On the Elements of Datasets for Cyber Physical Systems Security
- URL: http://arxiv.org/abs/2208.08255v1
- Date: Wed, 17 Aug 2022 12:20:57 GMT
- Title: On the Elements of Datasets for Cyber Physical Systems Security
- Authors: Ashraf Tantawy
- Abstract summary: We propose a dataset architecture that has the potential to enhance the performance of AI algorithms in securing cyber physical systems.
We compare existing datasets to the proposed architecture to identify the current limitations and discuss the future of CPS dataset generation using testbeds.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Datasets are essential to apply AI algorithms to Cyber Physical System (CPS)
Security. Due to scarcity of real CPS datasets, researchers elected to generate
their own datasets using either real or virtualized testbeds. However, unlike
other AI domains, a CPS is a complex system with many interfaces that determine
its behavior. A dataset that comprises merely a collection of sensor
measurements and network traffic may not be sufficient to develop resilient AI
defensive or offensive agents. In this paper, we study the \emph{elements} of
CPS security datasets required to capture the system behavior and interactions,
and propose a dataset architecture that has the potential to enhance the
performance of AI algorithms in securing cyber physical systems. The framework
includes dataset elements, attack representation, and required dataset
features. We compare existing datasets to the proposed architecture to identify
the current limitations and discuss the future of CPS dataset generation using
testbeds.
Related papers
- Building Hybrid B-Spline And Neural Network Operators [0.0]
Control systems are indispensable for ensuring the safety of cyber-physical systems (CPS)
We propose a novel strategy that combines the inductive bias of B-splines with data-driven neural networks to facilitate real-time predictions of CPS behavior.
arXiv Detail & Related papers (2024-06-06T21:54:59Z) - Generative AI for Secure and Privacy-Preserving Mobile Crowdsensing [74.58071278710896]
generative AI has attracted much attention from both academic and industrial fields.
Secure and privacy-preserving mobile crowdsensing (SPPMCS) has been widely applied in data collection/ acquirement.
arXiv Detail & Related papers (2024-05-17T04:00:58Z) - On Responsible Machine Learning Datasets with Fairness, Privacy, and Regulatory Norms [56.119374302685934]
There have been severe concerns over the trustworthiness of AI technologies.
Machine and deep learning algorithms depend heavily on the data used during their development.
We propose a framework to evaluate the datasets through a responsible rubric.
arXiv Detail & