A comparative analysis of recurrent neural network and support vector machine for binary classification of spam short message service

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) 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.

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.

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.

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.

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) 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) is one of the widely used mobile applications for global communication for personal and business purposes. Its widespread use for customer interaction, business updates, and reminders has made it a billion-dollar industry in “Text Marketing.” Along with valid SMS, a tsunami of spam messages also pop up that serve various purposes for the sender and the majority of them are fraudulent. Filtering spam SMS in an accurate manner is a crucial and challenging task that will benefit human lives both mentally and economically. Some of the challenges in the filtering of spam SMS include less number of characters, texts in informal languages, lack of public SMS spam corpus, and so on. Focusing solely on the textual features of the SMS is a major handicap of the existing methods, as it lacks in dynamically adapting to the increasing number of new keywords and jargon. In this article, we develop an intention-based approach of SMS spam filtering that efficiently handles dynamic keywords by focusing on the semantics of the words. We capture both semantic and textual features of the short-text messages based on 13 pre-defined intention labels. Moreover, the contextual embeddings of the texts are generated using various pre-trained NLP (Natural Language Processing) models. Finally, intention scores are computed for the pre-defined labels and a bunch of supervised learning classifiers are employed for filtering as spam or ham. Our approaches are evaluated on the SMS Spam Collection [ 24 ] benchmark dataset, and extensive experimentation shows interesting results. Our model did remarkably well with an accuracy of 98.07%, Precision and Recall of ∼ 0.97, which is better than few of the existing state-of-the-art alternatives. Though the accuracy of our approach is not the best among other existing approaches, the model is highly stable due to its emphasis on extracting the contextual features from the text through intention labels.

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.

ObjectiveTo assess healthcare providers’ intentions and the associated factors to use mobile phone-based short message service (SMS) to support adherence and care of tuberculosis (TB) patients in the Oromia region...

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

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%.

Short Message Service (SMS) is one of the mobile communication services that allows easy and inexpensive communication. Producing unwanted messages with the aim of advertising or harassment and sending these messages on SMS have become the biggest challenge in this service. Various methods have been presented to detect unsolicited short messages; many of which are based on machine learning. Neural Networks have been applied to separate the unwanted text messages (known as spam) from normal short messages (known as ham) in SMS. To the best of our knowledge, Recurrent Neural Network (RNN) has not been used in this issue yet. In this paper, we proposed a new method which utilizes RNN to separate the ham and spam with variable length sequences; even though we used a fixed sequence length. The proposed method achieved an accuracy of 98.11, indicates a considerable improvement compared to Support Vector Machine (SVM), token-based SVM and Bayesian algorithms with accuracies of 97.81, 97.64, and 80.54, respectively.

BackgroundContemporary perioperative fasting guidelines aim to alleviate patient discomfort before surgery and enhance postoperative recovery whilst seeking to reduce the risk of pulmonary aspiration during anesthesia. The impact of a short message service (SMS) reminder on fasting guideline compliance is unknown. Therefore, we performed a retrospective observational study and quality improvement project aiming to quantify the extent of excessive and prolonged fasting, and then assessed the impact of a SMS reminder in reducing fasting times.MethodsAfter ethics committee approval we performed a retrospective observational study investigating preoperative fasting times of adult patients undergoing elective surgery. First, we assessed whether the fasting guideline times were adhered to (Standard Care group). All patients received internationally recommended fasting guidelines in the form of a written hospital policy document. We then implemented an additional prompt via a mobile phone SMS 1 day prior to surgery containing a reminder of fasting guideline times (SMS group). The primary aims were to compare fasting times between the Standard Care group and the SMS group.ResultsThe fasting times of 160 patients in the Standard Care group and 110 patients in the SMS group were evaluated. Adherence to the fasting guidelines for solids occurred in 14 patients (8.8%) in the Standard Care group vs. Twenty-two patients (13.6%) in the SMS group (p=0.01). Adherence to the fasting guidelines for fluids occurred in 4 patients (2.5%) in the Standard Care group vs. Ten patients (6.3%) in the SMS group (p=0.023). Patients in the Standard Care group had a longer median (inter-quartile range (IQR)) fasting time for fluids compared the SMS group [6.5 h (IQR 4.5:11) vs 3.5 h (IQR 3:8.5), p< 0.0001]. Median fasting times for solids were 11 h (IQR 7:14) in the Standard Care group and 11.5 h (IQR 7:13.5) in the SMS group (p=0.756).ConclusionAdherence to internationally recommended fasting guidelines for patients undergoing elective surgery is poor. The introduction of a fasting guideline reminder via a mobile phone SMS in addition to a written hospital policy improved adherence to fasting advice and reduced the fasting times for fluids but not for solids. The use of an SMS reminder of fasting guidelines is a simple, feasible, low-cost, and effective tool in minimising excessive fasting for fluids among elective surgical patients.Trial registrationACTRN12619001232123 (Australia New Zealand Clinical Trials Registry). Registered 6th September 2019 (retrospectively registered).

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.