Bibliographic record
Abstract
Cybersecurity has emerged as one of the largest challenges in the 21st century. State-sponsored actors and other/violent groups can compromise information technology (IT) networks, gain access to IT networks, steal sensitive data from IT and operational technology (OT) networks, and impede essential industrial control systems (ICS)/OT operations through cyberattacks. On April 22, 2022, the US government's “Cyber Security and Infrastructure Security Agency” warned about malicious cyber threats to critical infrastructure. Critical infrastructure network defenders are being advised by cybersecurity experts from the US, Australia, Canada, New Zealand, and the UK to strengthen their cyber defenses and take reasonable precautions to spot signs of malicious activity to be ready for and mitigate potential cyber threats, such as ransomware, distributed denial of service (DDoS) attacks, etc. During heightened conflict and war-like scenarios, it has been observed that cybercriminal groups have threatened to attack various countries either to fulfill their demand or for retaliation. This paper advances the research about information warfare and the potential risks by examining the risk of cyberterrorism and how the States can use cyberspace in warlike situations. The study uses the case of the ongoing war between Russia and Ukraine to highlight the threats and challenges of cyberterrorism and information warfare. The paper highlights how the current legislative and policy frameworks to fight information warfare and cyberterrorism are inadequate. The paper recommends reforms in the landscape of cyber protection where information warfare can be defeated without infringing on the fundamental rights of citizens.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.016 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 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".