Relevant bibliographies by topics / Phishing attack detection

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Phishing attack detection'

Author: Grafiati
Published: 5 June 2025
Last updated: 15 July 2025

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Journal articles on the topic "Phishing attack detection"

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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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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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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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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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R, Nivyashree. "Phishing Website Detection." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem48594.

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Abstract - This literature survey examines software-based phishing detection techniques, a critical area of cybersecurity. With phishing attacks growing rapidly each year, this study explores the phishing ecosystem, current statistics, automatic detection schemes, feature analysis, datasets, algorithms, and evaluation metrics. Emphasis is given to the challenges in feature robustness, handling adaptive attacks, and limitations in large-scale data processing. The survey also identifies research gaps in addressing new attack vectors, offering insights for future directions. Key Words: Phishing,
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Soma Niloy Ghosh and Jayesh V Jawade. "Comparative analysis on different phishing website detection techniques." International Journal of Frontiers in Engineering and Technology Research 8, no. 2 (2025): 055–62. https://doi.org/10.53294/ijfetr.2025.8.2.0036.

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Phishing attack is an attempt to obtain confidential information or data, such as credit and debit card details, username, passwords, etc. by creating a fake website which is very much similar to genuine website. Because of visual similarity of website, users are not able to distinguish between a legitimate and phishing websites. Phishing attack often targets users to enter their personal information at a phishing website. Then that information is directly send to attackers. In today’s world most of the phishing attack takes place with the help of spoofed emails. The attackers first send the e
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Ifthikhar, Nimra, Ahthasham Sajid, Adeel Zafar, Atta Ur Rahman, Rida Malik, and Hamza Razzaq. "A Comprehensive Study on Phishing Attack Detection and Mitigation via Ransomware-as-a-Service (RAAS)." Nucleus 61, no. 2 (2025): 93–100. https://doi.org/10.71330/nucleus.61.02.1402.

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Ransomware-as-a-Service (RAAS), a new cybercriminal actor, is making ransomware attacks more potent and widespread. This research comprehensively assesses Ransomware-as-a-Service (RAAS) ecosystem phishing detection and prevention solutions. Seven studies compare RAAS-enabled phishing detection and prevention effectiveness, challenges, and trends. The findings recommend a multi-layered, context-aware approach for organizational resilience to shifting cyber threats. This thorough phishing attack detection and security study examines ransomware-as-a-service. Phishing attacks leverage human weakne
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Ifthikhar, Nimra, Ahthasham Sajid, Adeel Zafar, Atta Ur Rahman, Rida Malik, and Hamza Razzaq. "A Comprehensive Study on Phishing Attack Detection and Mitigation via Ransomware-as-a-Service (RAAS)." Nucleus 61, no. 2 (2025): 93–100. https://doi.org/10.71330/thenucleus.2024.1402.

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Ransomware-as-a-Service (RAAS), a new cybercriminal actor, is making ransomware attacks more potent and widespread. This research comprehensively assesses Ransomware-as-a-Service (RAAS) ecosystem phishing detection and prevention solutions. Seven studies compare RAAS-enabled phishing detection and prevention effectiveness, challenges, and trends. The findings recommend a multi-layered, context-aware approach for organizational resilience to shifting cyber threats. This thorough phishing attack detection and security study examines ransomware-as-a-service. Phishing attacks leverage human weakne
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PRIPAS, Marian-Iulian. "Phishing Attack – Detection and Removal." International Journal of Information Security and Cybercrime 3, no. 1 (2014): 59–64. http://dx.doi.org/10.19107/ijisc.2014.01.07.

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Kumar, H. V. Kishan, and Praveen K S. "Phishing Website Detection Using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 11, no. 7 (2023): 1824–26. http://dx.doi.org/10.22214/ijraset.2023.54850.

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Abstract: The growing internet user base and reliance on online platforms have led to a growing concern of phishing attacks. Conventional anti-phishing techniques struggle to keep up with evolving tactics. This research proposes a novel approach using machine learning algorithms to combat phishing attacks in real-time. The dataset includes legitimate and phishing websites, with various attack vectors and strategies. Data preprocessing, feature engineering, and machine learning models are trained on the dataset. The proposed approach achieves high accuracy and outperforms traditional rule-based
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Dissertations / Theses on the topic "Phishing attack detection"

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Jan, Steve T. K. "Robustifying Machine Learning based Security Applications." Diss., Virginia Tech, 2020. http://hdl.handle.net/10919/99862.

