Assessing Insider Attacks and Privacy Leakage in Managed IoT Systems for Residential Prosumers
Bibliographic record
Abstract
The transition towards the massive penetration of Renewable Energy Resources (RESs) into the electricity system requires the implementation of the Smart Grid (SG) paradigm with innovative control systems and equipment. In this new context, Distributed Energy Resources (DERs), including renewable sources and responsive loads, should be redesigned to enable aggregators to provide ancillary services. In fact, by using the Internet of Things (IoT) systems, aggregators can explore energy usage patterns from residential users, also known as prosumers and predict their services. This is undoubtedly important especially for SGs facing the presence of several RESs, where understanding the optimal match between demand and production is desirable from several points of view. However, revealing energy patterns and information can be of concern for privacy if the entire system is not properly designed. In this article, by assuming that the security of low-level communication protocols is guaranteed, we focus our attention at higher levels, in particular at the application level of managed IoT systems used by aggregators. In this regard, we provide an overview of the best practices and outline possible privacy leakages risks along with a list of correlated attacks.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".