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Showing 3 results for Spam Detection
Atefeh Mortazavi, Dr Farhad Soleimanian Gharehchopogh,
Volume 8, Issue 1 (9-2019)
Abstract
Emails are one of the fastest economic communications. Increasing email users has caused the increase of spam in recent years. As we know, spam not only damages user’s profits, time-consuming and bandwidth, but also has become as a risk to efficiency, reliability, and security of a network. Spam developers are always trying to find ways to escape the existing filters, therefore new filters to detect spams need to be developed. Most of these filters take advantage of a combination of several methods, such as black or white lists, using keywords, rule-based filters, machine learning methods and so on, to identify spams more accurately. many approaches about email spam detection exhausted up to now. In this paper, we propose a new approach for spam detection based on Particle Swarm Optimization Algorithm and K-Nearest Neighbor optimization, and we measure performance based on Accuracy, Precision, Recall, And f-measure. The results show that the proposed approach has a better performance than other models and the basic algorithms.
Farhad Soleimanian Gharehchopogh, Mohammad Sakhidek Hovshin,
Volume 9, Issue 1 (8-2020)
Abstract
Unfortunately, among internet services, users are faced with several unwanted messages that are not even related to their interests and scope, and they contain advertising or even malicious content. Spam email contains a huge collection of infected and malicious advertising emails that harms data destroying and stealing personal information for malicious purposes. In most cases, spam emails contain malware that is usually sent to users in the form of scripts or attachments, and the user infects the computer with malware by downloading and executing the attached file. In this paper, a new model for detecting spam e-mail is proposed based on the hybrid of the Harmony Search Algorithm (HAS) with the Magnetic Optimization Algorithm (MOA). The proposed model is used to select the effective features and then the classification is performed using the K Nearest Neighbor's (KNN) algorithm. In the proposed model, using the MOA was found the best features for the HSA, and the harmony matrix is formed based on them. Then the HSA changes based on the update and rate of step-change in each step of the harmony vectors so that the best vector is selected as the vector of characteristics among them. The results show that the accuracy of the proposed model on the Spam base dataset with 200 iterations is 94.17% and also the accuracy of the diagnostic model of the proposed model is more than other models.
Dr Majid Fani, Dr Mohammadamin Torabi, Dr Matineh Moghaddam,
Volume 9, Issue 2 (2-2021)
Abstract
Not all phishing attacks are always done in the form of website forgery and telephone phishing. Emails and messages that appear to be sent by the bank and receive information from the user can also be a phishing attack. Feature selection and sample selection are two very important issues in the data processing stage in detecting malicious emails. In particular, identifying spam without data reduction will not be nearly as accurate in the results. Most articles and research have focused on one of these issues, and there are few articles that have worked in combination to detect malicious emails. Therefore, the purpose of the present study is to provide a method to reduce the data in identifying emails by selecting features and samples simultaneously. In the proposed method in this paper, the forbidden search algorithm and the genetic algorithm are used in combination and simultaneously. For the suitability of this method, the evaluation vector machine evaluation function was used. The results showed that the detection rate of spam and e-mails in LineSpam and UCI datasets was 97.28, which was the highest possible value compared to other algorithms proposed in previous studies.
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