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A New Hybrid Approach of K-Nearest Neighbors Algorithm with Particle Swarm Optimization for E-Mail Spam Detection
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Atefeh Mortazavi   , Farhad Soleimanian Gharehchopogh * |
| Department of Computer Engineering, Urmia Branch, Islamic Azad University, Urmia, IRAN |
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Abstract: (2736 Views) |
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. |
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| Keywords: K-Nearest Neighbor optimization, Particle Swarm Optimization Algorithm, E-mail Spam, Spam Detection, Optimization |
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Full-Text [PDF 4168 kb]
(486 Downloads)
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Type of Study: Research Article |
Subject:
Special
Received: 2019/08/2 | Accepted: 2020/01/22 | Published: 2020/03/18
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