Digital hacking and cyber-attacks: cyber security from Islamic perspective
Bibliographic record
Abstract
As the number of devices connected to the Internet increase and the amount of data available online grow, the cyber-attacks will keep increasing in number and their severity. For Malaysia specifically, as of the second quarter of 2022, a total of 44.2 million accounts have been breached putting the security, privacy, and confidentiality of the account owners’ data in danger. Cyber hacking can become a strong power which can be harmful if the necessary skills are learned without ethical standards. On the other hand, learning the skills for ethical hacking became a necessity to protect digital systems, discover vulnerabilities and mitigate them. An Islamic approach to cyber security issues is presented in this paper. As Islam is a revealed religion from Allah SWT to be guidance to the Muslims in all matters, we can derive many ethical standards in any field following the holy Quran and the Sunnah of the Prophet (Peace be upon him).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.007 | 0.005 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".