Spam filtering by using Genetic based Feature Selection

kalaibar, Sorayya mirzapour; Razavi, Seyed Naser

doi:10.7753/ijcatr0312.1018

Cited by 4 publications

(1 citation statement)

References 12 publications

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“…The embedded method is used to incorporate the filter method with the wrapper method. Examples of the embedded methods are weighted Naïve Bayes and artificial neural networks [6,[16][17][18][19][20][21][22][23][24][25].…”

Section: Introductionmentioning

confidence: 99%

Email Filtering Using Hybrid Feature Selection Model

Mohammad¹,

Smadi²,

Alwada’n³

2022

Computer Modeling in Engineering &Amp; Sciences

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Undoubtedly, spam is a serious problem, and the number of spam emails is increased rapidly. Besides, the massive number of spam emails prompts the need for spam detection techniques. Several methods and algorithms are used for spam filtering. Also, some emergent spam detection techniques use machine learning methods and feature extraction. Some methods and algorithms have been introduced for spam detecting and filtering. This research proposes two models for spam detection and feature selection. The first model is evaluated with the email spam classification dataset, which is based on reducing the number of keywords to its minimum. The results of this model are promising and highly acceptable. The second proposed model is based on creating features for spam detection as a first stage. Then, the number of features is reduced using three well-known metaheuristic algorithms at the second stage. The algorithms used in the second model are Artificial Bee Colony (ABC), Ant Colony Optimization (ACO), and Particle Swarm Optimization (PSO), and these three algorithms are adapted to fit the proposed model. Also, the authors give it the names AABC, AACO, and APSO, respectively. The dataset used for the evaluation of this model is Enron. Finally, well-known criteria are used for the evaluation purposes of this model, such as true positive, false positive, false negative, precision, recall, and F-Measure. The outcomes of the second proposed model are highly significant compared to the first one.

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Section: Introductionmentioning

confidence: 99%

Email Filtering Using Hybrid Feature Selection Model

Mohammad¹,

Smadi²,

Alwada’n³

2022

Computer Modeling in Engineering &Amp; Sciences

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Empirical Study of Feature Selection Methods for High Dimensional Data

Deepalakshmi¹,

Velmurugan²

2016

Indian Journal of Science and Technology

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Advances in spam detection for email spam, web spam, social network spam, and review spam: ML-based and nature-inspired-based techniques

Akinyelu

2021

JCS

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Despite the great advances in spam detection, spam remains a major problem that has affected the global economy enormously. Spam attacks are popularly perpetrated through different digital platforms with a large electronic audience, such as emails, microblogging websites (e.g. Twitter), social networks (e.g. Facebook), and review sites (e.g. Amazon). Different spam detection solutions have been proposed in the literature, however, Machine Learning (ML) based solutions are one of the most effective. Nevertheless, most ML algorithms have computational complexity problem, thus some studies introduced Nature Inspired (NI) algorithms to further improve the speed and generalization performance of ML algorithms. This study presents a survey of recent ML-based and NI-based spam detection techniques to empower the research community with information that is suitable for designing effective spam filtering systems for emails, social networks, microblogging, and review websites. The recent success and prevalence of deep learning show that it can be used to solve spam detection problems. Moreover, the availability of large-scale spam datasets makes deep learning and big data solutions (such as Mahout) very suitable for spam detection. Few studies explored deep learning algorithms and big data solutions for spam detection. Besides, most of the datasets used in the literature are either small or synthetically created. Therefore, future studies can consider exploring big data solutions, big datasets, and deep learning algorithms for building efficient spam detection techniques.

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Spam filtering by using Genetic based Feature Selection

Cited by 4 publications

References 12 publications

Email Filtering Using Hybrid Feature Selection Model

Email Filtering Using Hybrid Feature Selection Model

Empirical Study of Feature Selection Methods for High Dimensional Data

Advances in spam detection for email spam, web spam, social network spam, and review spam: ML-based and nature-inspired-based techniques

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