A New Feature Selection in Email Spam Detection by Particle Swarm Optimization and Fruit Fly Optimization Algorithms

Computer and Knowledge Engineering

مقاله 5، دوره 2، شماره 2 - شماره پیاپی 4، آبان 2020، صفحه 49-62   

نوع مقاله: Machine Learning - Monsefi

شناسه دیجیتال (DOI): 10.22067/cke.v2i2.81750

نویسندگان

Farhad Soleimanian Gharehchopogh^{* 1}؛ Seyyed Keyvan Mousavi²

¹Urmia Branch, Islamic Azad University, Urmia, IRAN

²Department of Computer Engineering, Urmia Branch, Islamic Azad University, Urmia, IRAN

چکیده

With the advent of the internet, along with email, and social networking, there are some new issues that have caused vulnerability of users against attackers. Internet users face a lot of undesirable emails and their data privacy and security is in danger. Spammers are often sent to users by intruders and sales markets, and most of the time they target spam, harassment, and abuse of user data. With increasing attacks on computer networks, attempts to rebuild computer networks and detect spam emails are important. Hackers use the identities of users by obtaining their personal information and account of users for malicious and subversive actions. Intruders are attempting to expose, remove, or change user information by opening encrypted information. Therefore, it is very important to detect spam in the early stages. In this paper, a new approach is proposed based on a hybridization of Particle Swarm Optimization (PSO) with Fruit Fly Optimization (FFO) to email spam detection. This paper shows a Feature Selection (FS) based on PSO, which decreases dimensionality and improves the accuracy of email spam classification. The PSO searches the feature space for the best feature subsets. Experiments results on the public spambase dataset show that the accuracy of the proposed model is 92.21%, which is better in comparison with others models, such as PSO, Genetic Algorithm (GA), and Ant Colony Optimization (ACO).
 

کلیدواژه‌ها

Email spam detection؛ Feature selection؛ Particle swarm optimization؛ Fruit fly optimization

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