{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,29]],"date-time":"2026-06-29T21:32:15Z","timestamp":1782768735083,"version":"3.54.5"},"reference-count":32,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2025,8,15]],"date-time":"2025-08-15T00:00:00Z","timestamp":1755216000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["BDCC"],"abstract":"<jats:p>Phishing attacks are an increasingly common cybersecurity threat and are characterized by deceiving people into giving out their private credentials via emails, websites, and messages. An insight into students\u2019 challenges in recognizing phishing threats can provide valuable information on how AI-based detection systems can be improved to enhance accuracy, reduce false positives, and build user trust in cybersecurity. This study focuses on students\u2019 awareness of phishing attempts and evaluates AI-based phishing detection systems. Questionnaires were circulated amongst students, and responses were evaluated to uncover prevailing patterns and issues. The results indicate that most college students are knowledgeable about phishing methods, but many do not recognize the dangers of phishing. Because of this, AI-based detection systems have potential but also face issues relating to accuracy, false positives, and user faith. This research highlights the importance of bolstering cybersecurity education and ongoing enhancements to AI models to improve phishing detection. Future studies should include a more representative sample, evaluate AI detection systems in real-world settings, and assess longer-term changes in phishing-related awareness. By combining AI-driven solutions with education a safer digital world can created.<\/jats:p>","DOI":"10.3390\/bdcc9080210","type":"journal-article","created":{"date-parts":[[2025,8,15]],"date-time":"2025-08-15T08:39:07Z","timestamp":1755247147000},"page":"210","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["AI-Based Phishing Detection and Student Cybersecurity Awareness in the Digital Age"],"prefix":"10.3390","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1520-1799","authenticated-orcid":false,"given":"Zeinab","family":"Shahbazi","sequence":"first","affiliation":[{"name":"Research Environment of Computer Science (RECS), Kristianstad University, 29188 Kristianstad, Sweden"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rezvan","family":"Jalali","sequence":"additional","affiliation":[{"name":"Department of Computer and Systems Science, Stockholm University, 16440 Stockholm, Sweden"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maryam","family":"Molaeevand","sequence":"additional","affiliation":[{"name":"Department of Computer and Systems Science, Stockholm University, 16440 Stockholm, Sweden"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,8,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"525","DOI":"10.1177\/1094428116677299","article-title":"Big data methods: Leveraging modern data analytic techniques to build organizational science","volume":"21","author":"Tonidandel","year":"2018","journal-title":"Organ. Res. Methods"},{"key":"ref_2","first-page":"82","article-title":"The role of artificial intelligence in detecting and preventing cyber and phishing attacks","volume":"11","author":"Naseer","year":"2024","journal-title":"Eur. J. Adv. Eng. Technol."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Nageab, W.M., Alrasheed, R., and Khalifa, M. (2024, January 28\u201329). Cybersecurity in the era of artificial intelligence: Risks and solutions. Proceedings of the 2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS), Manama, Bahrain.","DOI":"10.1109\/ICETSIS61505.2024.10459584"},{"key":"ref_4","unstructured":"Dine, F. (2024, September 01). Enhancing Phishing Threat Detection and Resilience: Leveraging Machine Learning, AI, and User Education in Cybersecurity. Available online: https:\/\/www.researchgate.net\/profile\/Faizal-Dine\/publication\/384367000_Enhancing_Phishing_Threat_Detection_and_Resilience_Leveraging_Machine_Learning_AI_and_User_Education_in_Cybersecurity\/links\/66f643889e6e82486ff36820\/Enhancing-Phishing-Threat-Detection-and-Resilience-Leveraging-Machine-Learning-AI-and-User-Education-in-Cybersecurity.pdf."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Chinnasamy, P., Krishnamoorthy, P., Alankruthi, K., Mohanraj, T., Kumar, B.S., and Chandran, L. (2024, January 14\u201316). AI Enhanced Phishing Detection System. Proceedings of the 2024 Third International Conference on Intelligent Techniques in Control, Optimization and Signal Processing (INCOS), Krishnankoil, India.","DOI":"10.1109\/INCOS59338.2024.10527485"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"91","DOI":"10.4236\/jcc.2024.1212007","article-title":"Artificial Intelligence in Cybersecurity to Detect Phishing","volume":"12","author":"Kiseki","year":"2024","journal-title":"J. Comput. Commun."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Kulkarni, A., Balachandran, V., Divakaran, D.M., and Das, T. (2024, January 3\u20137). Mitigating Bias in Machine Learning Models for Phishing Webpage Detection. Proceedings of the 2024 16th International Conference on COMmunication Systems & NETworkS (COMSNETS), Bangalore, India.","DOI":"10.1109\/COMSNETS59351.2024.10427170"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Shahbazi, Z., Jalali, R., and Shahbazi, Z. (2025). AI-Driven Sentiment Analysis for Discovering Climate Change Impacts. Smart Cities, 8.","DOI":"10.3390\/smartcities8040109"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"606","DOI":"10.51594\/csitrj.v5i3.909","article-title":"Data privacy and security in it: A review of techniques and challenges","volume":"5","author":"Farayola","year":"2024","journal-title":"Comput. Sci. IT Res. J."