Cyber-criminology defense in pervasive environment: A study of cybercrimes in Malaysia
Bibliographic record
Abstract
The growth of technologies; infrastructures and platforms with less or no security protection in emerging big data and internet of things (IoT) trends increase the likelihood of cybercrime attacks. With the rise of coronavirus disease-2019 (Covid-19) pandemic towards mankind, more cybercrimes are designed to penetrate one’s cognitive mind in revealing sensitive details. In this paper; an exploration of cybercrime threats in Southeast Asia country; Malaysia from year 2008 up to 2020 and its hike trends and impacts will be discussed. An investigation revolving the study of cyber-criminology and the reasoning behind the growth in terms of technological advancement will be presented. The findings suggest that the consequences and impacts of the cyberspace attacks are beyond the loss of money and reputations. It now becomes the failure of the global systemic altogether. As a mechanism to handle this would be to focus on protecting mission critical applications in pervasive environment. In this paper, a comprehensive authentication and authorization framework in safeguarding applications and users in the pervasive environment will be presented.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".