Spam Messages Research Articles

A Comparative Study of Machine Learning Classifiers for Different Language Spam SMS Detection: Performance Evaluation and Analysis

With the continuous rise in the number of mobile device users, SMS (Short Message Service) remains a prevalent communication tool accessible on both smartphones and basic phones. Consequently, SMS traffic has experienced a significant surge. This increase has also led to a rise in spam messages, as spammers seek financial or business gains through activities like marketing promotions, lottery scams, and credit card information theft. Consequently, spam classification has become a focal point of research. In this paper, we explore the effectiveness of 11 machine learning algorithms for SMS spam detection, including multinomial Naïve Bayes, K-Nearest Neighbors (KNN), and Random Forest, among others. Utilizing datasets from UCI and Bangla SMS collections, our experimental results reveal that the multinomial Naïve Bayes algorithm surpasses previous models in spam detection, achieving accuracies of 98.65% and 89.10% in the respective datasets.

SMS Spam Detection System Based on Deep Learning Architectures for Turkish and English Messages

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.

Adoption of a Revised Text Classification Method Using the Laravel Framework

The prevalence of the Internet of Things has increased the frequency of malicious attacks on the many vulnerable devices, such as cellphones. Conventional framework design techniques for web design have a lot of limits, are time-consuming, and have other problems. The industry norm for message exchange and fusion between dissimilar systems has long been web services. We provide a machine learning-based strategy for extracting SPAM messages based on the MVC concept and Laravel. In order to solve these problems, this article offers a web design and implementation technique based on the Laravel framework. By automatically handling connections that lack business logic, Laravel standardises the development process.

MACHINE LEARNING ALGORITHMS FOR TELEGRAM SPAM FILTERING

With unprecedented usage of social media applications to interact in virtual communities, bad entities can now use these platforms to spread their malicious activities such as spam, hate speech, and even phishing to a very large population. Especially, Telegram is suitable for these kinds of activities because it is a new cloud-messenger that is highly popular among bloggers and media around the world, established by Pavel Durov in 2013. As a result, it is necessary for social media platforms to develop algorithms to filter these malicious contents. This paper employs Machine learning algorithms to filter spam messages in Telegram. Dataset obtained from Kaggle was used for the experiments in this paper. Five machine learning models were applied, namely, Extreme Gradient Boosting (XGB), Light Gradient Boosting Machine (LGBM), CatBoosting, Support Vector Machine (SVM) and K-Nearest Neighbours (KNN). Experimental results showed that SVM outperforms other machine learning models used for the study with a classification accuracy of 94%. This is an indication that SVM is a promising algorithm for Spam filtering in Telegram if adopted.

Complex-Network based model for SMS spam filtering

With the advancement of technology and the widespread use of mobile phones and wireless communication, SMS has become the most popular texting method due to its high response rate, affordability, and no internet connection requirement. A survey found that 3.5 billion users, or 80% of active users worldwide, use SMS for communication. SMS, however, has also attracted spammers, resulting in an explosion in spam messages, especially in Asia. Users are annoyed, lose money, and waste their time by receiving spam messages intended to serve various purposes, such as advertising, adult content, smishing, and fraud. Spam messages are a problem for users and providers, which calls for a mechanism to identify and filter them out. With supervised machine learning techniques, we propose a novel approach to classify spam and ham messages based on complex network theory. The proposed approach integrates complex network based features with statistical TF-IDF and grammatical rules features. Also, an under-sampling method has been employed in order to cope with the imbalanced data issue. We evaluated the performance of several supervised learners in terms of accuracy, precision, recall, F1-score, and AUC. In our experiments, Random Forest successfully classified spam messages more accurate than statistical methods that only extracted TF-IDF features.

Optimizing SMS Spam Detection Using Machine Learning: A Comparative Analysis of Ensemble and Traditional Classifiers

