{ "issue_key": "vol10-no2-2026", "volume": 10, "number": 2, "season": "Spring 2026", "year": 2026, "sequence": 8, "type": "research-article", "section": "Research Article", "title": "Investigating the Impact of Cyber Security Awareness Training in the field of Deepfake on Students", "subtitle": null, "slug": "investigating-the-impact-of-cyber-security-awareness-training-in-the-field-of", "corporate_author": null, "authors": [ { "given_name": "Mohammad Shahadat", "family_name": "Hossain", "credentials": "MSc", "affiliation": "London Churchill College", "email": "m.hossain@lcc.ac.uk", "orcid": null, "is_corresponding": true, "sequence": 1 } ], "abstract": "This research investigates how training about cybersecurity affects students’ capacity to identify deepfake content during its rapid expansion across digital information platforms. The production of synthetic media by AI threatens personal security at the same time that it creates substantial concerns about false information and declining social trust. The research design employs hypothetico-deductive principles through a pre-post assessment approach which studies 21 graduate students at the University of Staffordshire who are enrolled at Level 7. Research data was collected by two questionnaires before and after training which were evaluated using paired sample t-tests. The results demonstrated a significant statistical improvement across several domains. Subsequent research should utilise a variety of comprehensive data sets and develop longitudinal study approaches to ascertain long-term outcomes, as well as explore how deepfake literacy integrates into digital literacy programmess. The research findings establish that purposeful cybersecurity education can provide the necessary protective abilities to individuals specifically including students so they can face present and emerging security threats effectively.", "keywords": [ "Deepfake", "Cyber Security", "Training" ], "first_page": 42, "last_page": 58, "pdf_start": 43, "pdf_end": 59, "references": [ { "ordinal": 1, "raw_text": "Belghazi, Nadia, et al. (2023). Is Training Useful to Detect Deepfakes? : A Preliminary Study.. Aveiro, Portugal, IEEE.", "doi": null, "markdown": "Belghazi, Nadia, *et al*. (2023). *Is Training Useful to Detect Deepfakes? : A Preliminary Study*.. Aveiro, Portugal, IEEE." }, { "ordinal": 2, "raw_text": "Bhalli, Nashaba, et al. (2024) Listening for Expert Identified Linguistic Features: Assessment of Audio Deepfake Discernment among Undergraduate Students. Research Gate Preprint. DOI 10.48550/arXiv/2411.14586.", "doi": "10.48550/arXiv/2411.14586", "markdown": "Bhalli, Nashaba, *et al*. 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Participant Demographics Overview.", "pdf_page": 48, "text_in_image": null }, { "kind": "table", "file": "figures/table-01-p48.png", "caption": "TABLE 1: Pre- and Post-Training Responses/Results Summary:", "pdf_page": 48, "rows": [ [ "Question No", "Variables/ Focus", "Pre-Training Responses", "Post-Training Responses", "Insight" ], [ "Q4", "Perception/ Awareness", "52.4% Yes, 47.6% No", "100% Yes", "Knowledge gap closed" ], [ "Q5", "Confidence", "Mean=2.24 (SD=1.18); Most rated 1–3", "Mean=4.48 (SD=1.03); Most rated 4–5", "Strong confidence gain" ], [ "Q6", "Performance/ Indicators", "Mean=1.0 (SD=0.0); Mostly incorrect indicators", "Mean=2.81 (SD=0.75); Mostly correct indicators", "Improved identification skills" ], [ "Q7", "Perception/ Encountered Deepfake", "33.3% Yes, 38.1% Not Sure", "76.2% Yes, 19.0% Not Sure", "Recognition skill improved" ], [ "Q8", "Perception/ Perceived Uses", "Cybercrime: 61.9%; Entertainment: 33.3%", "Cybercrime: 95.2%; Broader recognition overall", "Greater awareness of malicious uses" ], [ "Q9", "Perception/ Threat Perception", "Mean=3.29 (SD=1.52); Mixed threat awareness", "Mean=4.76 (SD=0.54); Stronger agreement on threat", "Heightened threat perception" ], [ "Q10", "Perception/ Vulnerable Industries", "28.6% selected all four; 47.6% selected one", "90.5% selected all four", "Near-complete awareness" ], [ "Q11", "Engagement/ Fallen for Fakes", "28.6% Yes, 38.1% Not Sure", "61.9% Yes, 19.0% Not Sure", "Higher reporting, not necessarily higher risk" ], [ "Q12", "Behaviour/ Verification Frequency", "28.6% Never; 14.3% Always", "81.0% Always", "Big behavioral shift" ], [ "Q13", "Performance/ Verification Tools", "47.6% used none; 23.8% used all three", "76.2% used all three", "Significant tool adoption" ], [ "Q14", "Engagement/ Information Sources", "57.1% Social Media", "71.4% Academic/News", "More credible sources adopted" ], [ "Q15", "Behaviour/ Actions Taken", "66.7% Ignore; 0% Discuss", "85.7% Verify; 23.8% Discuss", "Proactive action post-training" ], [ "Q16", "Perception/ Future Difficulty", "61.9% Yes, 23.8% Not Sure", "90.5% Yes, 9.5% Not Sure", "Growing concern about detection" ], [ "Q17", "Perception/ Concerns", "47.6% Not Sure", "Misinformation: 57.1%, Social Impact: 38.1%", "More specific concerns emerged" ], [ "Q18", "Confidence Needs", "52.4% Not Sure", "71.4% cited