AI-Augmented Big Data Analytics for Real-Time Cyber Attack Detection and Proactive Threat Mitigation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Big data analytics, as used in defense, is the capacity to gather vast amounts of digital data for analysis, visualization, and decision-making that might aid in anticipating and preventing cyberattacks. When combined with security technologies, it improves it position in terms of cyber defense. They enable companies to identify behavioral patterns that point to network dangers. With its potent capabilities to tackle the increasing scope, variety, and complexity of cyberthreats, big data analytics has become a disruptive force in contemporary cybersecurity. Traditional data processing methods fall short in managing the massive volumes, varieties, and velocities (3Vs) characteristic of big data. This paper explores the foundational principles of big data analytics, including its core dimensions and key application areas such as healthcare, transportation, finance, education, and social media. The study further investigates the classification of cyberattacks malware, phishing, ransomware, and advanced persistent threats (APTs) and their evolving complexity due to AI-powered automation, IoT proliferation, and multi-vector intrusion techniques. It is highlighted how crucial big data is to supporting real-time threat detection, predictive modelling, and automated incident response. Techniques such as behavioral analysis, threat intelligence integration, and anomaly detection are examined for their effectiveness in identifying sophisticated attacks like polymorphic malware and zero-day exploits. Ultimately, this paper highlights how big data analytics enhances cybersecurity capabilities by delivering predictive, prescriptive, diagnostic, and cyber-specific insights that empower proactive threat mitigation and ensure digital resilience.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it