Validation of cyber security behaviour among adolescents at Malaysia university: Revisiting gender as a role
Bibliographic record
Abstract
Cyber-attacks and crimes are still a problem in Malaysia. COVID-19 has pushed Malaysians into the digital world more quickly. These cyberattacks may rise and affect more people. Thus, the aim of this study is to find out if there is a significant difference in the level of cyber security behaviour between males and females in the aspects of malware, password usage, phishing, social engineering and online scamming in Malaysia. An online questionnaire survey was used to gather data from Malaysia and received 207 total responses. Cronbach's alpha is used to measure questionnaire items' reliability. A t-test is used to determine the differences between male and female cyber security behaviour. The results show that there is no significant difference between males and females in four aspects out of five, which are malware, password usage, phishing and social engineering. There is a significant difference between males and females in the aspect of online scams. This research helps those who formulate education policies by determining that there is no noticeable gender difference. Men should get the same level of education and training as women. The findings also demonstrate that women's awareness of technology is increasing.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".