Implementing an Agent-based Multi-Natural Language Anti-Spam Model

Electronic mail; Information filtering; Learning systems; Robotics; Software agents; Visual languages; Anti spam technology; Blocking mechanisms; Business and management; JADE agent platforms; Natural languages; Security and privacy; spam; Visual information; Multi agent systems

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Bibliographic Details
Main Authors: Mohammed M.A., Gunasekaran S.S., Mostafa S.A., Mustafa A., Ghani M.K.A.
Other Authors: 57192089894
Format: Conference Paper
Published: Institute of Electrical and Electronics Engineers Inc. 2023
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spelling my.uniten.dspace-235442023-05-29T14:50:10Z Implementing an Agent-based Multi-Natural Language Anti-Spam Model Mohammed M.A. Gunasekaran S.S. Mostafa S.A. Mustafa A. Ghani M.K.A. 57192089894 55652730500 37036085800 57200530694 24491611800 Electronic mail; Information filtering; Learning systems; Robotics; Software agents; Visual languages; Anti spam technology; Blocking mechanisms; Business and management; JADE agent platforms; Natural languages; Security and privacy; spam; Visual information; Multi agent systems The spam is a negative practice of illegitimate use to the email services through unsolicited email such as phishing for scam practices which affects the email reliability. Spam problems and its influence on the society have been investigated and discussed from different perspectives. Several studies have looked into the influence of the spam on the economy, financial, marketing, business and management, while others deliberate the impact of the spam on the security and privacy. Subsequently, there are different anti-spam techniques that have spam filtering or blocking mechanisms. This work attempts to investigate an available anti-spam technology and highlight the possible improvements. Consequently, it constructs a new agent-based anti-spam model that can overcome some existing limitations. The Multi-Natural Language Anti-Spam (MNLAS) model comprises visual information, and texts of an email in the spam filtering process. The MNLAS is implemented in a Java environment using Jade agent platform. The application detects and filters spam emails of different types using a dataset of 200 emails. � 2018 IEEE. Final 2023-05-29T06:50:10Z 2023-05-29T06:50:10Z 2018 Conference Paper 10.1109/ISAMSR.2018.8540555 2-s2.0-85059771560 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85059771560&doi=10.1109%2fISAMSR.2018.8540555&partnerID=40&md5=77a2ba92b7802e902ab4c878c604e227 https://irepository.uniten.edu.my/handle/123456789/23544 8540555 Institute of Electrical and Electronics Engineers Inc. Scopus
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
description Electronic mail; Information filtering; Learning systems; Robotics; Software agents; Visual languages; Anti spam technology; Blocking mechanisms; Business and management; JADE agent platforms; Natural languages; Security and privacy; spam; Visual information; Multi agent systems
author2 57192089894
author_facet 57192089894
Mohammed M.A.
Gunasekaran S.S.
Mostafa S.A.
Mustafa A.
Ghani M.K.A.
format Conference Paper
author Mohammed M.A.
Gunasekaran S.S.
Mostafa S.A.
Mustafa A.
Ghani M.K.A.
spellingShingle Mohammed M.A.
Gunasekaran S.S.
Mostafa S.A.
Mustafa A.
Ghani M.K.A.
Implementing an Agent-based Multi-Natural Language Anti-Spam Model
author_sort Mohammed M.A.
title Implementing an Agent-based Multi-Natural Language Anti-Spam Model
title_short Implementing an Agent-based Multi-Natural Language Anti-Spam Model
title_full Implementing an Agent-based Multi-Natural Language Anti-Spam Model
title_fullStr Implementing an Agent-based Multi-Natural Language Anti-Spam Model
title_full_unstemmed Implementing an Agent-based Multi-Natural Language Anti-Spam Model
title_sort implementing an agent-based multi-natural language anti-spam model
publisher Institute of Electrical and Electronics Engineers Inc.
publishDate 2023
_version_ 1806423996719169536
score 13.211869