Online Gaming Crime and Security Issue - Cases and Countermeasures from Taiwan.
Bibliographic record
Abstract
Abstract—Along with the growth of information technology, online gaming has become a very successful and outstanding industry, especially in Asia. However due to the lack of legal regulation, security protection, privacy protection and related legislation, more and more players (mostly in the age range of 15-20) have violated the law. A fair number of arrests and prosecutions have occurred related to this. Online gaming is designed for entertainment. However, the cyber-criminal activity arising from online games is increasing at an alarming rate. The numbers of thefts, frauds, robberies, counterfeited documents, vandalisms, threats and illegal gambling cases from online gaming have increased to 1300 cases from 55 only 2 years earlier in Taiwan. In fact, some of these cases involved school dropouts who formed malicious gangs to boost their criminal activity. In other words, online gaming contributes to the increasing number of cyber-crimes, to such an extent that it is the number one cyber-criminal activity in Taiwan (in terms of convictions). More recently, all types of network-related crimes have increased rapidly. As well, the average age of players has decreased. The main objective of our research was to find a way to solve these problems. Index Terms—online gaming, cybercrime, prosecution, privacy, virtual community
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".