Exploring the role of behavioral analytics and anomaly detection in securing mobile networks for critical infrastructure
Bibliographic record
Abstract
Mobile networks have an ever-increasing presence in critical infrastructure, enabling just-in-time monitoring and real-time control in various domains, from energy and transport to healthcare. However, the security of these systems is now vulnerable to new cybersecurity threats from this integration. Evaluating the potential of behavioral analytics and anomaly detection in securing mobile networks for critical infrastructure. This paper presents a novel Multi-Layer Adaptive Anomaly Detection System (MAADS) that utilizes recent advances in machine learning (ML) to identify anomalies and detect novel threats in mobile networks. MAADS detects anomalies in mobile networks across three layers: network traffic, user mobility, and device behavior. It does this through privacy-preserving data collection, multi-model anomaly detection, and an adaptive response framework. The empirical evaluation of the proposed system shows that MAADS can detect anomalies with 95% precision and 93% recall. It is demonstrated to identify 87% of anomalies not detected by standard ML-based systems. It also maintains its performance up to 100,000 nodes and consistently performs across other critical infrastructure sectors. The analysis and comparison of MAADS with existing solutions show that it outperforms existing techniques in terms of the ability to adapt to novel threats and the explainability of the detected anomalies.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".