An empirical investigation of the prevalence of spyware in internet shareware and freeware distributions
Bibliographic record
Abstract
Purpose Spyware is a controversial software technology that allows the surreptitious collection of personal information from computers linked to the internet. The purpose of this study was to determine the prevalence of spyware in internet shareware and freeware and to investigate the personal information collected. Design/methodology/approach The methodology was a two‐phase, multi‐case study. The first phase determined that five of the 50 most popular pieces of Windows®‐based freeware and shareware available to internet users from CNETDownload.com were suspected of containing spyware: these were included as data sources for phase two. The purpose of phase two was to confirm the existence of spyware and to identify the types and frequencies of any personal data transmissions. For this phase, data were collected and analyzed utilizing a passive network monitor program to examine packets of data transmitted from a personal computer to external destinations on the internet. Findings The findings confirmed the existence and use of spyware in three of five suspected cases. However, the data indicated that there was a low occurrence of spyware and that these programs have the capability to collect numerous types of personal data. The main limitation is that these results are based on a snapshot of data obtained during five days. Practical implications The study has practical implications for internet users, who should be aware that spyware exists and understand its potential threat. Spyware developers should provide the user with an effective removal tool. Finally, marketers are cautioned that spyware might alienate customers. Originality/value This paper confirms the potential for misuse of these programs.
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 | 0.009 | 0.069 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| 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".