SMS Spam Detection Using Machine Learning: An Experimental Study

2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA), 2015

In the past years, SMS messages have shown to be a profitable revenue to the cell-phone industries, being one of the most used communication systems to date. However, this very same scenario has led spammers to concentrate their attentions into spreading spam messages through SMS, thus achieving some success due to the lack of proper tools to cope with this problem. In this paper, we introduced the Optimum-Path Forest classifier to the context of spam filtering in SMS messages, as well as we compared it against with some state-of-the-art supervised pattern recognition techniques. We have shown promising results with an user-friendly classifier, which requires minimum user interaction and less knowledge about the dataset.

2011

The growth of mobile phone users has lead to a dramatic increasing of SMS spam messages. In practice, fighting mobile phone spam is difficult by several factors, including the lower rate of SMS that has allowed many users and service providers to ignore the issue, and the limited availability of mobile phone spam-filtering software. On the other hand, in academic settings, a major handicap is the scarcity of public SMS spam datasets, that are sorely needed for validation and comparison of different classifiers. Moreover, as SMS messages are fairly short, content-based spam filters may have their performance degraded. In this paper, we offer a new real, public and non-encoded SMS spam collection that is the largest one as far as we know. Moreover, we compare the performance achieved by several established machine learning methods. The results indicate that Support Vector Machine outperforms other evaluated classifiers and, hence, it can be used as a good baseline for further comparison.

Turkish Journal of Computer and Mathematics Education (TURCOMAT), 2021

Over the last decade, the growth of short message services has been rising. These text messages are more powerful for corporations than even SMS. This is because about 80 percent of sms remain unopened while 98 percent of smartphone users read theirs by the end of the day. Spam, which refers to any irrelevant text messages sent via mobile networks, has also gained popularity. For consumers, they are seriously irritating. Due to the geographical material, use of abbreviated words, the current Spam Detection techniques are more challenging than e-mail spam detection techniques , unfortunately very few of the existing research addresses these challenges. Much of the current research that has attempted to filter Spam has focused on features that were manually found. This paper aims to solve these concerns. Filtering is one of the most effective strategies among the methods developed to stop spam. Days of machine learning techniques are now used to process the spam SMS automatically at a very good rate. The goal of this research is to differentiate between ham and spam messages by developing an accurate and responsive model of classification that provides good accuracy with a low false positive rate

High Technology Letters, 2024

This project focuses on enhancing mobile SMS spam detection through a systematic approach. The objective is to improve accuracy and reliability in identifying spam messages. The study includes a thorough literature review, extraction of a diverse SMS dataset, pre-processing techniques for data refinement, and the construction of a structured data frame. The proposed framework aims to build upon existing research, addressing challenges identified in prior works and contributing to the advancement of effective SMS spam detection systems.

Journal of Advances in Mathematics and Computer SciencJournal of Advances in Mathematics and Computer Science, 2023

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

Procedia Computer Science, 2024

In the digital age, the ubiquity of text messaging has unfortunately paved the way for SMS phishing, or 'smishing,' a deceptive practice where fraudsters dispatch fraudulent messages to extract sensitive information from unsuspecting recipients. This issue is not trivial. It represents a significant threat to both personal privacy and organizational security, leading to potential data breaches and financial repercussions. Against this backdrop, the imperative for advanced detection strategies is undeniable. This survey leverages a systematic review methodology to assess the effectiveness of Natural Language Processing (NLP) and Machine Learning (ML) techniques in the detection of SMS phishing, also known as smishing. By methodically analyzing research spanning various detection strategies, the study illuminates the evolution from basic rule-based frameworks to sophisticated ML algorithms, enriched with NLP for deep analysis. The findings underscore the superior efficacy of combining ML classifiers with NLP, particularly through the deployment of advanced deep learning models like Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), which offer unprecedented accuracy in identifying and thwarting complex smishing attacks. The value of this study lies in its comprehensive synthesis of current methodologies and its contribution to the ongoing enhancement of cybersecurity defenses. It serves as a crucial guide for future research directions, emphasizing the necessity of adopting and innovating cutting-edge NLP and ML techniques to stay ahead of evolving digital threats.

Transactions on Machine Learning and Artificial Intelligence, 2019

This In the past years, spammers have focused their attention on sending spam through short messages services (SMS) to mobile users. They have had some success because of the lack of appropriate tools to deal with this issue. This paper is dedicated to review and study the relative strengths of various emerging technologies to detect spam messages sent to mobile devices. Machine Learning methods and topic modelling techniques have been remarkably effective in classifying spam SMS. Detecting SMS spam suffers from a lack of the availability of SMS dataset and a few numbers of features in SMS. Various features extracted and dataset used by the researchers with some related issues also discussed. The most important measurements used by the researchers to evaluate the performance of these techniques were based on their recall, precision, accuracies and CAP Curve. In this review, the performance achieved by machine learning algorithms was compared, and we found that Naive Bayes and SVM produce effective performance.

Short Message Service (SMS) is becoming the secure medium of communication due to large-scale global coverage, reliability, and power efficiency. As person--to--person (P2P) messaging is less secure than application-to-person (A2P) messaging, anyone can send a message, leading to the attack. Attackers mistreat this opportunity to spread malicious content, perform harmful activities, and abuse other people, commonly known as spam. Moreover, such messages can waste a lot of time, and important messages are sometimes overlooked. As a result, accurate spam detection in SMS and its computational time are burning issues. In this paper, we conduct six different experiments to detect SMS spam from the dataset of 5574 messages using machine learning classifiers such as Multinomial Naïve Bayes (MNB) and Support Vector Machine (SVM), considering variations of \textit{Term Frequency-- Inverse Document Frequency (TF--IDF)} features for exploring the trade-off among accuracy, F1-score and computa...

2020

Millions of shillings are lost by mobile phone users every year in Kenya due to SMs Spam, a social engineering skill attempting to obtain sensitive information such as passwords, Personal identification numbers and other details by masquerading as a trustworthy entity in an electronic commerce. The design of efficient fraud detection algorithm and techniques is key to reducing these losses. Fraud detection using machine learning is a new approach of detecting fraud especially in Mobile commerce. The design of fraud detection techniques in a mobile platform is challenging due to the non-stationary distribution of the data. Most machine learning techniques especially in SMs Spam deal with one language. It is in this background that the study will focus on a client side SMs Spam detection in Kenya’s mobile using machine learning. Naive’s Bayes algorithm was used for this purpose because it is highly scalable in text classification. The contributors of Corpus include mobile service prov...

International Journal of Scientific & Technology Research, 2020

The development of the cell phone clients has prompted a sensational increment in SMS spam messages. Despite the fact that in many parts of the world, versatile informing channel is right now viewed as "spotless" and trusted, on the complexity ongoing reports obviously show that the volume of cell phone spam is drastically expanding step by step. It is a developing mishap particularly in the Middle East and Asia. SMS spam separating is a similarly late errand to arrangement such an issue. It acquires numerous worries and convenient solutions from SMS spam separating. Anyway it fronts its own specific issues and issues. This paper moves to deal with the undertaking of sifting versatile messages as Ham or Spam for the Indian Users by adding Indian messages to the overall accessible SMS dataset. The paper examinations distinctive machine learning classifiers on vast corpus of SMS messages for individuals.