Mobile SMS Spam Detection using Machine Learning Techniques (original) (raw)

SMS Spam Detection Using Machine Learning

Background: The number of people using mobile phones is increasing; hence, the SMS messages are also increasing day by day. Correspondingly, SMS spam messages and spam email are also increasing, as is SMS spam detection, such as limited message size, use of local and shortcut words, and incomplete slogan information. These challenges need to be solved. Objectives: Efficient spam detection is an important tool in order to help people classify whether something is spam or not. In order to construct a model that is capable of distinguishing between legitimate and malicious Android applications, it provides a systematic approach to managing safety risks in operations. Methods: In this paper, we applied various machine learning techniques for SMS spam detection using the SVM algorithm. And Naïve Bayes has many more algorithms and preprocessing methods used for used datasets for the detection of SMS spam. Statistical Analysis: The SVM algorithm and CNN algorithm are used for predicting the detection of spam in the given datasets in the model, as well as information about spammers. Applications: It also shows the details of the spammers whose spam messages were sent to end users. Improvements: The paper ends with implications and suggestions for future research and more datasets are processed to get better spam details and information.

A Review on Various Approaches on Spam Detection of Mobile Phone SMS

A Review on Various Approaches on Spam Detection of Mobile Phone SMS, 2023

Spam and ham SMS detection in mobile phones, this paper presents review of spam Due to the availability of inexpensive bulk SMS bundles and the fact that messages elicit greater response rates due to the one-on-one and personalized nature of the service, they are a modern problem. In this study, to differentiate the messages, we will that will be classified into two categories spam and ham. Dataset of messages that contain whether or not the records are authentic messages is indicated by the text of SMS messages at the side of the label. Spam is defined as a dataset that includes SMS message text content and a label designating it as junk mail. In SMS spam messages, marketers use SMS text messages to send unwanted advertisements to specific clients. To get around this, we use the SMS spam dataset to compare the machine learning methods used to detect spam and non-spam messages and to determine the accuracy threshold.

SMS Spam Filtering Using Machine Learning Techniques: A Survey

2016

Objective: To report a review of various machine learning and hybrid algorithms for detecting SMS spam messages and comparing them according to accuracy criterion. Data sources: Original articles written in English found in Sciencedirect.com, Google-scholar.com, Search.com, IEEE explorer, and the ACM library. Study selection: Those articles dealing with machine learning and hybrid approaches for SMS spam filtering. Data extraction: Many articles extracted by searching a predefined string and the outcome was reviewed by one author and checked by the second. The primary paper was reviewed and edited by the third author. Results: A total of 44 articles were selected which were concerned machine learning and hybrid methods for detecting SMS spam messages. 28 methods and algorithms were extracted from these papers and studied and finally 15 algorithms among them have been compared in one table according to their accuracy, strengths, and weaknesses in detecting spam messages of the Tiago ...

A Comparative Analysis of SMS Spam Detection Employing Machine Learning Methods

IEEE, 2022

In recent times, the increment of mobile phone usage has resulted in a huge number of spam messages. Spammers continuously apply more and more new tricks that cause managing or preventing spam messages a challenging task. The aim of this study is to detect spam message to prevent different cybercrimes as spam messages have become a security threat nowadays. In this paper, we contributed to previous studies on SMS spam problems to perform a better accuracy using several different techniques such as Support Vector Machine, K-Nearest Neighbor, Naïve Bayes, Random Forest, Logistic Regression and some more. Our result indicated that Support Vector Machine achieved the highest accuracy of 99%, indicating it might be useful as an effective machine learning system for future research.

Spam Detection In Sms Using Machine Learning Through Text Mining

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.

SMS Spam Detection using Supervised Learning

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

A Novel Approach to Detect Spam and Smishing SMS using Machine Learning Techniques

International Journal of E-Services and Mobile Applications, 2020

Smishing attack is generally performed by sending a fake short message service (SMS) that contains a link of the malicious webpage or application. Smishing messages are the subclass of spam SMS and these are more harmful compared to spam messages. There are various solutions available to detect the spam messages. However, no existing solution, filters the smishing message from the spam message. Therefore, this article presents a novel method to filter smishing message from spam message. The proposed approach is divided into two phases. The first phase filters the spam messages and ham messages. The second phase filters smishing messages from spam messages. The performance of the proposed method is evaluated on various machine learning classifiers using the dataset of ham and spam messages. The simulation results indicate that the proposed approach can detect spam messages with the accuracy of 94.9% and it can filter smishing messages with the accuracy of 96% on neural network classi...

SMS Spam Detection Framework Using Machine Learning Algorithms and Neural Networks

IJCSMC, 2021

In our current generation we are very much habituated to many mobile services like communication, ecommerce etc. In mobile communication services SMS's (Short Message Service's) are very common and important services which we are using in personal purposes and profession. In these services some messages may cause spam attacks which is trap to users to access their personal information or attracting them to purchase a product from unauthorized websites. It is very easy for companies send any information or service or alert to their customers/users with these SMS API's. Based on these services it is also possible for sending spam messages. So in this system we are using advance Machine Learning concepts for detection of the spam filtering in the SMS's. In this system we are importing the dataset from UCI repository and for spam SMS detection we implementing machine learning classifiers like Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Neural Networks (NN) algorithms and with their metrics like accuracy, precision, recall and f-score. We calculate performances between there algorithms as well as we show the experiment results with visualization techniques and analyses which algorithm is best for spam SMS detection.

Machine Learning Sms Spam Detection Model

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...

A Survey of Emerging Techniques in Detecting SMS Spam

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.