AI-Based Phishing Detection and Student Cybersecurity Awareness in the Digital Age

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’ 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’ 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.

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Institutions

Publication Details

Journal
Big Data and Cognitive Computing
Published
2025-08-15
DOI
https://doi.org/10.3390/bdcc9080210
Citations
1
Primary Topic
Spam and Phishing Detection
Type
article
Field-Weighted Citation Impact
3.25
Controls
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article

AI-Based Phishing Detection and Student Cybersecurity Awareness in the Digital Age

Zeinab Shahbazi, Rezvan Jalali, Maryam Molaeevand
1 citations
Big Data and Cognitive Computing
Spam and Phishing Detection
3.25
article

AI-Based Phishing Detection and Student Cybersecurity Awareness in the Digital Age

Zeinab Shahbazi, Rezvan Jalali, Maryam Molaeevand
article en
1 citations

Abstract

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’ 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’ 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.

Big Data and Cognitive ComputingVol. 9(8)
Stockholm University (SE), Kristianstad University (SE)
Openalex Percentile: Top 7%
Spam and Phishing Detection
3.25
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AI-Based Phishing Detection and Student Cybersecurity Awareness in the Digital Age — Zeinab Shahbazi, Rezvan Jalali, et al. · Big Data and Cognitive Computing (2025) | TGRS Research Map | TGRS