Detection of Known and Unknown Intrusive Sensor Behavior in Critical Applications
Bibliographic record
Abstract
This article presents a hybrid architecture to identify intrusive behavior among networked sensors that monitor critical systems such as environment, medical, and smart grid. Monitoring through sensors is desired for critical applications such as epilepsy seizures, pollution, power quality assessment, and transformer monitoring. Wireless sensors are being widely used in critical applications due to their advantages including low-cost, flexibility, and communication efficiency. However, when sensors are networked to monitor a critical infrastructure such as the smart grid, they become the target of different types of attackers such as intruders via the communication medium. In order to maintain sensing in a secure manner, robust architectures are needed to identify intrusive behavior of sensors in a network. In this article, we present a hybrid architecture to detect intrusive behavior of sensors for both unknown and known intruders. The former requires anomaly detection, whereas the latter requires signature detection. The proposed architecture consists of two subsystems that co-operate to detect unknown and known attacks through duty-cycling of enhanced density-based spatial clustering of applications with noise and random forest methods. Through various tests on real intrusion data, we show that the proposed architecture has a strong potential to detect both known and unknown intrusive behavior of sensor nodes as the results show 99.73% detection rate with 98.95% overall accuracy.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".