Relevant bibliographies by topics / Phishing attack

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Phishing attack'

Author: Grafiati
Published: 4 June 2021
Last updated: 26 July 2025

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Journal articles on the topic "Phishing attack"

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Rastenis, Justinas, Simona Ramanauskaitė, Justinas Janulevičius, Antanas Čenys, Asta Slotkienė, and Kęstutis Pakrijauskas. "E-mail-Based Phishing Attack Taxonomy." Applied Sciences 10, no. 7 (2020): 2363. http://dx.doi.org/10.3390/app10072363.

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The amount of fraud on the Internet is increasing along with the availability and the popularity of the Internet around the world. One of the most common forms of Internet fraud is phishing. Phishing attacks seek to obtain a user’s personal or secret information. The variety of phishing attacks is very broad, and usage of novel, more sophisticated methods complicates its automated filtering. Therefore, it is important to form up-to-date and detailed phishing attack taxonomy, which could be used for both human education purposes as well as phishing attack discrete notation. In this paper, we pr
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Mohamed, Gori, J. Visumathi, Miroslav Mahdal, Jose Anand, and Muniyandy Elangovan. "An Effective and Secure Mechanism for Phishing Attacks Using a Machine Learning Approach." Processes 10, no. 7 (2022): 1356. http://dx.doi.org/10.3390/pr10071356.

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Phishing is one of the biggest crimes in the world and involves the theft of the user’s sensitive data. Usually, phishing websites target individuals’ websites, organizations, sites for cloud storage, and government websites. Most users, while surfing the internet, are unaware of phishing attacks. Many existing phishing approaches have failed in providing a useful way to the issues facing e-mails attacks. Currently, hardware-based phishing approaches are used to face software attacks. Due to the rise in these kinds of problems, the proposed work focused on a three-stage phishing series attack
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P.M, Dinesh, Mukesh M, Navaneethan B, Sabeenian R.S, Paramasivam M.E, and Manjunathan A. "Identification of Phishing Attacks using Machine Learning Algorithm." E3S Web of Conferences 399 (2023): 04010. http://dx.doi.org/10.1051/e3sconf/202339904010.

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Phishing is a particular type of cybercrime that allows criminals to trick people and steal crucial data. The phishing assault has developed into a more complex attack vector since the first instance was published in 1990. Phishing is currently one of the most prevalent types of online fraud behavior. Phishing is done using a number of methods, such as through emails, phone calls, instant chats, adverts, pop-up windows on websites, and DNS poisoning. Phishing attacks can cause their victims to suffer significant losses, including the loss of confidential information, identity theft, businesses
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Nur, Sholihah Zaini, Stiawan Deris, Faizal Ab Razak Mohd, et al. "Phishing detection system using machine learning classifiers." Indonesian Journal of Electrical Engineering and Computer Science (IJEECS) 17, no. 3 (2020): 1165–71. https://doi.org/10.11591/ijeecs.v17.i3.pp1165-1171.

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The increasing development of the Internet, more and more applications are put into websites can be directly accessed through the network. This development has attracted an attacker with phishing websites to compromise computer systems. Several solutions have been proposed to detect a phishing attack. However, there still room for improvement to tackle this phishing threat. This paper aims to investigate and evaluate the effectiveness of machine learning approach in the classification of phishing attack. This paper applied a heuristic approach with machine learning classifier to identify phish
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Al Ogaili, Riyadh Rahef Nuiaa, and Selvakumar Manickam. "A Critical Review: A New Taxonomy for Phishing Attacks Based on Phishing Techniques Used." Wasit Journal for Pure sciences 2, no. 2 (2023): 251–69. http://dx.doi.org/10.31185/wjps.143.

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People are increasingly sharing their personal information online as internet usage grows. As a result, fraudsters have access to a massive amount of personal information and financial activities. In recent years, phishing assaults have become one of the most common threats faced by internet users, governments, and service providers. The attacker(s) uses falsified emails or bogus websites to obtain the client's sensitive data (i.e., user account login details, credit/debit card numbers, etc.) in a phishing assault. Studies have classed phishing attacks based on fundamental phishing mechanisms
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Alsariera, Yazan A., Meshari H. Alanazi, Yahia Said, and Firas Allan. "An Investigation of AI-Based Ensemble Methods for the Detection of Phishing Attacks." Engineering, Technology & Applied Science Research 14, no. 3 (2024): 14266–74. http://dx.doi.org/10.48084/etasr.7267.

