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Effective Feature Selection-Based Meta-heuristics Optimization Approach for Spam Detection

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Abstract

According to studies of international monetary data, the sum of money lost due to fraud in all transactions around the world keeps rising. In particular, the adoption of the Bank-as-a-Service model by financial institutions will place additional strain on payment processing infrastructure and exacerbate the already serious problem of payment fraud. These communications carried more than just beneficial information: contain bogus content, phishing mails, viruses, annoying reviews, and more. Spammers, or spam users, are driving this growth every year. These scammers frequently make attractive-looking bogus accounts. Spam detection has become an essential demand currently, considered that spam has become a significant problem. Online reviews about product quality influence bank, particularly when it comes to merchandise. Some people use this as an attempt to spam the transaction, upgrading or degrading the user account. For this reason, the identification of these evaluations is crucial to ensure the interests of customers are preserved. Research on spam reviews and their detection has been undertaken by several researchers in order to help both customers and bank. In addition, the limited number of spam detection results in a data imbalance problem. Further, comparing reviews requires expensive computing. Studying how to identify spam detection and spam activity is a hot topic, and while numerous experiments have been done in this area, so far, there is no way that can recognize spam feedback or show the worth of the derived function types. To better detect spam in social media text, in this research, an effective feature selection-based meta-heuristics optimization (FS-MHO) using machine learning is proposed for accurate feature extraction and selection for further processing of online payment fraud detection. The proposed model achieves 97% accuracy in detecting relevant features to train the model for accurate online fraud prediction.

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  1. Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation (K.L.E.F), Aziz Nagar, Hyderabad, India

    P. Swetha & D. S. Rao

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  1. P. Swetha
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This article is part of the topical collection “Research Trends in Computational Intelligence” guest edited by Anshul Verma, Pradeepika Verma, Vivek Kumar Singh and S. Karthikeyan.

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Swetha, P., Rao, D.S. Effective Feature Selection-Based Meta-heuristics Optimization Approach for Spam Detection. SN COMPUT. SCI. 4, 681 (2023). https://doi.org/10.1007/s42979-023-02126-z

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