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Relevant bibliographies by topics / Anti-phishing tools

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Academic literature on the topic 'Anti-phishing tools'

Author: Grafiati

Published: 4 June 2025

Last updated: 1 August 2025

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Journal articles on the topic "Anti-phishing tools"

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Sharma, Ishant, and Aman Kumar Sharma. "Anti-Phishing Tools: A Thorough Comparison of Features and Performance." International Journal for Research in Applied Science and Engineering Technology 11, no. 5 (2023): 478–82. http://dx.doi.org/10.22214/ijraset.2023.51502.

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Abstract: Phishing attacks are a common form of cyber-attacks that can result in data breaches, financial losses, and harm an organization's reputation. The purpose of the study is to evaluate and compare three popular anti-phishing tools' effectiveness in detecting and preventing such attacks. The research method involves conducting a study to identify best performing antiphishing tool. The findings show that some tools have high accuracy; others have a lower false positive rate. Overall, this research paper provides useful information about the effectiveness of anti-phishing tools and can he

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Challacombe, Darin J., and Elizabeth N. McElhiney. "Phishing Susceptibility Among Healthcare Workers: The Impact of Awareness, Email Type, and Location." International Journal of Advanced Corporate Learning (iJAC) 18, no. 1 (2025): 4–15. https://doi.org/10.3991/ijac.v18i1.51671.

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While attempts by malicious actors to compromise computer systems continue to increase, there have been limited success in educating corporate learners. Most corporations must rely upon firewalls, email filtering, and other tools to prevent compromises since their employees vary in prevention reliability. Recent studies have shown limited success of anti-phishing awareness corporate learning campaigns; however, these studies have mostly utilized students or individuals aware of their participation in an experiment. The current research utilized healthcare workers. Over the course of 18 months

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Glăvan, Dragoș. "Detection of phishing attacks using the anti-phishing framework." Scientific Bulletin of Naval Academy XXIII, no. 1 (2020): 208–12. http://dx.doi.org/10.21279/1454-864x-20-i1-028.

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. In the area of computer security, phishing is a form of criminal activity that involves obtaining sensitive data, such as access data for banking applications, e-commerce applications (such as eBay or PayPal) or credit card information, using techniques to manipulate the identity data of a person or institution. A phishing attack consists of sending the attacker an electronic message, using instant messaging software or telephone, in which the user is advised to give his confidential data to win certain prizes, or is informed that they are prizes. necessary due to technical errors that led t

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Purkait, Swapan. "Examining the effectiveness of phishing filters against DNS based phishing attacks." Information & Computer Security 23, no. 3 (2015): 333–46. http://dx.doi.org/10.1108/ics-02-2013-0009.

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Purpose – This paper aims to report on research that tests the effectiveness of anti-phishing tools in detecting phishing attacks by conducting some real-time experiments using freshly hosted phishing sites. Almost all modern-day Web browsers and antivirus programs provide security indicators to mitigate the widespread problem of phishing on the Internet. Design/methodology/approach – The current work examines and evaluates the effectiveness of five popular Web browsers, two third-party phishing toolbar add-ons and seven popular antivirus programs in terms of their capability to detect locally

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Dr., Rajendra Gupta. "AN STUDY OF SIMILARITY MEASUREMENT BETWEEN PHISHING AND LEGITIMATE WEBSITES USING BAYESIAN CLASSIFICATION AND ITS PERFORMANCE EVALUATION." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 6, no. 2 (2017): 28–34. https://doi.org/10.5281/zenodo.266735.

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Safe web browsing and feeding confidential information into websites require the use of protected and secured websites. For the web security, a number of anti-phishing tools have been proposed which provide web user with a dynamic system of warning and protection against potential phishing attacks. Earlier study shows that there is no anti-phishing tool gives satisfactory result in identifying the phishing web pages. For the solution of this problem, in this paper a Bayesian classification approach is proposed to identify the phishing websites. Bayesian filter require two datasets in their app

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Andrushchak, I. "Andrushchak I.Ye. Features of the main directions, techniques and methods of protection against fishing at-tacks." COMPUTER-INTEGRATED TECHNOLOGIES: EDUCATION, SCIENCE, PRODUCTION, no. 47 (June 30, 2022): 5–9. http://dx.doi.org/10.36910/6775-2524-0560-2022-47-01.

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This article discusses common modern heuristic technologies used in anti-phishing tools and protection methods. An overview of different types of mobile phishing attacks is given, as well as the main methods of detecting and reducing the level of fishing attacks.

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Gupta, Rajendra, and Piyush Kumar Shukla. "Performance Analysis of Anti-Phishing Tools and Study of Classification Data Mining Algorithms for a Novel Anti-Phishing System." International Journal of Computer Network and Information Security 7, no. 12 (2015): 70–77. http://dx.doi.org/10.5815/ijcnis.2015.12.08.

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Noah, Naheem, Abebe Tayachew, Stuart Ryan, and Sanchari Das. "PhisherCop: Developing an NLP-Based Automated Tool for Phishing Detection." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 66, no. 1 (2022): 2093–97. http://dx.doi.org/10.1177/1071181322661060.

