AI-Augmented Big Data Analytics for Real-Time Cyber Attack Detection and Proactive Threat Mitigation
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.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.002 |
| 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".