training", "Clear preference for further education" ], [ "Q19–Q23", "Post-Only", "N/A", "95%+ satisfied, more skeptical, confident, and aware", "Training well-received and impactful" ] ] }, { "kind": "figure", "file": "figures/fig-04-p49.png", "caption": "Fig. 2. Pre-Post Training Awareness of Deepfake Technology (Q4).", "pdf_page": 49, "text_in_image": null }, { "kind": "figure", "file": "figures/fig-05-p50.png", "caption": "Fig. 3. Pre- and Post-Training Confidence in Identifying Deepfake Videos (Q5).", "pdf_page": 50, "text_in_image": null }, { "kind": "figure", "file": "figures/fig-06-p50.png", "caption": "Fig. 4. Pre and Post Training Selection of Deepfake Indicators (Q6).", "pdf_page": 50, "text_in_image": null }, { "kind": "figure", "file": "figures/fig-07-p51.png", "caption": "Fig. 5. Perceived threat levels Q9", "pdf_page": 51, "text_in_image": null }, { "kind": "figure", "file": "figures/fig-08-p51.png", "caption": "Fig. 6. Awareness of vulnerable industries- Perception (Q10)", "pdf_page": 51, "text_in_image": null }, { "kind": "figure", "file": "figures/fig-09-p52.png", "caption": "Fig. 7. Verification behaviours (Q12).", "pdf_page": 52, "text_in_image": null }, { "kind": "figure", "file": "figures/fig-10-p52.png", "caption": "Fig. 8. Verification Tools (Q13. Performance)", "pdf_page": 52, "text_in_image": null }, { "kind": "figure", "file": "figures/fig-11-p53.png", "caption": "Fig. 9. Sources of information (Q14) Engagement", "pdf_page": 53, "text_in_image": null }, { "kind": "figure", "file": "figures/fig-12-p53.png", "caption": "Fig.10. Response actions (Q15) Behaviour.", "pdf_page": 53, "text_in_image": null }, { "kind": "figure", "file": "figures/fig-13-p54.png", "caption": "Fig. 11. Future Difficulty Q16 (Perception).", "pdf_page": 54, "text_in_image": null }, { "kind": "figure", "file": "figures/fig-14-p54.png", "caption": "Fig. 12. Perception of concerns (Q17).", "pdf_page": 54, "text_in_image": null }, { "kind": "figure", "file": "figures/fig-15-p55.png", "caption": "Fig. 13. What can be made them confident to identify deepfakes – Q18.", "pdf_page": 55, "text_in_image": null }, { "kind": "figure", "file": "figures/fig-16-p55.png", "caption": "Fig.14. Post-Training responses.", "pdf_page": 55, "text_in_image": null } ], "files": { "html": "index.html", "body_markdown": "body.md", "pdf": "article.pdf" }, "word_count": 4843, "conversion_warnings": [ "Table 1 (pdf 48-49) rebuilt as one pipe table; the page-break continuation row 'Verification Frequency' was merged into Q12 ('Behaviour/ Verification Frequency'). The table image figures/table-01-p48.png is a stitched crop of both pages.", "Fig. 5 (Q9) chart image as printed has inconsistent legend/axis labels (legend '1-5, 2, 3, 4'; axis 100%/70%/50%...); figures are the author's rasters and cannot be corrected here.", "Fig. 4 (Q6) chart image has truncated pie labels in the source raster (e.g. 'Perfectly Smooth: 47.6%' cut off) - as printed.", "Statistical-analysis run-in labels on pdf 56-57 ('Q5: Confidence (Confidence):', 'Paired t-test:', 'Pre-Training Analysis:' etc.) are kept as bold paragraph labels, not headings.", "Citation/reference mismatches in the source (not corrected): text cites 'Bondi, et al. (2021)' but list has 'Bond, Luca, et al. (2020)'; text 'Naffe' vs list 'Naffi'; 'Kurt, Yesem, et al. (2017)' and 'Yesem Kurt, et al. (2017)' are the same work listed twice; Mekhail DOI printed garbled ('10.10.16/jbusres.2022 113368') so doi left null.", "Source typos kept verbatim: heading 'Quantative', 'quesions', 'Broder recognition', 'emphased', 'programmess' (abstract), 'Techniqus' in Fig. 14 image." ], "manifest_notes": "Byline 'Mohammad Shahadat Hossain' (no degree); MSc, affiliation, email from Notes on Contributors; no ORCID printed. Overview describes it as based on an MSc prize-winning dissertation and calls it a 'case study'. Keywords inline: 'Keywords— Deepfake, Cyber Security, Training.' References heading has trailing full stop 'References.'. 14 chart images (Fig. 1-14), captions are text lines BELOW each image (11pt regular x72, except Fig.1 centred x192 and Fig. 4 italic): Fig.1 pdf 48; Fig.2 image at foot of pdf 49 with caption at top of pdf 50; Figs 3-4 pdf 50; 5-6 pdf 51; 7-8 pdf 52; 9-10 pdf 53; 11-12 pdf 54; 13-14 pdf 55. One uncaptioned 5-column table (Question No | Variables/Focus | Pre-Training Responses | Post-Training Responses | Insight) spans pdf 48-49 with header repeated, drawn with many vector lines; heading 'Results/Pre- and Post-Training Responses:' precedes it. pdf 56-57 contain whole bold body sentences/paragraphs (e.g. 'Pre-training mean = 2.24, ...', 'Mean = 1.0, SD = 0.0. Distribution ...') that are NOT headings, and many short bold run-in labels ending in ':' ('Paired t-test:', 'Q5: Confidence (Confidence):'). Heading typo in source: 'Quantative and Qualitative Methods for Data Analysis'. pdf 45 has shaded line backgrounds (highlight rectangles) - ignore. Uses 'H₀'/'H₁' subscripts (pdf 58).", "source": { "pdf": "vol10-no2-2026.pdf", "ocr": false }, "reviewed": true }