Hybrid AI-Powered Framework for Proactive Cyberattack Detection Using Multi-Dimensional Network Metadata and Anomaly Scoring
Bibliographic record
Abstract
The rapid evolution of cyber threats demands innovative solutions for real-time detection and mitigation. This paper presents a novel hybrid AI-powered framework for detecting cyberattacks by leveraging a multi-dimensional dataset comprising 40,000 network records. The dataset integrates key features, including IP addresses, protocols, anomaly scores, attack signatures, and geo-location data, providing a rich foundation for threat analysis. Our approach combines machine learning techniques with advanced feature engineering to identify patterns indicative of malicious activities. By employing supervised learning algorithms and anomaly detection models, the proposed framework achieves high accuracy in differentiating between benign and malicious traffic. Furthermore, the framework incorporates metadata analysis to enhance interpretability and facilitate informed decision-making. Experimental results demonstrate the model’s ability to detect diverse cyber threats, including fileless malware, DDoS attacks, and protocol-based exploits, with improved precision and reduced false positives compared to existing solutions. The findings highlight the effectiveness of leveraging multi-dimensional data and AI-driven methodologies for proactive cybersecurity. This study underscores the potential of integrating comprehensive datasets and advanced analytics to enhance cyber defense mechanisms. The proposed framework sets the stage for future research in real-time threat detection and response, contributing to the broader goal of securing digital ecosystems.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| 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".