Self-Healing Security Architectures: Autonomous Threat Detection and Response in Contemporary Cyber Ecosystems
Bibliographic record
Abstract
Breaches are on the rise in the global cybersecurity space at an unprecedented rate: 2, 2 0 0 incidents were reported in 2018 and the forecast is for a 51.2% CAGR to 30,458 breaches per year by 2024. Traditional security operations have been limited, delivering an average Mean Time to Detect (MTTD) of 194 days and Mean Time to Contain (MTTC) of 64 days, thus an extended exposure period. In this work, we propose a fully automated artificial intelligence (AI) and machine learning (ML) based self-healing security architecture with adaptive real-time protection. Sub-5 second detection and containment was observed in large-scale testing across 1. 2 million security incidents across 94 countries, demonstrating false detection loss as low as less than 6% across a wide variety of threat vectors. Compared with conventional SOC operations, the proposed architecture was able to achieve MTTC reduction of 75%, which is much faster remediation. In addition, automation greatly minimized the impact of the human factor - the cause of 68% of present breaches, while not sacrificing system productivity. By incorporating drift detection capabilities, threat intelligence fusion capabilities, and autonomous policy orchestration capabilities, this framework redefines proactive cybersecurity protection.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".