Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
Adversarial Regression for Detecting Attacks in Cyber-Physical Systems
Authors: Amin Ghafouri, Yevgeniy Vorobeychik, Xenofon Koutsoukos
IJCAI 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments show that (a) the stealthy attacks we develop are extremely effective, and (b) our resilient detector significantly reduces the impact of a stealthy attack without appreciably increasing the false alarm rate. |
| Researcher Affiliation | Collaboration | Amin Ghafouri1, Yevgeniy Vorobeychik2, and Xenofon Koutsoukos2 1Cruise Automation, San Francisco, CA 2Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN EMAIL,EMAIL |
| Pseudocode | Yes | Algorithm 1 Adversarial Regression for Neural Network and Algorithm 2 Resilient Detector |
| Open Source Code | No | The paper does not contain any explicit statement about releasing source code for the described methodology, nor does it provide any links to a code repository. |
| Open Datasets | Yes | We evaluate our contributions using a case study of the wellknown Tennessee-Eastman process control system (TE-PCS) and We use the revised Simulink model of TE-PCS [Bathelt et al., 2015]. |
| Dataset Splits | No | Note that since the data is sequential, the train and test data cannot be randomly sampled and instead, we divide the data in two blocks. (This mentions train and test, but not a separate validation set nor specific split percentages/counts.) |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., CPU, GPU models, memory) used to run the experiments. |
| Software Dependencies | No | We trained the networks in Tensorflow for 5000 epochs using Adam optimizer with β1 = 0.9, β2 = 0.999, and ϵ = 10 8, and a learning rate of 0.01. (Mentions Tensorflow but no version number or other software dependencies with versions.) |
| Experiment Setup | Yes | We considered neural networks with 2 to 4 hidden layers and 10 to 20 neurons in each layer. All the neurons in the hidden layers use tanh activation functions. We also experimented with Re LU activation functions but tanh performs better. We trained the networks in Tensorflow for 5000 epochs using Adam optimizer with β1 = 0.9, β2 = 0.999, and ϵ = 10 8, and a learning rate of 0.01. |