Proactively Defending Enterprise Computer Systems Against Threats and Vulnerabilities
Bibliographic record
Abstract
Cybersecurity remains a critical concern, even amidst global events like the COVID-19 pandemic. The rise of COVID-19 has deepened cybersecurity threats, with phishing emails and phone scams attempting to exploit the situation. This paper focuses on proactive strategies to protect enterprise environments against threats and vulnerabilities, specifically in Windows-based systems. Tracing threats and vulnerabilities to their source aims to address them in their early stages rather than after an attack has occurred. This research tackles three prevalent issues: phishing emails, vulnerability patching, and industrial internet-connected devices. Through analyzing various cyber defense models and vulnerability databases, this paper proposes frameworks to mitigate these issues effectively. The study includes a detailed examination of sources of threats and vulnerabilities, aiming to develop methodologies for practical implementation. Ultimately, the goal is to summarize best practices to enhance tool utilization and process improvement and propose new proactive defense methods. The research emphasizes the shift from reactive to proactive defense strategies to better protect enterprise networks.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".