With the rapid rise of mobile communication, Short Message Service (SMS) has become an essential platform for transmitting information. However, the growing volume of unsolicited and harmful spam messages presents significant challenges for both users and mobile network operators. This study explores the effectiveness of various machine learning models, including Random Forest, Gradient Boosting, AdaBoost, Support Vector Machine (SVM), Logistic Regression, and an Ensemble Voting Classifier, in detecting SMS spam. A dataset containing 5,572 SMS messages, labeled as either spam or ham (legitimate), was used to evaluate these models. Hyperparameter tuning was performed on each model to optimize accuracy, and the models were assessed using metrics such as precision, recall, F1-score, and accuracy. The results indicated that the SVM and Ensemble Voting Classifier achieved the highest performance, with accuracies of 0.9857 and 0.9848, respectively. Both models demonstrated superior recall for spam messages, making them highly effective for real-world spam detection systems. While Random Forest, Gradient Boosting, and AdaBoost also performed well, their slightly lower recall for spam suggests that they may misclassify some spam as legitimate messages. The study highlights the effectiveness of machine learning models in addressing the SMS spam problem, particularly when using ensemble methods. Future research should focus on addressing class imbalance and exploring deep learning approaches to further enhance model performance. These findings offer valuable insights for developing more accurate and scalable SMS spam detection systems.

EGMA: Ensemble Learning-Based Hybrid Model Approach for Spam Detection

Spam messages have emerged as a significant issue in digital communication, adversely affecting users’ mental health, personal safety, and network resources. Traditional spam detection methods often suffer from low detection rates and high false positives, underscoring the need for more effective solutions. This paper proposes the EGMA model, an ensemble learning-based hybrid approach for spam detection in SMS messages, which integrates gated recurrent unit (GRU), multilayer perceptron (MLP), and hybrid autoencoder models utilizing a majority voting algorithm. The EGMA model enhances performance by incorporating additional statistical features extracted from message content and employing text vectorization techniques, such as Term Frequency–Inverse Document Frequency (TF-IDF) and CountVectorizer. The proposed model achieved impressive classification accuracies of 99.28% on the SMS Spam Collection dataset, 99.24% on the Email Spam dataset, 99.00% on the Enron-Spam dataset, 98.71% on the Super SMS dataset, and 95.09% on UtkMl’s Twitter Spam dataset. These results demonstrate that the EGMA model outperforms individual models and existing methods in the literature, providing a robust solution for enhancing spam detection performance and effectively mitigating the threats that spam messages pose in digital communication.

Combining the spatiotemporal mobility patterns and MMC for next location prediction of fake base stations

The spatiotemporal mobility patterns and next location prediction of fake base stations (FBS) provide important technical support for the police to prevent spam messages from FBS. However, due to the difficulty in locating their real-time locations, our understanding of the mobility patterns and predictability of FBS is still limited. Based on the crowdsourced spam data, we extract the time and potential locations of FBS and propose a Tucker-MMC method that combines Tucker decomposition with a Mobility Markov Chain (MMC) model to investigate the mobility patterns and predictability of FBS sending spam messages. First, we utilize Tucker decomposition to reflect the spatial and temporal preferences during the movement of the corresponding FBS. Then the mobility regularity and the theoretical maximum predictability of the FBS trajectories with similar mobility preferences are analyzed by entropy and Fano's inequality. A Tucker-MMC is also established for the next location prediction. The results using the spam dataset in Beijing show that the accuracy of Tucker-MMC is more than double that of the MMC. The accuracy of the actual location prediction model is more likely to approach the theoretical maximum predictability when FBS send spam messages in a shorter time, shorter transfer distance, and smaller access range.

Exploring Machine Learning Algorithms for Email Spam Filtering

Spams are irrelevant or inappropriate emails, and they are on the rise across different digital communication channels globally. These unsolicited and unwelcome perilous emails lead to adverse impacts like security risks, time and resources wastage, financial loss, reputation damage, and legal consequences. Therefore, intelligent anti-spam email filters are required to prevent such emails from being delivered to the recipient's mailbox. Artificial Intelligence and Machine Learning algorithms provide trustworthy countermeasures and affordable solutions to tackle this global problem. Machine Learning techniques like Logistics Regression, Decision Tree, Support Vector Machines, K-nearest neighbor, Naïve Bayes Classifier, and Random Forest provide solutions to classify spam emails with reasonable accuracy and precision. This research explores different approaches to identifying spam emails and developing machine-learning algorithms to filter such emails automatically. Results manifest that the Support Vector Machine and Naïve Bayes Model most accurately classified spam emails. The Support Vector Machine exhibited the best balance between high precision and recall, making it highly effective in identifying spam messages with fewer false positives.

