Mitigating Propagation of Cyber-Attacks in Wide-Area Measurement Systems
Bibliographic record
Abstract
Wide Area Measurement Systems (WAMSs) are used in power networks to improve the situational awareness of the operator, as well as to facilitate real-time control and protection decisions. In WAMSs, Phasor Data Concentrators (PDCs) collect time-synchronized data of Phasor Measurement Units (PMUs) through the communication system, and direct it to the control center to be used in wide-area control and protection applications. Due to the dependence of WAMSs on information and communication technologies, cyber-attacks can target these systems and propagate through them, i.e., infect a greater number of components by accessing and controlling a few of them. On this basis, this paper initially develops a Learning-Based Framework (LBF) to estimate the required defense strategy to counter the propagation of cyber-attacks in WAMSs. Afterwards, through solving a linear Binary Integer Programming (BIP) problem, this paper develops a mitigation strategy to optimally reconfigure the communication network and reduce the contamination probability for critical PMUs and PDCs while maintaining the observability of the grid. The simulation results obtained from IEEE 14- and 30-bus test systems corroborate the effectiveness of the proposed LBF and communication network reconfiguration strategy in mitigating the propagation of cyber-attacks in WAMSs.
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 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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".