average CPU load on a standard CPU. The results establish three key principles for next-generation spam filters: (i) lexical information remains the dominant signal; (ii) lightweight metadata provides measurable incremental value when paired with text; and (iii) margin-based classifiers exploit multimodal fusion most effectively. Taken together, these findings validate that a lightweight hybrid feature-engineering approach provides a robust, generalizable, and resource-efficient solution for real-time spam mitigation, thereby presenting a compelling and practical alternative to computationally expensive deep learning architectures.

The Short Messaging Service (SMS), one of the most successful cellular services, generates millions of dollars in revenue for mobile operators. Estimates indicate that billions of text messages are traveling the airwaves daily. Nevertheless, text messaging is becoming a source of customer dissatisfaction due to the rapid surge of messaging abuse activities. Although spam is a well tackled problem in the email world, SMS spam experiences a yearly growth larger than 500%. In this paper we present, to the best of our knowledge, the first analysis of SMS spam traffic from a tier-1 cellular operator. Communication patterns of spammers are compared to those of legitimate cell-phone users and Machine to Machine (M2M) connected appliances. The results indicate that M2M systems exhibit communication profiles similar to spammers, which could mislead spam filters. Beyond the expected results, such as a large load of text messages sent out to a wide target list, other interesting findings are made. For example, the results indicate that the great majority of the spammers connect to the network with just a handful of different hardware models. We find the main geographical sources of messaging abuse in the US. We also find evidence of spammer mobility, voice and data traffic resembling the behavior of legitimate customers.

Over the past few years, short message service (SMS) usage has significantly increased. This service is used to deliver text messages by billions of people. Service providers have launched a number of popular applications, including mobile banking, summons checkpoints, SMS chat, and others. This chapter explores the numerous SMS applications that are available to users and provides an outline of how this service is provided. We examine the causes of its success and the problems that need to be solved. We also look at upcoming trends and the difficulties that to improve this service, certain obstacles must be overcome. This chapter should help you understand how SMS applications work and what to expect from them going forwards given the improvements to current SMS and technological advancement. We propose a privacy-preserving Naive Bayes classifier and apply it to the problem of private text classification. In this setting, a party (Alice) holds a text message, while another party (Bob) holds a classifier. At the end of the protocol, Alice will only learn the result of the classifier applied to her text input and Bob learns nothing. Our solution is based on Secure Multiparty Computation (SMC). Our Rust implementation provides a fast and secure solution for the classification of unstructured text. Applying our solution to the case of spam detection (the solution is generic, and can be used in any other scenario in which the Naive Bayes classifier can be employed), we can classify an SMS as spam or ham in less than 340 ms in the case where the dictionary size of Bob’s model includes all words (n 5200) and Alice’s SMS has at most m 160 unigrams. In the case with n 369 and m 8 (the average of a spam SMS in the database), our solution takes only 21 ms.

Semi-supervised learning using frequent itemset and ensemble learning for SMS classification