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A Flexible Phishing Detection Approach Based on Software-Defined Networking Using Ensemble Learning Method

Published: 06 August 2020 Publication History

Abstract

Phishing is a plague in cyberspace, in which threat actors try to lure unsuspecting victims to disclose their sensitive information like bank account or password. Existing detection techniques of phishing attacks in traditional networks generally use a proxy server to determine whether a website is a phishing website lacking flexibility. The flexibility of software-defined networks solves the limitation of traditional networks. Based on this, this paper proposes a new scalable phishing detection approach based on Software-Defined Networking (SDN) to identify phishing activities through web-based communications, which separates detection mechanism from end users with good accuracy and flexibility with ensemble learning method.

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  • (2024)An Overview of Problems and Difficulties with ML in WSNs ProtectionEuropean Journal of Applied Science, Engineering and Technology10.59324/ejaset.2024.2(2).182:2(245-278)Online publication date: 1-Mar-2024
  • (2024)CybersecurityAnalyzing Privacy and Security Difficulties in Social Media10.4018/979-8-3693-9491-5.ch010(213-246)Online publication date: 6-Dec-2024
  • (2024)Exploring the Synergy: A Review of Machine Learning Techniques in Software Defined Networking (SDN)ITM Web of Conferences10.1051/itmconf/2024640101664(01016)Online publication date: 5-Jul-2024
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      cover image ACM Other conferences
      HP3C 2020: Proceedings of the 2020 4th International Conference on High Performance Compilation, Computing and Communications
      June 2020
      191 pages
      ISBN:9781450376914
      DOI:10.1145/3407947
      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]

      In-Cooperation

      • Xi'an Jiaotong-Liverpool University: Xi'an Jiaotong-Liverpool University
      • City University of Hong Kong: City University of Hong Kong
      • Guangdong University of Technology: Guangdong University of Technology

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      Association for Computing Machinery

      New York, NY, United States

      Publication History

      Published: 06 August 2020

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      Author Tags

      1. Ensemble learning
      2. Phishing detection
      3. Security
      4. Software-Defined Networks

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      • Refereed limited

      Funding Sources

      • National Natural Science Foundation of China
      • National Key R&D Program of China

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      HP3C 2020

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      Cited By

      View all
      • (2024)An Overview of Problems and Difficulties with ML in WSNs ProtectionEuropean Journal of Applied Science, Engineering and Technology10.59324/ejaset.2024.2(2).182:2(245-278)Online publication date: 1-Mar-2024
      • (2024)CybersecurityAnalyzing Privacy and Security Difficulties in Social Media10.4018/979-8-3693-9491-5.ch010(213-246)Online publication date: 6-Dec-2024
      • (2024)Exploring the Synergy: A Review of Machine Learning Techniques in Software Defined Networking (SDN)ITM Web of Conferences10.1051/itmconf/2024640101664(01016)Online publication date: 5-Jul-2024
      • (2023)An Ensemble Learning Model for the Detection of Phishing Attacks2023 20th International Bhurban Conference on Applied Sciences and Technology (IBCAST)10.1109/IBCAST59916.2023.10712859(594-599)Online publication date: 22-Aug-2023
      • (2023)SDN as a defence mechanism: a comprehensive surveyInternational Journal of Information Security10.1007/s10207-023-00764-123:1(141-185)Online publication date: 6-Oct-2023
      • (2022)Machine Learning for Wireless Sensor Networks Security: An Overview of Challenges and IssuesSensors10.3390/s2213473022:13(4730)Online publication date: 23-Jun-2022
      • (2021)Sustaining accurate detection of phishing URLs using SDN and feature selection approachesComputer Networks: The International Journal of Computer and Telecommunications Networking10.1016/j.comnet.2021.108591201:COnline publication date: 30-Dec-2021

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