Adaptively Supervised and Intrusion-Aware Data Aggregation for Wireless Sensor Clusters in Critical Infrastructures
Bibliographic record
Abstract
Wireless sensor networks have become integral components of the monitoring systems for critical infrastructures such as the power grid or residential microgrids. Therefore, implementation of robust Intrusion Detection Systems (IDS) at the sensory data aggregation stage has become of paramount importance. Key performance targets for IDS in these environments involve accuracy, precision, and the receiver operating characteristics which is a function of the sensitivity and the ratio of false alarms. Furthermore, the interplay between machine learning and networked systems has led to promising opportunities, particularly for the system level security of wireless sensor networks. Pursuant to these, in this paper, we propose Adaptively Supervised and Clustered Hybrid IDS (ASCH-IDS) for wirelessly connected sensor clusters that monitor critical infrastructures. The proposed ASCH-IDS mechanism is built on a hybrid IDS framework, and transforms the previous work by continuously monitoring the behavior of the receiver operating characteristics, and adaptively directing the incoming packets at a sensor cluster towards either misuse detection or anomaly detection module. We evaluate the proposed mechanism by introducing real attack data sets into simulations, and show that our proposal performs at 98.9% detection rate and approximately 99.80% overall accuracy to detect known and unknown malicious behavior in the sensor network.
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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 | 0.001 | 0.002 |
| 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 | 0.001 | 0.001 |
| 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 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".