Securing Substations with Trust, Risk Posture, and Multi-Agent Systems: A Comprehensive Approach
Bibliographic record
Abstract
The Smart Grid is an IT-integrated power grid that generates, transmits, and distributes electricity to households and businesses. The substation is a crucial element of the Smart Grid’s operation, which adjusts voltages during the entire process. The integration of IT has increased in the substation’s attack surfaces. Sophisticated attacks such as the Pipeline APT contain multi-protocol modules for various devices. Performance constraints make substations a unique case; hence it is challenging to implement encryption and intrusion detection systems. We believe trust can tackle this problem. We present an improved trust model that detects protocol-based attacks toward an IED/SCADA HMI. This model is included within a multi-agent-based trust management system that computes the substation’s risk posture. Our proposed design was implemented in a Docker-based testbed environment with a SOC-influenced dashboard to provide real-time updates. The implementation was subjected to three attack scenarios: external attack, internal attack from compromised SCADA HMI, and internal attack from a compromised non-trusted IED. We observed that our model was robust against all attacks except for the baseline replay and delay response attacks. Detecting these attacks will be considered for future work as well as trust transferability. Our institute’s website provides a publicly available dataset containing captures of our MAS testbed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".