Faculty of Communication, Arts and Sciences
Permanent URI for this community
This community includes articles, book chapters, and conferences published by the Faculty of Communication, Arts, and Sciences faculty members.
Browse
Browsing Faculty of Communication, Arts and Sciences by Subject "Anti-phishing"
Now showing 1 - 3 of 3
Results Per Page
Sort Options
Item Avoiding the Phishing Bait: The Need for Conventional Countermeasures for Mobile Users(Institute of Electrical and Electronics Engineers Inc., 2018-07-02) Baadel, Said; Thabtah, Fadi; Majeed, AsimItem Cybersecurity awareness: A critical analysis of education and law enforcement methods(Slovene Society Informatika, 2021) Baadel, Said; Thabtah, Fadi; Lu, JoanAccording to the international Anti-Phishing Work Group (APWG), phishing activities have abruptly risen over the last few years, and users are becoming more susceptible to online and mobile fraud. Machine Learning techniques have potential for building technical anti-phishing models, with a handful already implemented in the real time environment. However, majority of them have yet to be applied in a real time environment and require domain experts to interpret the results. This gives conventional techniques a vital role as supportive tools for a wider audience, especially novice users. This paper reviews in-depth, common, phishing countermeasures including legislation, law enforcement, hands-on training, and education among others. A complete prevention layer based on the aforementioned approaches is suggested to increase awareness and report phishing to different stakeholders, including organizations, novice users, researchers, and computer security experts. Therefore, these stakeholders can understand the upsides and downsides of the current conventional approaches and the ways forward for improving them. © 2021 Slovene Society Informatika. All rights reserved.Item Data analytics : intelligent anti-phishing techniques based on machine learning(World Scientific Publishing Co. Pte Ltd, 2019) Baadel, Said; Lu, JoanAccording to the international body Anti-Phishing Work Group (APWG), phishing activities have skyrocketed in the last few years and more online users are becoming susceptible to phishing attacks and scams. While many online users are vulnerable and naive to the phishing attacks, playing catch-up to the phishers' evolving strategies is not an option. Machine Learning techniques play a significant role in developing effective anti-phishing models. This paper looks at phishing as a classification problem and outlines some of the recent intelligent machine learning techniques (associative classifications, dynamic self-structuring neural network, dynamic rule-induction, etc.) in the literature that is used as anti-phishing models. The purpose of this review is to serve researchers, organisations' managers, computer security experts, lecturers, and students who are interested in understanding phishing and its corresponding intelligent solutions. This will equip individuals with knowledge and skills that may prevent phishing on a wider context within the community. © 2019 World Scientific Publishing Co.