Cyber Security amid COVID-19
Bibliographic record
Abstract
COVID-19 pandemic obliged thousands of companies pertaining to all economic sectors to undergo the transformation from on-board work to working from home. Along such rush, the probability for companies being hacked incremented many folds. According to VMware cybersecurity strategist Tom Kellermann, quoted in Menn (2020), “There is a digitally historic event occurring in the background of this pandemic, and that is there is a cybercrime pandemic that is occurring” (para 5). In fact, Software and security company VMware Carbon Black declared during April, “that ransomware attacks it monitored jumped 148% in March from the previous month, as governments worldwide curbed movement to slow the spread of the novel corona virus” (Para 4). On the other hand, Anft (2020) reported that “more than 500 educational institutions, including colleges and K-12 schools, faced ransom attacks in 2019” (para 2). This paper uses a descriptive qualitative approach to shed light on the aforementioned subject depending on reported secondary literature about the topic, and offers an analysis to pinpoint weaknesses and barriers, as well as best practices to counterattack the breaches to cybersecurity in organizations. The outcomes serve as an eye opener for security officers in charge of the safety of organizational intellectual properties and stimulates organizations to adopt protection systems and safety practices.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.014 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 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".