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Phishing poses a major security risk to organizations and individuals leading to loss of billions of dollars yearly. While risk communication serves as a tool to mitigate phishing attempts, it is imperative to create automated phishing detection tools. Numerous Natural Language Processing (NLP) approaches have been deployed to tackle phishing. However, phishing attempts continue to increase exponentially, which reiterates the need for more effective approach. To address this, we have developed an anti-phishing tool called; PhisherCop. PhisherCop is built upon Stochastic Gradient Descent classi

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Tepede Dipo. "Phishing Attacks among Internet Banking Users in Nigeria: An Exploration of Remedial Strategies." International Journal of Latest Technology in Engineering Management & Applied Science 13, no. 5 (2024): 122–29. http://dx.doi.org/10.51583/ijltemas.2024.130512.

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This study meticulously examines phishing attacks targeting Nigerian internet banking users. It delves into prevailing trends, methodologies, and the effectiveness of existing countermeasures. It also proposes robust, tailored security measures in the form of a conceptual model. Similarly, by analyzing the latest tools and techniques, the study emphasizes the need for adaptive and continuously learning anti-phishing systems. The outcomes aim to empower stakeholders in the Nigerian banking sector, including policymakers, financial institutions, and users, to combat the complex and evolving thre

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Lappas, Dimitrios, Panagiotis Karampelas, and Giorgos Fesakis. "Enhancement of Phishing Email Detection with Bayesian Networks. A Cyber Security Training Module." European Conference on Cyber Warfare and Security 24, no. 1 (2025): 328–37. https://doi.org/10.34190/eccws.24.1.3315.

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In today’s digital world, we rely on various applications to protect ourselves from malicious software on the internet. Many of these tools also aim to shield us from phishing emails, which are increasingly prevalent. But how reliable are these phishing protection tools? Do we uncritically trust their indication that an email is safe, or are their assessments merely probabilistic estimates? These questions became the focal point of an innovative educational process in cybersecurity training. In this training activity, participants initially assumed the role of phishing email creators, crafting

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Book chapters on the topic "Anti-phishing tools"

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Mavroeidis, Vasileios, and Mathew Nicho. "Quick Response Code Secure: A Cryptographically Secure Anti-Phishing Tool for QR Code Attacks." In Lecture Notes in Computer Science. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-65127-9_25.

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Navaneeth, Jampula, Mosiur Rahaman, and Brij B. Gupta. "Anti-Phishing Technologies and Tools." In Critical Phishing Defense Strategies and Digital Asset Protection. IGI Global, 2025. https://doi.org/10.4018/979-8-3693-8784-9.ch007.

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Phishing continues to be one of the most common and effective forms of cyber security threats and involve deception of users thereby getting them provide unauthorized individuals with personal information. In this chapter an overview of phishing attacks is presented with a special focus on anti-phishing technologies. Phishing is then introduced in terms of what is it and different types of it that are currently existing, and the impact that they bring to the individuals who are targeted. Anti-phishing measures are then considered with reference to their purpose of protecting personal and organ

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Kaur, Kashish Preet, Sunil K. Singh, Sudhakar Kumar, et al. "Advanced Tools and Technologies for Phishing Prevention." In Critical Phishing Defense Strategies and Digital Asset Protection. IGI Global, 2025. https://doi.org/10.4018/979-8-3693-8784-9.ch009.

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Phishing attacks are a major cybersecurity threat, exploiting human vulnerabilities by manipulating trust to access sensitive information. As phishing tactics evolve, the demand for advanced anti-phishing technologies has surged. This paper examines key measures such as email filters, SSL/TLS certificates, DNS-based authentication, multi-factor authentication, and URL filtering to protect against phishing. Advanced methods like machine learning, AI-driven analysis, and real-time threat detection have improved phishing prevention. It also discusses browser-based, cloud-based, and email security

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Muku, Abhavya, Sunil K. Singh, Sudhakar Kumar, et al. "Phishing Prevention Solutions and Mechanisms." In Critical Phishing Defense Strategies and Digital Asset Protection. IGI Global, 2025. https://doi.org/10.4018/979-8-3693-8784-9.ch003.

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Phishing is a dangerous online fraud tactic that tricks individuals into disclosing personal information, like usernames, passwords, and credit card numbers. It often appears through fake emails, messages, or websites designed to lure victims into sharing sensitive data. This illegal activity can cause significant financial losses for businesses and consumers, while also damaging trust in online transactions. Anti-phishing tools, such as SpamTitan and Barracuda Sentinel, use advanced algorithms and machine learning to detect and block phishing attempts in real time, protecting users from data

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Pathak, Astha, Prashant Pandey, and Vaishali Raheja. "Comparing Different Machine Learning Techniques for Detecting Phishing Websites." In AI in the Social and Business World: A Comprehensive Approach. BENTHAM SCIENCE PUBLISHERS, 2024. http://dx.doi.org/10.2174/9789815256864124010012.