Addressing data imbalance in neural network spam detection with insights from SMS spam collection

In cybersecurity, the persistent challenge of spam detection remains paramount. Traditional methods reliant on human scrutiny or rule-based algorithms are proving inadequate against the constantly evolving tactics employed by spammers. Machine learning emerges as a promising solution, leveraging vast datasets to swiftly and objectively discern patterns and traits within spam messages. By uncovering subtle correlations among message elements, machine learning enhances the precision and efficacy of spam detection systems, offering a dependable and economical approach to combat spam. This study aims to investigate the impact of different strategies for addressing data imbalance on neural network-based spam detection performance. Using the SMS Spam Collection Dataset, four methods for mitigating data imbalance are evaluated against an untreated scenario. Notably, despite inherent data imbalance, the unprocessed scenario exhibits the highest overall performance. Stratified sampling emerges as the most effective technique for accurately identifying spam, while SMOTE excels in preserving legitimate messages (ham) while filtering out spam. These results contribute significantly to peoples understanding of the intricate dynamics in controlling data imbalance in spam detection and offer insightful information for future studies and real-world applications.

A Novel Optimized Classification approach for Spam Message Cyber Attack Detection and Prevention using Machine Learning Approach

A Novel Optimized Classification approach for Spam Message Cyber Attack Detection and Prevention using Machine Learning Approach

SPAM EMAIL CLASSIFIER

Today, a sizable portion of people rely on freely accessible email or communications provided by strangers. Because anyone may send an email or leave a note, spammers have an excellent opportunity to write spam messages regarding our various interests. Spam overflows email inboxes with absurd emails. severely reduces the speed of our internet. stealing vital information, such as our contact information, from us. Finding these spammers and the spam content can be difficult work and a popular research area. Spam email is the act of sending many messages via postal mail. Spam is effectively postage due advertising because the recipient bears the majority of the cost. Keywords—Machine Learning(ML), Dataset, Parkinson’s Disease, Artificial Intelligence(AI), Support Vector Machine(SVM).

A novel deep learning model-based optimization algorithm for text message spam detection

A novel deep learning model-based optimization algorithm for text message spam detection

Bayesian Classification for SMS Spam Detection in Mobile Devices

Abstract: The abundance of unwanted spam messages complicates the use of Short Message Service (SMS) for efficient communication in modern times. This study investigates developing and utilizing a Naive Bayes Theorem-based Ham/Spam detection system. Because of its ease of use and effectiveness in text classification tasks, the Naive Bayes classifier is used. A collection of SMS messages labeled as” spam” or” ham” (non-spam) makes up the dataset that was used for testing and training. Preprocessing methods, including tokenization, stop-word elimination, and stemming, are employed to extract pertinent features from the text messages. The Naive Bayes classifier learns how words relate to whether they’re in a spam or non-spam message by looking at some examples from the dataset. Utilizing criteria such as accuracy, precision, and confusion matrix on a separate testing set, the classifier’s performance is evaluated. Additionally, the impact of varying parameters such as smoothing techniques and feature selection methods on the classifier’s performance is analyzed. The experimental results used to distinguishing between ham and spam messages in SMS communication

Analysis of SMS Spam Detection using Tf-Idf: A Study On SMS Spam Collection Dataset

This study explores the detection of SMS spam utilizing TF-IDF analysis on a dataset containing a collection of text messages labeled as spam or ham (non-spam). The dataset comprises messages suitable for spam detection analysis using TF-IDF techniques. The research aims to evaluate the effectiveness of TF-IDF in distinguishing between spam and spam (non-spam) messages. The analysis involves examining the precision, recall, and F1-score metrics to assess the performance of the classification model. The results demonstrate promising outcomes, with a high accuracy rate achieved in classifying spam and ham (non-spam) messages. Additionally, the study provides insights into the distribution of spam and ham (non-spam) labels in the test data, further enhancing our understanding of SMS spam detection techniques.

