An overview on web security threats and impact to e-commerce success
Bibliographic record
Abstract
E-commerce has made great strides in providing a convenient, fast and secure shopping experience for consumers. However, there is still a significant portion of shoppers whose security fears impact how they spend their money online. Because of this, security issues associated with e-commerce and customer sites must be constantly reviewed and updated with appropriate countermeasures. As web security threats detrimentally affect the success of electronic consumerism, it is imperative to educate both consumers and businesses on the issues and how to eliminate or minimize the risks of security breaching in an e-commerce environment. This paper presents a survey and analysis on e-commerce related security issues, the impact to E-commerce success, and the available integrated security strategies. We attempt to offer a simple guide how to properly deal with the security threats that detrimentally affect e-commerce. In addition, this paper provides an analysis on the barriers that prevent many developing countries from adopting e-commerce. Some recommendations on how to overcome these problems will also be provided.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.000 | 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".