Adaptive intelligent learning approach based on visual anti-spam email model for multi-natural language

Spam electronic mails (emails) refer to harmful and unwanted commercial emails sent to corporate bodies or individuals to cause harm. Even though such mails are often used for advertising services and products, they sometimes contain links to malware or phishing hosting websites through which privat...

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Autores principales:	Mohammed Mazin Abed, Ibrahim Dheyaa Ahmed, Salman Akbal Omran
Formato:	article
Lenguaje:	EN
Publicado:	De Gruyter 2021
Materias:	anti-spam detection machine learning techniques adaptive intelligent learning multi-natural language multi-agent system naive bayes classifier Science Q Electronic computers. Computer science QA75.5-76.95
Acceso en línea:	https://doaj.org/article/7a6d013e88eb4113863ac02f76722219
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spelling	oai:doaj.org-article:7a6d013e88eb4113863ac02f767222192021-12-05T14:10:51ZAdaptive intelligent learning approach based on visual anti-spam email model for multi-natural language2191-026X10.1515/jisys-2021-0045https://doaj.org/article/7a6d013e88eb4113863ac02f767222192021-06-01T00:00:00Zhttps://doi.org/10.1515/jisys-2021-0045https://doaj.org/toc/2191-026XSpam electronic mails (emails) refer to harmful and unwanted commercial emails sent to corporate bodies or individuals to cause harm. Even though such mails are often used for advertising services and products, they sometimes contain links to malware or phishing hosting websites through which private information can be stolen. This study shows how the adaptive intelligent learning approach, based on the visual anti-spam model for multi-natural language, can be used to detect abnormal situations effectively. The application of this approach is for spam filtering. With adaptive intelligent learning, high performance is achieved alongside a low false detection rate. There are three main phases through which the approach functions intelligently to ascertain if an email is legitimate based on the knowledge that has been gathered previously during the course of training. The proposed approach includes two models to identify the phishing emails. The first model has proposed to identify the type of the language. New trainable model based on Naive Bayes classifier has also been proposed. The proposed model is trained on three types of languages (Arabic, English and Chinese) and the trained model has used to identify the language type and use the label for the next model. The second model has been built by using two classes (phishing and normal email for each language) as a training data. The second trained model (Naive Bayes classifier) has been applied to identify the phishing emails as a final decision for the proposed approach. The proposed strategy is implemented using the Java environments and JADE agent platform. The testing of the performance of the AIA learning model involved the use of a dataset that is made up of 2,000 emails, and the results proved the efficiency of the model in accurately detecting and filtering a wide range of spam emails. The results of our study suggest that the Naive Bayes classifier performed ideally when tested on a database that has the biggest estimate (having a general accuracy of 98.4%, false positive rate of 0.08%, and false negative rate of 2.90%). This indicates that our Naive Bayes classifier algorithm will work viably on the off chance, connected to a real-world database, which is more common but not the largest.Mohammed Mazin AbedIbrahim Dheyaa AhmedSalman Akbal OmranDe Gruyterarticleanti-spam detectionmachine learning techniquesadaptive intelligent learningmulti-natural languagemulti-agent systemnaive bayes classifierScienceQElectronic computers. Computer scienceQA75.5-76.95ENJournal of Intelligent Systems, Vol 30, Iss 1, Pp 774-792 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	anti-spam detection machine learning techniques adaptive intelligent learning multi-natural language multi-agent system naive bayes classifier Science Q Electronic computers. Computer science QA75.5-76.95
spellingShingle	anti-spam detection machine learning techniques adaptive intelligent learning multi-natural language multi-agent system naive bayes classifier Science Q Electronic computers. Computer science QA75.5-76.95 Mohammed Mazin Abed Ibrahim Dheyaa Ahmed Salman Akbal Omran Adaptive intelligent learning approach based on visual anti-spam email model for multi-natural language
description	Spam electronic mails (emails) refer to harmful and unwanted commercial emails sent to corporate bodies or individuals to cause harm. Even though such mails are often used for advertising services and products, they sometimes contain links to malware or phishing hosting websites through which private information can be stolen. This study shows how the adaptive intelligent learning approach, based on the visual anti-spam model for multi-natural language, can be used to detect abnormal situations effectively. The application of this approach is for spam filtering. With adaptive intelligent learning, high performance is achieved alongside a low false detection rate. There are three main phases through which the approach functions intelligently to ascertain if an email is legitimate based on the knowledge that has been gathered previously during the course of training. The proposed approach includes two models to identify the phishing emails. The first model has proposed to identify the type of the language. New trainable model based on Naive Bayes classifier has also been proposed. The proposed model is trained on three types of languages (Arabic, English and Chinese) and the trained model has used to identify the language type and use the label for the next model. The second model has been built by using two classes (phishing and normal email for each language) as a training data. The second trained model (Naive Bayes classifier) has been applied to identify the phishing emails as a final decision for the proposed approach. The proposed strategy is implemented using the Java environments and JADE agent platform. The testing of the performance of the AIA learning model involved the use of a dataset that is made up of 2,000 emails, and the results proved the efficiency of the model in accurately detecting and filtering a wide range of spam emails. The results of our study suggest that the Naive Bayes classifier performed ideally when tested on a database that has the biggest estimate (having a general accuracy of 98.4%, false positive rate of 0.08%, and false negative rate of 2.90%). This indicates that our Naive Bayes classifier algorithm will work viably on the off chance, connected to a real-world database, which is more common but not the largest.
format	article
author	Mohammed Mazin Abed Ibrahim Dheyaa Ahmed Salman Akbal Omran
author_facet	Mohammed Mazin Abed Ibrahim Dheyaa Ahmed Salman Akbal Omran
author_sort	Mohammed Mazin Abed
title	Adaptive intelligent learning approach based on visual anti-spam email model for multi-natural language
title_short	Adaptive intelligent learning approach based on visual anti-spam email model for multi-natural language
title_full	Adaptive intelligent learning approach based on visual anti-spam email model for multi-natural language
title_fullStr	Adaptive intelligent learning approach based on visual anti-spam email model for multi-natural language
title_full_unstemmed	Adaptive intelligent learning approach based on visual anti-spam email model for multi-natural language
title_sort	adaptive intelligent learning approach based on visual anti-spam email model for multi-natural language
publisher	De Gruyter
publishDate	2021
url	https://doaj.org/article/7a6d013e88eb4113863ac02f76722219
work_keys_str_mv	AT mohammedmazinabed adaptiveintelligentlearningapproachbasedonvisualantispamemailmodelformultinaturallanguage AT ibrahimdheyaaahmed adaptiveintelligentlearningapproachbasedonvisualantispamemailmodelformultinaturallanguage AT salmanakbalomran adaptiveintelligentlearningapproachbasedonvisualantispamemailmodelformultinaturallanguage
_version_	1718371673999147008

Adaptive intelligent learning approach based on visual anti-spam email model for multi-natural language

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