Triadic-OCD: Asynchronous Online Change Detection with Provable Robustness, Optimality, and Convergence
Authors: Yancheng Huang, Kai Yang, Zelin Zhu, Leian Chen
ICML 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments have been conducted to elucidate the effectiveness of the proposed method. |
| Researcher Affiliation | Academia | 1Tongji University 2Columbia University 3This work was done prior to Leian Chen joining Amazon. |
| Pseudocode | Yes | Algorithm 1 Triadic-OCD |
| Open Source Code | No | The paper does not contain any explicit statement about releasing source code for the described methodology, nor does it provide a link to a code repository. |
| Open Datasets | Yes | In our experiments, we utilized the measurement data generated from the IEEE-14 bus power system, which can be regarded as a benchmark dataset in current research studies. |
| Dataset Splits | No | The paper mentions using the IEEE-14 bus power system dataset but does not specify any training, validation, or test dataset splits (e.g., percentages or sample counts). |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., GPU/CPU models, memory) used to conduct the experiments. |
| Software Dependencies | No | The paper does not list specific software dependencies with their version numbers, which would be necessary for reproducibility. |
| Experiment Setup | Yes | In our experiment, we assume that the system matrix can be accurately determined in all areas except for the 4th area. Suppose attackers utilize a sequence of randomly generated fabricated vectors to compromise the meter reading y(t) from the time instant ta. The generation process of the injected attacks is as follows, a(t) = P Hu, ui U(0.1, 1), where P H I H HT H 1 HT . Given the fluctuations in admittance caused by factors like environmental disturbances, the detection algorithms employed in the experiment utilize imprecise estimates of H to identify anomaly vectors injected into smart grid systems. We provide the inaccurate estimates of H in Appendix A, as well as the corresponding detailed settings of various uncertainty sets. |