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In recent years, machine learning (ML) has been explored and employed in many fields. However, there are growing concerns about the robustness of machine learning models. These concerns are further amplified in security-critical applications — attackers can manipulate the inputs (i.e., adversarial examples) to cause machine learning models to make a mistake, and it's very challenging to obtain a large amount of attackers' data. These make applying machine learning in security-critical applications difficult. In this dissertation, we present several approaches to robustifying three machine lea
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Maurer, Max-Emanuel [Verfasser], and Heinrich [Akademischer Betreuer] Hußmann. "Counteracting phishing through HCI : detecting attacks and warning users / Max-Emanuel Maurer. Betreuer: Heinrich Hußmann." München : Universitätsbibliothek der Ludwig-Maximilians-Universität, 2014. http://d-nb.info/1051777089/34.

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Likarish, Peter F. "Early detection of malicious web content with applied machine learning." Diss., University of Iowa, 2011. https://ir.uiowa.edu/etd/4871.

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This thesis explores the use of applied machine learning techniques to augment traditional methods of identifying and preventing web-based attacks. Several factors complicate the identification of web-based attacks. The first is the scale of the web. The amount of data on the web and the heterogeneous nature of this data complicate efforts to distinguish between benign sites and attack sites. Second, an attacker may duplicate their attack at multiple, unexpected locations (multiple URLs spread across different domains) wit
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Lin, Ting-Ching, and 林廷璟. "A Method of Detecting Phishing Attacks for Auction Sites." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/23h859.

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碩士<br>國立中興大學<br>資訊科學與工程學系所<br>101<br>Phishing attacks attract users to visit fake website to steal their personal information. Most phishing detection approaches adopt Uniform Resource Locator (URL) blacklists or phishing website features as a detection character to cope with phishing attacks. The existing approaches of using URL blacklists cannot detect the new phishing attacks because they are not existed in the blacklists. Similarly, the existing feature-based methods suffer high false positive rates and this leads to an inadequacy in the online transactions. To solve these problems, we use
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Book chapters on the topic "Phishing attack detection"

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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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Rastogi, Uttkarsh, Tanu, Vinayak Singhal, Ankush Gupta, and Vimal Kumar. "Detection of Phishing Attack Using Machine Learning." In Advances in Data Science and Computing Technologies. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-3656-4_45.

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Soni, Jayesh, Surya Sirigineedi, Krishna Sai Vutukuru, S. S. ChandanaEswari Sirigineedi, Nagarajan Prabakar, and Himanshu Upadhyay. "Learning-Based Model for Phishing Attack Detection." In Artificial Intelligence in Cyber Security: Theories and Applications. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-28581-3_11.

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Singh, Akhilendra Pratap, Vimal Kumar, Sandeep Singh Sengar, and Manoj Wairiya. "Detection and Prevention of Phishing Attack Using Dynamic Watermarking." In Information Technology and Mobile Communication. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-20573-6_21.

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Yoon, Jun Yong, and Bong Jun Choi. "Privacy-Friendly Phishing Attack Detection Using Personalized Federated Learning." In Intelligent Human Computer Interaction. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-27199-1_46.

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McConnell, Benjamin, Daniel Del Monaco, Mahdieh Zabihimayvan, Fatemeh Abdollahzadeh, and Samir Hamada. "Phishing Attack Detection: An Improved Performance Through Ensemble Learning." In Artificial Intelligence and Soft Computing. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-42508-0_14.

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Chi, Yaping, Zhiting Ling, Xuejing Ba, and Shuhao Li. "An Analysis of a New Detection Method for Spear Phishing Attack." In Lecture Notes in Electrical Engineering. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-6508-9_129.

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Aneesh Kini, U., M. Poornananda Bhat, Raghavendra Ganiga, Radhika M. Pai, M. M. Manohara Pai, and H. C. Shiva Prasad. "Detection and Control of Phishing Attack in Electronic Medical Record Application." In Lecture Notes in Electrical Engineering. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-5558-9_82.

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Conference papers on the topic "Phishing attack detection"

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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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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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Masmoudi, Salma, Habib M. Kammoun, Maha Charfeddine, and Bechir Hamdaoui. "Phishing Attack Detection Through Recursive Feature Elimination Via Cross Validation." In 2025 International Wireless Communications and Mobile Computing (IWCMC). IEEE, 2025. https://doi.org/10.1109/iwcmc65282.2025.11059706.

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Lobo, Royce, Muhammad Naveed Abbas, and Mamoona Naveed Asghar. "Email Phishing Attack Detection using Recurrent and Feed-forward Neural Networks." In 2023 Cyber Research Conference - Ireland (Cyber-RCI). IEEE, 2023. http://dx.doi.org/10.1109/cyber-rci59474.2023.10671515.

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