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Phishing attacks remain a significant cybersecurity threat in the digital landscape, leading to the development of defense mechanisms. This paper presents a thorough examination of Artificial Intelligence (AI)-based ensemble methods for detecting phishing attacks, including websites, emails, and SMS. Through the screening of research articles published between 2019 and 2023, 37 relevant studies were identified and analyzed. Key findings highlight the prevalence of ensemble methods such as AdaBoost, Bagging, and Gradient Boosting in phishing attack detection models. Adaboost emerged as the most
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Jimmy, Fnu. "Phishing attackers: prevention and response strategies." Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023 2, no. 1 (2024): 307–18. http://dx.doi.org/10.60087/jaigs.v2i1.249.

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Phishing attacks remain one of the most prevalent and destructive forms of cybercrime, posing significant threats to individuals, organizations, and governments. These attacks, which typically involve fraudulent attempts to steal sensitive information, have evolved in sophistication, exploiting both technological vulnerabilities and human error. This paper reviews various prevention and response strategies for phishing attacks, examining both proactive measures to prevent phishing attempts and reactive responses to mitigate damage post-attack. It discusses common phishing techniques such as em
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Zaini, Nur Sholihah, Deris Stiawan, Mohd Faizal Ab Razak, et al. "Phishing detection system using nachine learning classifiers." Indonesian Journal of Electrical Engineering and Computer Science 17, no. 3 (2020): 1165. http://dx.doi.org/10.11591/ijeecs.v17.i3.pp1165-1171.

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<span>The increasing development of the Internet, more and more applications are put into websites can be directly accessed through the network. This development has attracted an attacker with phishing websites to compromise computer systems. Several solutions have been proposed to detect a phishing attack. However, there still room for improvement to tackle this phishing threat. This paper aims to investigate and evaluate the effectiveness of machine learning approach in the classification of phishing attack. This paper applied a heuristic approach with machine learning classifier to id
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Desai, Viraj, and Kavitha R. "Unveiling the Depths of Phishing: Understanding Tactics, Impacts, and Countermeasures." International Journal of Innovative Research in Science,Engineering and Technology 13, no. 05 (2024): 8596–600. http://dx.doi.org/10.15680/ijirset.2024.1305331.

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Phishing attacks have emerged as a pervasive threat in the cybersecurity landscape, exploiting human vulnerabilities to compromise sensitive information and perpetrate cybercrime. This paper provides a comprehensive examination of phishing attacks, encompassing their methods, prevalence, and impact on individuals and organizations. Through an in-depth literature review, we delve into the various types of phishing attacks, including email phishing, spear phishing, and pharming, elucidating their distinct characteristics and methodologies. We highlight the significant financial and reputational
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Wahyuni, Ni Komang Arista Tri, Putu Putri Cahayani, I Gusti Ngurah Yogi Wicaksana, and Ida Ayu Kadek Bintang Wijayanti. "ANALISIS KERENTANAN KEJAHATAN ONLINE PHISING MENGGUNAKAN TOOLS ZPHISHER, SHELLPHISH DAN WHPHISHER." Jurnal Teknik Mesin, Elektro dan Ilmu Komputer 3, no. 1 (2023): 23–31. http://dx.doi.org/10.55606/teknik.v3i1.915.

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In the era of increasingly advanced and widespread technology as it is today, personal data is an asset that really needs to be protected by an organization or individual, personal data information that is private is very important to be protected so that it cannot be accessed by unauthorized parties. Phishing attacks are currently still increasing, this happens because the perpetrators of phishing attacks understand to trick victims where the victim is still weak to understand the security system on personal data information. The purpose of this article is to explain how these phishing attack
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Dissertations / Theses on the topic "Phishing attack"

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Bian, Kaigui. "New Approaches for Ensuring User Online Privacy." Thesis, Virginia Tech, 2007. http://hdl.handle.net/10919/35972.

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With the increase of requesting personal information online, unauthorized disclosure of user privacy is a significant problem faced by todayâ s Internet. As a typical identity theft, phishing usually employs fraudulent emails and spoofed web sites to trick unsuspecting users into divulging their private information. Even legitimate web sites may collect private information from unsophisticated users such as children for commercial purposes without their parentsâ consent. The Childrenâ s Online Privacy Protection Act (COPPA) of 1998 was enacted in reaction to the widespread collection of in
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Loreti, Ludovico. "Pass The Hash attack. Panoramica, sperimentazione ed analisi." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2018. http://amslaurea.unibo.it/17297/.

