A novel approach for spam detection using horse herd optimization algorithm

Abstract

Communication via email has expanded dramatically in recent decades due to its cost-effectiveness, convenience, speed, and utility for a variety of contexts, including social, scientific, cultural, political, authentication, and advertising applications. Spam is an email sent to a large number of individuals or organizations without the recipient's desire or request. It is increasingly becoming a harmful part of email traffic and can negatively affect the usability of email systems. Such emails consume network bandwidth as well as storage space, causing email systems to slow down, wasting time and effort scanning and eliminating enormous amounts of useless information. Spam is also used for distributing offensive and harmful content on the Internet. The objective of the current study was to develop a new method for email spam detection with high accuracy and a low error rate. There are several methods to recognize, detect, filter, categorize, and delete spam emails, and almost the majority of the proposed methods have some extent of error rate. None of the spam detection techniques, despite the optimizations performed, have been effective alone. A step in text mining and message classification is feature selection, and one of the best approaches for feature selection is the use of metaheuristic algorithms. This article introduces a new method for detecting spam using the Horse herd metaheuristic Optimization Algorithm (HOA). First, the continuous HOA was transformed into a discrete algorithm. The inputs of the resulting algorithm then became opposition-based and then converted to multiobjective. Finally, it was used for spam detection, which is a discrete and multiobjective problem. The evaluation results indicate that the proposed method performs better compared to other methods such as K-nearest neighbours-grey wolf optimisation, K-nearest neighbours, multilayer perceptron, support vector machine, and Naive Bayesian. The results show that the new multiobjective opposition-based binary horse herd optimizer, running on the UCI data set, has been more successful in the average selection size and classification accuracy compared with other standard metaheuristic methods. According to the findings, the proposed algorithm is substantially more accurate in detecting spam emails in the data set in comparison with other similar algorithms, and it shows lower computational complexity.

Email Net: Efficient Email Classification Approach Based on Graph Similarity Measure

“…In 2022 Hosseinalipour, A. and Ghanbarzadeh, R., [14] presented a horse herd optimization algorithm for spam detection with increased accuracy and a minimum error rate. First, a discrete algorithm was created from the continuous HOA.…”

An Improved Dandelion Optimizer Algorithm for Spam Detection: Next-Generation Email Filtering System

“…On the other hand [20] introduced a spam detection method based on the Horse Herd Optimization Algorithm (HOA) with a KNN classifier. Their method gained high accuracy in detecting spam emails; nonetheless, its performance could be heavily impacted by the sensitivity of the HOA algorithm, which comes from the nature of its parameter settings.…”

An Efficient Spam Mail Classification Approach Using Improved Moth Flame Optimization and Multi-class Support Vector Machine

“…However, the efficiency of the classifier is reduced when evaluated for smaller datasets. Hosseinalipour and Ghanbarzadeh [19] have introduced horse-herd optimization algorithm (HOA) to perform an effective detection of email spam with a minimal error rate. Initially, the continuous HOA is transformed into an algorithm with discrete components and transformed into a multi-objective.…”