Distributed Fault Detection in Formation of Multi-Agent Systems with Attack Impact Analysis
Bibliographic record
Abstract
Autonomous Underwater Vehicles (AUVs) are capable of performing a variety of deepwater marine applications as in multiple mobile robots and cooperative robot reconnaissance. Due to the environment that AUVs operate in, fault detection and isolation as well as the formation control of AUVs are more challenging than other Multi-Agent Systems (MASs). In this thesis, two main challenges are tackled. \n \nWe first investigate the formation control and fault accommodation algorithms for AUVs in presence of abnormal events such as faults and communication attacks in any of the team members. These undesirable events can prevent the entire team to achieve a safe, \nreliable, and efficient performance while executing underwater mission tasks. For instance, AUVs may face unexpected actuator/sensor faults and the communication between AUVs \ncan be compromised, and consequently make the entire multi-agent system vulnerable to cyber-attacks. Moreover, a possible deception attack on network system may have a negative \nimpact on the environment and more importantly the national security. Furthermore, there are certain requirements for speed, position or depth of the AUV team. For this reason, we propose a distributed fault detection scheme that is able to detect and isolate faults in AUVs while maintaining their formation under security constraints. The effects of faults and communication attacks with a control theoretical perspective will be studied. \n \nAnother contribution of this thesis is to study a state estimation problem for a linear dynamical system in presence of a Bias Injection Attack (BIA). For this purpose, a Kalman Filter (KF) is used, where we show that the impact of an attack can be analyzed as the solution of a quadratically constrained problem for which the exact solution can be found efficiently. We also introduce a lower bound for the attack impact in terms of the number of compromised actuators and a combination of sensors and actuators. The theoretical findings are accompanied by simulation results and numerical can study examples.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.001 | 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".