SMS Spam Detection using Supervised Learning (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.

Intelligent spam classification for mobile text message

Computer Science and Network …, 2011

This paper analyses the methods of intelligent spam filtering techniques in the SMS (Short Message Service) text paradigm, in the context of mobile text message spam. The unique characteristics of the SMS contents are indicative of the fact that all approaches may not be equally effective or efficient. This paper compares some of the popular spam filtering techniques on a publically available SMS spam corpus, to identify the methods that work best in the SMS text context. This can give hints on optimized spam detection for mobile text messages.

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.

Mobile SMS Spam Detection using Machine Learning Techniques

Journal of emerging technologies and innovative research, 2018

Spam SMS be unwanted messages to users, which be worrying and from time to time damaging. present be a group of survey papers available on SMS spam detection techniques. study and reviewed their used techniques, approaches and algorithms, their advantages and disadvantages, evaluation measures, discussion on datasets as well as lastly end result judgment of the studies. even though, the SMS spam detection techniques are additional demanding than SMS spam detection techniques since of the local contents, use of shortened words, unluckily not any of the existing research addresses these challenges. There is a enormous scope of upcoming research in this region and this survey can act as a reference point for the upcoming direction of research.

Content Based SMS Spam Detection

Over recent years, as the popularity of mobile phone devices has increased, Short Message Service (SMS) has grown into a multi-billion dollars industry. At the same time, reduction in the cost of messaging services has resulted in growth in unsolicited commercial advertisements (spams) being sent to mobile phones. SMS spamming is an activity of sending 'unwanted messages' through text messaging or other communication services; normally using mobile phones. The SMS spam problem can be approached with legal, economic or technical measures. Nowadays there are many methods for SMS spam detection, ranging from the list-based, statistical algorithm, IP-based and using machine learning. However, an optimum method for SMS spam detection is difficult to find due to issues of SMS length, battery and memory performances. A database of real SMS Spams from UCI Machine Learning repository is used, and after preprocessing and feature extraction, different machine learning techniques are applied to the database. Among the wide range of technical measures, Bayesian filters are playing a key role in stopping sms spam. Here, we analyze to what extent Bayesian filtering techniques can be applied to the problem of detecting and stopping mobile spam. In particular, we have built SMS spam test collections of significant size in English. We have tested on them a number of messages representation techniques and Machine Learning algorithms, in terms of effectiveness. The effectiveness of the proposed features is empirically validated using multiple classification methods. The results demonstrate that the proposed features can improve the performance of SMS spam detection.

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.

Towards SMS Spam Filtering: Results under a New Dataset

International Journal of Information Security Science, 2013

The growth of mobile phone users has lead to a dramatic increasing of SMS spam messages. Recent reports clearly indicate that the volume of mobile phone spam is dramatically increasing year by year. In practice, fighting such plague 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. Probably, one of the major concerns in academic settings is the scarcity of public SMS spam datasets, that are sorely needed for validation and comparison of different classifiers. Moreover, traditional content-based filters may have their performance seriously degraded since SMS messages are fairly short and their text is generally rife with idioms and abbreviations. In this paper, we present details about a new real, public and non-encoded SMS spam collection that is the largest one as far as we know. Moreover, we offer a comprehensive analysis of such dataset in order to ensure that there are no duplicated messages coming from previously existing datasets, since it may ease the task of learning SMS spam classifiers and could compromise the evaluation of methods. Additionally, we compare the performance achieved by several established machine learning techniques. In summary, the results indicate that the procedure followed to build the collection does not lead to near-duplicates and, regarding the classifiers, the Support Vector Machines outperforms other evaluated techniques and, hence, it can be used as a good baseline for further comparison.

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 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...