An Efficient Cluster Based Multi-Label Classification Model for Advanced Persistent Threat Attacks Detecting
Bibliographic record
Abstract
In response to escalating cyber threats, there is an urgent need for adaptive detection mechanisms.This study introduces a cyber threat detection framework employing ensemble learning and a hybrid feature ranking approach.Designed to address diverse and evolving threats, the framework aims to enhance detection accuracy in dynamic environments.The framework comprises three key components.Firstly, an ensemble feature ranking algorithm identifies influential features in imbalanced datasets, ensuring effective threat detection while mitigating imbalanced class impact.Secondly, a hybrid feature ranking measure (HFRM) integrates fusion entropy to assess feature importance comprehensively.HFRM combines information gain, entropy, and proposed fusion entropy for a holistic ranking.Thirdly, the framework includes a multi-class k-means rank-based classification for efficient clustering and threat categorization.Evaluation using diverse datasets underscores the framework's effectiveness in achieving high detection accuracy and robustness across threat scenarios.The ensemble approach, hybrid feature ranking, and rank-based classification collectively provide an adaptive solution for cyber threat detection.In conclusion, this research introduces an innovative framework integrating ensemble learning, hybrid feature ranking, and k-means clustering, promising more resilient cybersecurity in the face of sophisticated threats.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".