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Phishing site URLs are designed to gather confidential data such as user identities, passwords, and transactions involving online money. Phishing strategies have begun to advance quickly as technology advances; this could be avoided by using anti-phishing tools to identify phishing. Employing machine learning techniques to identify fraudulent websites was previously suggested and put into practice. This project's primary goal is to develop the system in a way that is highly efficient, accurate, and economical. Delivered to the system, the dataset of genuine and phishing URLs is pre-processed t

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Delfino, Manuela. "Don’t Trash Your Spam!" In Handbook of Research on Didactic Strategies and Technologies for Education. IGI Global, 2013. http://dx.doi.org/10.4018/978-1-4666-2122-0.ch070.

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Some activities that are typically regarded as “a waste of time” may inspire useful experiences in school contexts. This is the case of spamming. As competent users of digital technologies, we do everything we can in order to block spam messages (e.g., by choosing, training and updating anti-spam filters), but as teachers we can find that also in spam there is an educative potential that it is worth cultivating. This chapter presents a reflection based on an educational experience realized in a lower secondary Italian school during a course on Digital Literacy aimed at making students aware of

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Maharana, Narayana, Sudesh Kumar Kuppili, B. Udaya Bhaskar Ganesh, Girish Prasad Das, and Suman Kalyan Chaudhury. "From Defense to Deception." In Advances in Business Strategy and Competitive Advantage. IGI Global, 2025. https://doi.org/10.4018/979-8-3693-7026-1.ch012.

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This paper critically analyzes the intersection between artificial intelligence (AI) and financial fraud in India. As digital financial services expand, AI has emerged as a double-edged sword, offering advanced tools for both preventing and perpetrating financial fraud. On the one hand, AI-driven technologies are employed by financial institutions to enhance security measures, detect anomalies, and prevent fraudulent activities through sophisticated algorithms and real-time data analysis. On the other hand, cybercriminals are increasingly leveraging AI to execute complex fraud schemes, such as

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Mohan, N., G. Rajasekar, and Ratno Agriyanto. "Anti-Fraud AI for Banking and FinTech Used in a Proactive Banking Strategy to Thwart SIM Phishing." In The Impact of AI Innovation on Financial Sectors in the Era of Industry 5.0. IGI Global, 2024. http://dx.doi.org/10.4018/979-8-3693-0082-4.ch010.

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The Fintech disruption has sped up and opened up banking to everyone. People with old-style accounts can now conduct their daily banking activities from any locality at any time thanks to allied technology. Financial invasion requires fin-tech as well. People without bank accounts can now access essential financial services, such as peer-to-peer lending and digital payments, thanks to smart phone apps and web-tools. India is one of the fastest growing Fintech ecologies of the world. It stands second after China in terms of Fintech adoption index rate of 87%. Overall estimation of the Fin-tech

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Conference papers on the topic "Anti-phishing tools"

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Alqahtani, Saleh, and Priyadarsi Nanda. "Enhancing Phishing Resilience in Academia: The Mediating Role of Anti-Phishing Tools on Student Awareness and Behavior." In 2024 17th International Conference on Security of Information and Networks (SIN). IEEE, 2024. https://doi.org/10.1109/sin63213.2024.10871376.

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Alqahtani, Saleh, and Priyadarsi Nanda. "Effects of Personal Characteristics on Phishing Awareness, Anti-Phishing Tool Usage, and Phishing Avoidance Behavior: A Structural Equation Modeling Approach." In 2024 17th International Conference on Security of Information and Networks (SIN). IEEE, 2024. https://doi.org/10.1109/sin63213.2024.10871302.

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Zeydan, Hiba Zuhair, Ali Selamat, and Mazleena Salleh. "Survey of anti-phishing tools with detection capabilities." In 2014 International Symposium on Biometrics and Security Technologies (ISBAST). IEEE, 2014. http://dx.doi.org/10.1109/isbast.2014.7013124.

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Rosser, Holly, Maylene Mayor, Adam Stemmler, Vinod Ahuja, Andrea Grover, and Matthew Hale. "Phish Finders: Crowd-powered RE for anti-phishing training tools." In 2022 IEEE 30th International Requirements Engineering Conference Workshops (REW). IEEE, 2022. http://dx.doi.org/10.1109/rew56159.2022.00031.

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Fahmy, Hossam M. A., and Salma A. Ghoneim. "PhishBlock: A hybrid anti-phishing tool." In 2011 International Conference on Communications, Computing and Control Applications (CCCA). IEEE, 2011. http://dx.doi.org/10.1109/ccca.2011.6031523.

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Alnajim, Abdullah, and Malcolm Munro. "An Approach to the Implementation of the Anti-Phishing Tool for Phishing Websites Detection." In 2009 International Conference on Intelligent Networking and Collaborative Systems (INCOS). IEEE, 2009. http://dx.doi.org/10.1109/incos.2009.37.

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Abbasi, Ahmed, Fatemeh Zahedi, and Yan Chen. "Impact of anti-phishing tool performance on attack success rates." In 2012 IEEE International Conference on Intelligence and Security Informatics (ISI 2012). IEEE, 2012. http://dx.doi.org/10.1109/isi.2012.6282648.

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