Publication: Phishing Attack Types and Mitigation: A Survey
Date
2023
Authors
Alghenaim M.F.
Bakar N.A.A.
Abdul Rahim F.
Vanduhe V.Z.
Alkawsi G.
Journal Title
Journal ISSN
Volume Title
Publisher
Springer Science and Business Media Deutschland GmbH
Abstract
The proliferation of the internet and computing devices has drawn much attention during the Covid-19 pandemic stay home and work, and this has led the organization to adapt to staying home. Also, to let the organization work due to the infrastructure for working on proxy during the pandemic. The alarming rate of cyber-attacks, which through this study infer that phishing is one of the most effective and efficient ways for cyber-attack success. In this light, this study aims to study phishing attacks and mitigation methods in play, notwithstanding analysing performance metrics of the current mitigation performance metrics. Results indicate that business enterprises and educational institutions are the most hit using email (social engineering) and web app phishing attacks. The most effective mitigation methods are training/awareness campaigns on social engineering and using artificial intelligence/machine learning (AI/ML). To gain zero or 100% phishing mitigation, AI/ML need to be applied in large scale to measure its efficiency in phishing mitigation. � 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
Description
Keywords
Artificial intelligence , Machine learning , Mitigation , Phishing , Social engineering , Computer crime , Crime , Network security , Computing devices , Cyber-attacks , Internet devices , Machine-learning , Mitigation , Mitigation methods , Performance metrices , Phishing , Phishing attacks , Social engineering , Machine learning