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"8373","DOI":"10.1109\/ACCESS.2024.3351946","article-title":"Enhancing phishing detection: A novel hybrid deep learning framework for cybercrime forensics","volume":"12","author":"Alsubaei","year":"2024","journal-title":"IEEE Access"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"104317","DOI":"10.1016\/j.cose.2025.104317","article-title":"Detection and Prevention of Spear Phishing Attacks: A Comprehensive Survey","volume":"151","author":"Birthriya","year":"2025","journal-title":"Comput. Secur."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Alotaibi, B. (2025). Cybersecurity Attacks and Detection Methods in Web 3.0 Technology: A Review. Sensors, 25.","DOI":"10.3390\/s25020342"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Shahbazi, Z., Jalali, R., and Shahbazi, Z. (2025). AI-Driven Framework for Evaluating Climate Misinformation and Data Quality on Social Media. Future Internet, 17.","DOI":"10.3390\/fi17060231"},{"key":"ref_14","unstructured":"Rudi, R. (2023). An Analysis of Phishing Susceptibility Through the Lens of Protection Motivation Theory. [Master\u2019s Thesis, University of Agder]."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Tirumala, S.S., Sarrafzadeh, A., and Pang, P. (2016, January 12\u201314). A Survey on Internet Usage and Cybersecurity Awareness in Students. Proceedings of the 2016 14th Annual Conference on Privacy, Security and Trust (PST), Auckland, New Zealand.","DOI":"10.1109\/PST.2016.7906931"},{"key":"ref_16","unstructured":"Johnson, J.j. (2024). Public School and District Leaders\u2019 Understanding of Cybersecurity Practices and Policies. [Ph.D. Thesis, Walden University]."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Uddin, M.A., and Sarker, I.H. (2024). An explainable transformer-based model for phishing email detection: A large language model approach. arXiv.","DOI":"10.2139\/ssrn.4785953"},{"key":"ref_18","unstructured":"Nahmias, D., Engelberg, G., Klein, D., and Shabtai, A. (2024). Prompted contextual vectors for spear-phishing detection. arXiv."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Altwaijry, N., Al-Turaiki, I., Alotaibi, R., and Alakeel, F. (2024). Advancing phishing email detection: A comparative study of deep learning models. Sensors, 24.","DOI":"10.3390\/s24072077"},{"key":"ref_20","unstructured":"Lim, B., Huerta, R., Sotelo, A., Quintela, A., and Kumar, P. (2025). EXPLICATE: Enhancing Phishing Detection through Explainable AI and LLM-Powered Interpretability. arXiv."},{"key":"ref_21","unstructured":"Heiding, F., Lermen, S., Kao, A., Schneier, B., and Vishwanath, A. (2024). Evaluating Large Language Models\u2019 Capability to Launch Fully Automated Spear Phishing Campaigns: Validated on Human Subjects. arXiv."},{"key":"ref_22","unstructured":"Evans, K., Abuadbba, A., Wu, T., Moore, K., Ahmed, M., Pogrebna, G., Nepal, S., and Johnstone, M. RAIDER: Reinforcement-aided spear phishing detector. Proceedings of the International Conference on Network and System Security."},{"key":"ref_23","unstructured":"Hazell, J. (2023). Spear phishing with large language models. arXiv."},{"key":"ref_24","unstructured":"Francia, J., Hansen, D., Schooley, B., Taylor, M., Murray, S., and Snow, G. (2024). Assessing AI vs. human-authored spear phishing sms attacks: An empirical study using the trapd method. arXiv."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"116","DOI":"10.33093\/jiwe.2024.3.2.9","article-title":"Unveiling the efficacy of AI-based algorithms in phishing attack detection","volume":"3","author":"Shahzad","year":"2024","journal-title":"J. Inform. Web Eng."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Kyaw, P.H., Gutierrez, J., and Ghobakhlou, A. (2024). A Systematic Review of Deep Learning Techniques for Phishing Email Detection. Electronics, 13.","DOI":"10.3390\/electronics13193823"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Ahmed, S.K. (2024). Research Methodology Simplified: How to Choose the Right Sampling Technique and Determine the Appropriate Sample Size for Research. Oral Oncol. Rep., 100662.","DOI":"10.1016\/j.oor.2024.100662"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1186\/s13673-018-0128-7","article-title":"User characteristics that influence judgment of social engineering attacks in social networks","volume":"8","author":"Albladi","year":"2018","journal-title":"Hum.-Centric Comput. Inf. Sci."},{"key":"ref_29","first-page":"3","article-title":"Phishing attacks: A security challenge for university students studying remotely","volume":"15","author":"Nyasvisvo","year":"2023","journal-title":"Afr. J. Inf. Syst."},{"key":"ref_30","unstructured":"Karunarathna, I., Gunasena, P., Hapuarachchi, T., and Gunathilake, S. (2024). The Crucial Role of Data Collection in Research: Techniques, Challenges, and Best Practices, Uva Clinical Research Lab."},{"key":"ref_31","unstructured":"Karunarathna, I., Gunasena, P., Hapuarachchi, T., and Gunathilake, S. (2024, September 01). Data Collection Fundamentals: A Guide to Effective Research Methodologies and Ethical Practices. Available online: https:\/\/www.researchgate.net\/publication\/383155577_Data_Collection_Fundamentals_A_Guide_to_Effective_Research_Methodologies_and_Ethical_Practices."},{"key":"ref_32","unstructured":"Smith, S. (2023). Investigating Factors that Increase Vulnerability to Cyber-Attacks During the First Year College Transition. [Ph.D. Thesis, Purdue University]."}],"container-title":["Big Data and Cognitive Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2504-2289\/9\/8\/210\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T18:28:05Z","timestamp":1760034485000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2504-2289\/9\/8\/210"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,15]]},"references-count":32,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2025,8]]}},"alternative-id":["bdcc9080210"],"URL":"https:\/\/doi.org\/10.3390\/bdcc9080210","relation":{},"ISSN":["2504-2289"],"issn-type":[{"value":"2504-2289","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,8,15]]}}}