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La tesi è incentrata sul noto attacco informatico Pass The Hash e sul suo sviluppo all'interno di un sistema Microsoft Active Directory. Viene descritta una panoramica dell'attacco accennando la sua storia ed evoluzione fino ad oggi, alcuni dei tanti attacchi informatici ad esso propedeutici ed un background tecnico in cui vengono spiegate le sue componenti principali: il tool che viene utilizzato oggigiorno per sperimentare questa tecnica (Mimikatz), le funzioni di hash, i sistemi Single Sign-On, la suddivisione in domini, alberi e foreste di Active Directory. In seguito viene mostrato l'amb
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Ahmed, Olfet, and Nawar Saman. "Utvärdering av nätverkssäkerheten på J Bil AB." Thesis, KTH, Data- och elektroteknik, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-123403.

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Detta examensarbete är en utvärdering av nätverkssäkerheten hos J BiL AB, både på social och teknisk nivå. Företaget är beroende av säkra Internet-anslutningar för att nå externa tjänster och interna servrar lokaliserade på olika geografiska platser. Företaget har ingen IT-ansvarig som aktivt underhåller och övervakar nätverket, utan konsulterar ett externt dataföretag. Syftet med examensarbetet är att utvärdera säkerheten, upptäcka brister, ge förbättringsförslag och till viss del implementera lösningar. För att undersöka säkerheten har observationer och intervjuer med personalen gjorts och e
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Dong, Xun. "Defending against phishing attacks." Thesis, University of York, 2009. http://etheses.whiterose.ac.uk/2215/.

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Valuable information, such as user authentication credentials and personal sensitive information, can be obtained by exploiting vulnerabilities within the user’s understanding of a system, and particularly a lack of understanding of the user interface. As the barrier to exploiting system vulnerabilities has increased significantly with time, attacking users has rapidly become a more efficient and effective alternative. To protect users from phishing attacks system designers and security professionals need to understand how users interact with those attacks. In this thesis I present an improved u
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Persson, Anders. "Exploring Phishing Attacks and Countermeasures." Thesis, Blekinge Tekniska Högskola, Avdelningen för för interaktion och systemdesign, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-3620.

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Online banking and e-commerce applications have good protection against attacks directed direct towards their computer systems. This, the attacker has considered and instead use “social engineering” attacks, such as phishing to gain access to the information inside [1] [15] [21]. Phishing is a growing problem that many different companies are trying to develop a working protection against. The number of new phishing-sites per month increased by 1363 % between January 2005 and October 2006, from 2560 to 37 444 attacks [3] [2]. Today there are several different antiphishing applications as well
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Nordgren, Daniella. "Phishing attacks targeting hospitals : A study over phishing knowledge at Blekingesjukhuset." Thesis, Blekinge Tekniska Högskola, Institutionen för datalogi och datorsystemteknik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-16681.

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Context. Phishing emails is a type of computer attack targeting users and tries to trick them into giving out personal information, follow shady links or download malicious attachments. Phishing is often closely linked to ransomware, which is a type of attack that locks a users computer and asks for a ransom in order to give access back. Ransomware viruses often contaminate a computer through a phishing email. Hospitals are a growing target for these types of attacks because of their need of being able to access their system at all times. Objectives. This study intends to research the phishing
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Henshaw, James Presley. "Phishing Warden : enhancing content-triggered trust negotiation to prevent phishing attacks /." Diss., CLICK HERE for online access, 2005. http://contentdm.lib.byu.edu/ETD/image/etd840.pdf.

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Le, Page Sophie. "Understanding the Phishing Ecosystem." Thesis, Université d'Ottawa / University of Ottawa, 2019. http://hdl.handle.net/10393/39385.

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In “phishing attacks”, phishing websites mimic trustworthy websites in order to steal sensitive information from end-users. Despite research by both academia and the industry focusing on development of anti-phishing detection techniques, phishing has increasingly become an online threat. Our inability to slow down phishing attacks shows that we need to go beyond detection and focus more on understanding the phishing ecosystem. In this thesis, we contribute in three ways to understand the phishing ecosystem and to offer insight for future anti-phishing efforts. First, we provide a new and co
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Floderus, Sebastian, and Linus Rosenholm. "An educational experiment in discovering spear phishing attacks." Thesis, Blekinge Tekniska Högskola, Institutionen för datavetenskap, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-18446.

