Multi-Objective Evolutionary Optimization for Worst-Case Analysis of False Data Injection Attacks in the Smart Grid
Bibliographic record
Abstract
False data injection attacks (FDIA) have drawn significant interests recently after the discovery of vulnerabilities of bad data detectors (BDD) deployed in the smart grid. While most FDIA analyses focused separately on the aspects of stealthiness, knowledge, resources, or expected consequences of the attack, few have evaluated the relationship and tradeoffs among these factors to identify the worst-case scenario in realistic operations. To fill the gap, this paper investigates a strictly stealthy FDIA scheme with multi-objective evolutionary optimization, which could compromise a small set of meters to inflict large impacts on the smart grid in realistic scenarios. Compared with existing attack schemes that relax the problem with the ℓ1-norm, the paper introduced the Improved Strength Pareto Evolutionary Algorithm (SPEA2) as the solver to directly obtain the ℓ0-sparse attack vector. Meanwhile, the unobservability is ensured by not only bypassing the BDD but also satisfying the physical and operational constraints. A three-step constraint handling technique is also proposed for the SPEA2 to ensure the stealthiness and improve the efficiency of attack vector identification in the worst-case scenario. Simulation results on the IEEE 14-bus and 30-bus systems demonstrate that the new multi-objective formulation discovers highly sparse attack vectors with significant impacts on the system without triggering immediate emergency responses. The influence of alternative objectives and constraints has also been evaluated to reveal the trade-offs among the attack's stealthiness, sparsity, and impact. The results are expected to facilitate better-informed risk assessment and mitigation with refined worst-case understandings.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".