Bibliographic record
Abstract
As we venture further into the digital age, the landscape of cyber security and forensics is rapidly evolving. This paper explores the emerging trends and future directions in these critical fields, focusing on the technological advancements and strategic approaches necessary to protect against increasingly sophisticated cyber threats. Key areas of exploration include the integration of cloud forensics, social crime forensics, IoT, and the development of advanced forensic techniques to keep pace with new types of cybercrimes. These technologies enhance the ability to predict, identify, and mitigate cyber attacks more efficiently than traditional methods. The advent of quantum computing, while promising unprecedented computational power, also poses significant risks to current cryptographic standards, necessitating the development of quantum-resistant encryption algorithms. In cyber forensics, advancements focus on tracking and analysing cybercriminals’ complex digital footprints. Enhanced forensic tools and methodologies are essential for investigating sophisticated cybercrimes, ranging from data breaches to ransomware attacks. The paper concludes by emphasising the importance of a multi-faceted approach to cyber security and forensics, combining technological innovation, regulatory frameworks, and international cooperation. As cyber threats evolve, so must our defences, protecting sensitive information and maintaining trust in digital systems.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.197 | 0.048 |
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".