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Background: Spear phishing attacks uses social engineering targeting a specific person to steal credential information or infect the users computer with malware. It is often done through emails and it can be very hard to spot the difference between a legitimate email and a scam email. Cybercrime is a growing problem and there is many ways to inform and educate individuals on the subject.Objectives: This study intends to perform an experiment to see if an educationalsupport tool can be used to better identify phishing emails. Furthermore see if there is a difference in susceptibility between st
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Kuo, Chuan-Chi. "Multi-layered regulation of phishing attacks : a Taiwan case study." Thesis, University of Warwick, 2014. http://wrap.warwick.ac.uk/67171/.

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This research examines the regulation of phishing in Taiwan, particularly focusing on legal regulation but within a context of a multi-dimensional regulatory framework which also necessarily includes an examination of international regulation and the interaction between international and Taiwan regulatory interfaces given the transnational nature of phishing. Phishing is a malicious cyber activity which targets the acquisition of various types of confidential information by deception through the use of spoofed emails and/or websites. The increasing threat of phishing to information security ha
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Books on the topic "Phishing attack"

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Atkins, Mr Christopher. Phishing Attacks: Advanced Attack Techniques. CreateSpace Independent Publishing Platform, 2018.

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Phishing and Communication Channels: A Guide to Identifying and Mitigating Phishing Attacks. Apress L. P., 2021.

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Mauro, Aaron. The Language of Cyber Attacks. Bloomsbury Publishing Plc, 2024. http://dx.doi.org/10.5040/9781350354715.

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Many cyberattacks begin with a lure: a seemingly innocent message designed to establish trust with a target to obtain sensitive information or compromise a computer system. The perils of clicking an unknown link or divulging sensitive information via email are well-known, so why do we continue to fall prey to these malicious messages?This groundbreaking book examines the rhetoric of deception through the lure, asking where its all-too-human allure comes from and suggesting ways in which we can protect ourselves online. Examining practices and tools such as phishing, ransomware and clickbait, t
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Cybersecurity Threats, Malware Trends, and Strategies: Mitigate Exploits, Malware, Phishing, and Other Social Engineering Attacks. Packt Publishing, Limited, 2020.

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How to Remove Malwares from a Computer: A STEP by STEP GUIDE to REMOVE MALWARES THREATS,PHISHING and OTHER SOCIAL ENGINEERING ATTACKS. Independently Published, 2021.

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How to Remove Malwares from a Computer: A STEP by STEP GUIDE to REMOVE MALWARES THREATS, PHISHING and OTHER SOCIAL ENGINEERING ATTACKS. Independently Published, 2021.

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Book chapters on the topic "Phishing attack"

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Olaleye, Taiwo O., Oluwasefunmi T. Arogundade, Agbaegbu Johnbosco, et al. "Phishing Attack and Defense." In AI-Centric Modeling and Analytics. CRC Press, 2023. http://dx.doi.org/10.1201/9781003400110-17.

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Theodoros, Tzouramanis, and Karampelas Loukas. "Online Social Network Phishing Attack." In Encyclopedia of Social Network Analysis and Mining. Springer New York, 2017. http://dx.doi.org/10.1007/978-1-4614-7163-9_348-1.

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Sahoo, Bhaswati, and Prasant Kumar Pattnaik. "Cybersecurity Analysis and Phishing Attack." In Communication Technology and Gender Violence. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-45237-6_3.

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Theodoros, Tzouramanis, and Karampelas Loukas. "Online Social Network Phishing Attack." In Encyclopedia of Social Network Analysis and Mining. Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4614-6170-8_348.

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Theodoros, Tzouramanis, and Karampelas Loukas. "Online Social Network Phishing Attack." In Encyclopedia of Social Network Analysis and Mining. Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4939-7131-2_348.

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Gallagher, Sean, Ben Gelman, Salma Taoufiq, et al. "Phishing and Social Engineering in the Age of LLMs." In Large Language Models in Cybersecurity. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-54827-7_8.

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AbstractThe human factor remains a major vulnerability in cybersecurity. This chapter explores the escalating threats that Large Language Models (LLMs) pose in the field of cybercrime, particularly in phishing and social engineering. Due to their ability to generate highly convincing and individualized content, LLMs enhance the effectiveness and scale of phishing attacks, making them increasingly difficult to detect. The integration of multimodal generative models allows malicious actors to leverage AI-generated text, images, and audio, increasing attack avenues and making attacks more convinc
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NaliniPriya, G., K. Damoddaram, G. Gopi, and R. Nitish Kumar. "Phishing Attack Detection Using Machine Learning." In Emerging Trends in Expert Applications and Security. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-1946-8_27.

