Classifying Spam Messages Research Articles

Improving Arabic Spam classification in social media using hyperparameters tuning and Particle Swarm Optimization

Online social networks continue to evolve, serving a variety of purposes, such as sharing educational content, chatting, making friends and followers, sharing news, and playing online games. However, the widespread flow of unwanted messages poses significant problems, including reducing online user interaction time, extremist views, reducing the quality of information, especially in the educational field. The use of coordinated automated accounts or robots on social networking sites is a common tactic for spreading unwanted messages, rumors, fake news, and false testimonies for mass communication or targeted users. Since users (especially in the educational field) receive many messages through social media, they often fail to recognize the content of unwanted messages, which may contain harmful links, malicious programs, fake accounts, false reports, and misleading opinions. Therefore, it is vital to regulate and classify disturbing texts to enhance the security of social media. This study focuses on building an Arabic disturbing message dataset extracted from Twitter, which consists of 14,250 tweets. Our proposed methodology includes applying new tag identification technology to collected tweets. Then, we use prevailing machine learning algorithms to build a model for classifying disturbing messages in Arabic, using effective parameter tuning methods to obtain the most suitable parameters for each algorithm. In addition, we use particle swarm optimization to identify the most relevant features to improve the classification performance. The results indicate a clear improvement in the classification performance from 0.9822 to 0.98875, with a 50% reduction in the feature set. Our study focuses on Arabic spam messages, classifying spam messages, tuning effective parameters, and selecting features as key areas of investigation.

A COMPARISON OF DIFFERENT KERNEL FUNCTIONS OF SVM CLASSIFICATION METHOD FOR SPAM DETECTION

Today, the use of e-mail, especially for formal online communication, is still often done. There is one common problem faced by e-mail users, which is the frequent receiving of spam messages. Spam messages are generally in the form of advertising or promotional messages in bulk to everyone. Of course this will cause inconvenience for people who receive the SPAM message. SPAM e-mails can be interpreted as junk messages or junk mail. So that spam has the nature of sending electronic messages repeatedly to the owner of the e-mail. This is abuse of the messaging system. One way to solve the spam problem is to identify spam messages for automatic message filtering. Several machine learning based methods are used to classify spam messages. In this study, a comparison was made between several kernel functions (i.e., linear, degree 1 polynomial, degree 2 polynomial, degree 3 polynomial, and RBF) of the SVM method to get the best SVM model in identifying spam messages. The evaluation results based on the Kaggle 1100 dataset showed that the best model were the SVM model with a linear kernel function and a degree 1 polynomial, where both models returned Precision = 0.99, Recall = 0.99, and F1-Score = 0.98. On the other hand, the RBF kernel produced lower performance in terms of Precision, Recall, and F1-Score of 0.95, 0.95, and 0.94, respectively.

Detecting Spam Vietnamese Email

Spam messages have been causing much impact on email users. There are many different techniques and measures applied to detect and classify spam messages with other emails. Filters have been installed and configured to detect spam emails based on their identifying characteristics. However, relying only on filters is very easy to miss spam emails because spammers often try to use techniques to bypass the filter's control. The most effective approach in spam detection today is to find ways to analyze the content of emails. However, it is recognized that each natural language will have different analytical and identification characteristics, so it is not possible to use the same technique or method for all languages. In this paper, we will present a method to detect Vietnamese email based on the process of analyzing email content in both HTML and image formats.

Designing Spam Model- Classification Analysis using Decision Trees

spam has diluted the message pool, causing frustration so require an automatic processing of emails. This study is to construct a spam model using classification technique in data mining. To accomplish this, experiments were conducted on spam dataset downloaded from the UCI machine learning repository which was classified using a popular data mining tool called WEKA. The final classification result should be 1 if it is finally spam, otherwise, it should be 0.Email is popular mode of communication and its users are growing day by day. But, due to social networks and electronic business, most of the emails contain unsolicited bulk e-mail called spam. Several solutions have been proposed to overcome the spam problem, filtering using decision tree classifiers is the one of the most significant techniques. Machine learning classifiers, J48, J48graft and Simple CART were used for classifying spam messages from e-mail. These trees are induced first and then prune sub trees to improve classification accuracy and size of tree. It helps to reduce size, complexity and to achieve better predictive accuracy of final classifier. Grafting is then applied as a post process to an inferred decision tree. Results showed that J48graft had pretty good prediction accuracy as compared to CART and J48 algorithms.

SPAM CLASSIFICATION BASED ON SUPERVISED LEARNING USING MACHINE LEARNING TECHNIQUES

E-mail is one of the most popular and frequently used ways of communication due to its worldwide accessibility, relatively fast message transfer, and low sending cost. The flaws in the e-mail protocols and the increasing amount of electronic business and financial transactions directly contribute to the increase in e-mail-based threats. Email spam is one of the major problems of the today's Internet, bringing financial damage to companies and annoying individual users. Spam emails are invading users without their consent and filling their mail boxes. They consume more network capacity as well as time in checking and deleting spam mails. The vast majority of Internet users are outspoken in their disdain for spam, although enough of them respond to commercial offers that spam remains a viable source of income to spammers. While most of the users want to do right think to avoid and get rid of spam, they need clear and simple guidelines on how to behave. In spite of all the measures taken to eliminate spam, they are not yet eradicated. Also when the counter measures are over sensitive, even legitimate emails will be eliminated. Among the approaches developed to stop spam, filtering is the one of the most important technique. Many researches in spam filtering have been centered on the more sophisticated classifier-related issues. In recent days, Machine learning for spam classification is an important research issue. The effectiveness of the proposed work is explores and identifies the use of different learning algorithms for classifying spam messages from e-mail. A comparative analysis among the algorithms has also been presented.