Bibliographic record
Abstract
The Internet and related technologies have enabled companies to automate almost all of their operations resulting in enhanced efficiencies and cost-effectiveness. The technologies, however, have also introduced numerous security risks. Through security risks such as Electronic Hacking (EH), individuals and companies have lost a lot of valuable data and money. In this regard, there is a need to understand the extent of the threat of EH. A comprehensive thematic review and analysis of EH with a focus on developments, evolution, challenges, prognosis, and prevalence in select institutions was thus conducted. The research involved reviewing the literature on cybersecurity and its effect on organizations' operations. The result shows that cases of security breaches and associated costs continue to increase. Over five years, the healthcare and medical institutions were the most vulnerable. They were closely followed by corporations. The implications are that as institutions become more automated, their respective degrees of cybercrime vulnerability increase. The consequences of security breaches are normally dire for companies, as well as individuals. Millions, or possibly billions, of dollars worth of data, have been lost as a result of security breaches. This trend is expected to continue in the future, as computers and Internet technologies continue to advance. Through cybercrimes, numerous companies' operations have been sabotaged, and personal information from social media and email stolen. Long term, effective and sustainable strategies are therefore required. The paper is significant because it identifies the information security risks various organizations are exposed to and strategies that organizations can use to mitigate the risks.
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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".