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Nataraj, K. R., D. K. Yashaswini, R. Hema, Nayana S. Pawar, and S. Yashaswi. "Phishing Attack Detection Using Machine Learning." In Lecture Notes in Electrical Engineering. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-2058-7_33.

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Buber, Ebubekir, Banu Diri, and Ozgur Koray Sahingoz. "NLP Based Phishing Attack Detection from URLs." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-76348-4_59.

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Alghenaim, Mohammed Fahad, Nur Azaliah Abu Bakar, Fiza Abdul Rahim, Vanye Zira Vanduhe, and Gamal Alkawsi. "Phishing Attack Types and Mitigation: A Survey." In Data Science and Emerging Technologies. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-0741-0_10.

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Conference papers on the topic "Phishing attack"

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J, Ranjani, Kamala B, Kalaiselvi VKG, Aishwarya R, and Abinaya A. "Phishing Attack Detector and Awareness Generator." In 2024 International Conference on Power, Energy, Control and Transmission Systems (ICPECTS). IEEE, 2024. https://doi.org/10.1109/icpects62210.2024.10780168.

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Goyal, Ashtha, Saksham Mittal, Sumit Negi, et al. "Phishing Attack Detection Using MapReduce and Machine Learning." In 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT). IEEE, 2024. http://dx.doi.org/10.1109/icccnt61001.2024.10726226.

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Karthick Kumar, M., and N. Sivakumar. "URL Phishing attack Detection using Machine Learning Algorithms." In 2024 OPJU International Technology Conference (OTCON) on Smart Computing for Innovation and Advancement in Industry 4.0. IEEE, 2024. http://dx.doi.org/10.1109/otcon60325.2024.10687804.

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Geetha, B., P. Malathi, T. Thirumalaikumari, V. Janakiraman, H. Anwer Basha, and S. Rukmani Devi. "Machine Learning Approaches for Proactive Phishing Attack Detection." In 2024 7th International Conference on Circuit Power and Computing Technologies (ICCPCT). IEEE, 2024. http://dx.doi.org/10.1109/iccpct61902.2024.10672638.

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Krishnaveni, A., and S. Balamurugan. "Phishing Attack Prediction using Several Machine Learning Techniques." In 2024 4th International Conference on Sustainable Expert Systems (ICSES). IEEE, 2024. https://doi.org/10.1109/icses63445.2024.10763142.

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Pandey, Shivam, Prashant Priyadarshi, and Suman Devi. "Phishing attack Detection using NLP and Machine learning." In 2024 1st International Conference on Advances in Computing, Communication and Networking (ICAC2N). IEEE, 2024. https://doi.org/10.1109/icac2n63387.2024.10895895.

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Gopal, S. B., Nanthiya D, Praveenkumar R, et al. "Examination of Phishing Attack Detection Using Machine Learning Techniques." In 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT). IEEE, 2024. http://dx.doi.org/10.1109/icccnt61001.2024.10723929.

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Zhumabekova, Aidana, Vladislav Karyukin, Kuanysh Zhumabekova, et al. "The Comprehensive Deep Learning Models for Phishing Attack Detection." In 2024 20th International Asian School-Seminar on Optimization Problems of Complex Systems (OPCS). IEEE, 2024. http://dx.doi.org/10.1109/opcs63516.2024.10720441.

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Li, Haiwei, Jiahai Yang, Yuqi Li, and Kun Li. "Email phishing attack detection based on BERT transformer model." In International Conference on Optics, Electronics, and Communication Engineering, edited by Yang Yue. SPIE, 2024. http://dx.doi.org/10.1117/12.3049161.

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Almalkawi, Islam T., Mohammad F. Al-Hammouri, Mohammed Abu Mallouh, and Tasneem Barakat. "Improving Email Security Through Machine Learning-Based Phishing Attack Detection." In 2024 International Jordanian Cybersecurity Conference (IJCC). IEEE, 2024. https://doi.org/10.1109/ijcc64742.2024.10847270.

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Reports on the topic "Phishing attack"

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Tamborello, Franklin P., and Kristen K. Greene. Exploratory lens model of decision-making in a potential phishing attack scenario. National Institute of Standards and Technology, 2017. http://dx.doi.org/10.6028/nist.ir.8194.

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