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Using machine learning to deal with Phishing and Spam Detection: An overview

Published: 18 May 2020 Publication History

Abstract

Cybersecurity is a growing field that requires a lot of attention due to the remarkable progress made in social networks, cloud and web technologies, online banking, mobile environment, smart networks, etc. Various approaches have been developed to solve many computer security problems, including those based on machine learning. This paper examines and highlights the various works using machine learning in network security. Two types of detection are discussed. Phishing detection and Spam detection. For each type, related work is presented and some proposed methods in the literature are compared taking into account their accuracy and other characteristics.

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Cited By

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  • (2024)Machine Learning-Based Solution for SMS Spam Detection Problem2024 Intelligent Methods, Systems, and Applications (IMSA)10.1109/IMSA61967.2024.10652878(235-242)Online publication date: 13-Jul-2024
  • (2023)A Feature Extraction Approach for the Detection of Phishing Websites Using Machine LearningJournal of Circuits, Systems and Computers10.1142/S021812662450031233:02Online publication date: 3-Aug-2023
  • (2022)Using Decision Tree Algorithms in Detecting Spam Emails Written in Malay: A Comparison StudyITM Web of Conferences10.1051/itmconf/2022420100142(01001)Online publication date: 24-Feb-2022
  • Show More Cited By

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cover image ACM Other conferences
NISS '20: Proceedings of the 3rd International Conference on Networking, Information Systems & Security
March 2020
528 pages
ISBN:9781450376341
DOI:10.1145/3386723
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 18 May 2020

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Author Tags

  1. Cyber Security
  2. Machine Learning
  3. Network Security
  4. Phishing detection
  5. Spam detection

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Cited By

View all
  • (2024)Machine Learning-Based Solution for SMS Spam Detection Problem2024 Intelligent Methods, Systems, and Applications (IMSA)10.1109/IMSA61967.2024.10652878(235-242)Online publication date: 13-Jul-2024
  • (2023)A Feature Extraction Approach for the Detection of Phishing Websites Using Machine LearningJournal of Circuits, Systems and Computers10.1142/S021812662450031233:02Online publication date: 3-Aug-2023
  • (2022)Using Decision Tree Algorithms in Detecting Spam Emails Written in Malay: A Comparison StudyITM Web of Conferences10.1051/itmconf/2022420100142(01001)Online publication date: 24-Feb-2022
  • (2022)A Collaborative Abstraction Based Email Spam Filtering with FingerprintsWireless Personal Communications: An International Journal10.1007/s11277-021-09221-5123:2(1913-1923)Online publication date: 1-Mar-2022
  • (2020)Accelerated supervised learning to detect spam using feature selection and Apache Spark architecture2020 6th Iranian Conference on Signal Processing and Intelligent Systems (ICSPIS)10.1109/ICSPIS51611.2020.9349587(1-6)Online publication date: 23-Dec-2020

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