Cyber Security Challenges in the Post-Pandemic Digital Landscape
Bibliographic record
Abstract
The world has started using more online activities like Bank transactions, social profiles etc after covid-19 pandemic, at the same time cyber security attacks have started trying to stealing the data and money from the users by using different methods. In this view the attacker has focused on social engineering attacks like phone call, messages ,Dumpster diving, shoulder surfing, emails, ads, pop-ups, and phishing etc, in obtaining the users critical data and trying to be more effective in their attacks. Accordingly many people have turned on to online work at the time of pandemic (covid-19). The cyber security agencies like CERT-In i.e., Indian computer emergency response team has issued warnings that the cyber threat attackers are increasing and that they are improving in terms of stealing money, personal information and intellectual property. The number of attacks has increased significantly during pandemic to more than 35% and also there has been an increasing vulnerability in cyber security among the government sector, businesses and individuals worldwide.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.014 | 0.016 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".