Enhanced MNB Method for SPAM E-mail/SMS Text Detection Using TF-IDF Vectorizer

Spam, whether in the form of SMS or email, poses significant threats by compromising user privacy and potentially leading to unauthorized access to personal data. In the era of smartphones, where users store sensitive information, the risk of cyber-attack through spam messages is heightened. This research addresses the pressing issue of spam SMS and email detection using a dataset comprising 5574 messages from reputable sources. The collection includes contributions from the National University of Singapore SMS Corpus, Grumble text Website, Caroline Tag’s PhD Theses, and SMS Spam Corpus v.0.1 Big. With a meticulous approach encompassing data cleaning, balancing, preprocessing, and exploratory data analysis, the research employs the TF-IDF (Term Frequency and Inverse Document Frequency) vectorizer to enhance the model’s ability to capture the importance of individual words. This foundational work sets the stage for evaluating various machine learning models, including Support Vector Machine, Multinomial Naïve Bayes, Decision Tree, Logistic Regression, Random Forest, AdaBoost, K-Nearest Neighbors, XGBoost, Gradient Boost Classifier, Bagging Classifier, and Extra Tree Classifier. Notably, the Multinomial Naïve Bayes model emerges as a standout performer with 100% accuracy and 97% precision in phishing detection. The research introduces an intuitive user interface, facilitating real-time interactivity for model assessment and offering valuable insights for cybersecurity applications. The study contributes to the advancement of robust cybersecurity systems, emphasizing precision and accuracy in spam SMS and email text detection.

Developing the Framework Using Deep Neural Network for Detection of Spam and Fake Spam Messages in Twitter

Social media plays vital role among the user communities for social gathering, entertainment, communication, sharing knowledge so on. Twitter is one such network to connect millions of users to share information. Nowadays, there are humpteen numbers of users using social media for social engagements. Due to the fact that wide publicity of individuals and products get viral in social media, everyone wish to use social media as a platform to promote their product. Furthermore, large number of people relies on social media contents to take decisions. Twitter is one of the social media platforms to post the media contents by the user. Spammers are illegal users intrude the twitter account and send the duplicate messages to promote advertisement, phishing, scam and personal blogs etc. In this paper, a novel spam detection mechanism is introduced to detect the suspicious users on twitter. The system has been designed such a way that it initially set with semi-supervised at the tweet level and update into supervised level for learning the input tweets to detect the spammers. The proposed system will also identify the type of spammers and will also remove duplicate tweets. We have applied with multi-classifier algorithms like naïve Bayesian, K-Nearest neighbor and Random forest into twitter data set and the performance is compared. The experimental result shows very promising results.

Survey Paper on Implementation of Abusive Language Detection in Chatting Application

Communication has become stronger due to exponential increase in the usage of social media in the last few years. People use them for communicating with friends, finding new friends, updating any important activities of their life, etc. Due to their growing popularity and deep reach, these mediums are infiltrated with huge Volume of spam messages. Spam message randomly sent to multiple addressees by all sorts of groups, but mostly lazy advertisers and criminals who wish to lead you to phishing sites. In this we are using various machine learning techniques for detecting spam in the short text messages and also Google vision API for detecting spam images. Key Words: Spam Filtration, Google Vision API, OWASP, Naive baise classifier, Dictionary Based Algorithm

Detecting Malicious Twitter Bots using Machine Learning

Abstract: Twitter play a significant role in our daily lives, offering a wide range of opportunities to their users. However, Twitter and online social networks (OSNs) in general are increasingly being utilized by automated accounts, commonly known as bots, as they continue to gain immense popularity across various user demographics. Malicious twitter Bots detection is required to detect real users from fraudulent users because it leads to spreading of spam messages and engage in fraudulent activities. To overcome this, we are going to differentiate bots from legitimate users using feature extraction techniques and find malicious bots and tweets using machine learning algorithm and deep learning architecture known as VGG19 which is combined with the convolutional neural network (CNN) in order to identify whether the tweets are posted by bots or real users and also identifying malicious twitter bots along with malicious URLs. By following these techniques, we can identify the account as bots or real user and prevent spreading of malicious content in the society. The deep learning architecture combined with convolutional neural network to evaluate over a series of experiments using two large real Twitter datasets and compare the experimental results with other machine learning algorithms and provide advantages over other existing techniques like logistic regression targeting the identification of malicious users in social media.

SMS SPAM DETECTION WITH MULTINOMIAL NAIVE BAYES

SMS (short messaging service) usage has increased dramatically as a result of the growth in mobile users, enabling text messaging between smartphone and landline users. But there has also been a noticeable increase in unsolicited communications, or spam, coinciding with this growth in SMS usage. Through marketing campaigns and attempts to gain private information, such as credit card numbers, these spam messages seek to further business or financial objectives. The duty of removing spam mails has therefore grown in significance. In response, a number of deep learning and machine learning methods have been used to identify SMS spam. Using data from the University of California, Irvine (UCI), this research examines the application of such strategies. Key Words: MultinomailNaiveBayes, SMS Spam Detection, NLTK, Vectorization, Feature Extraction.