Specification-based Intrusion Detection for home area networks in smart grids
Bibliographic record
Abstract
Achievement of the goals of smart grid such as resilience, high power quality, and consumer participation strongly depends on the security of this system. Along with the security measures that should be built into the smart grid from the beginning, appropriate Intrusion Detection Systems (IDSs) should also be designed. Home area network (HAN) is one of the most vulnerable subsystems within the smart grid, mostly because of its physically insecure environment. In this paper, we present a layered specification-based IDS for HAN. Considering that ZigBee is the dominant technology in future HAN, our IDS is designed to target ZigBee technology; specifically we address the physical and medium access control (MAC) layers. In our IDS the normal behavior of the network is defined through selected specifications that we extract from the IEEE 802.15.4 standard. Deviations from the defined normal behavior can be a sign of some malicious activities. We further investigate the physical and MAC layer attacks in ZigBee networks and evaluate the performance of our proposed IDS against them. Our IDS provides a good detection capability against known attacks, and since this is an IDS based on anomalous event detection, we expect the same for unknown attacks.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".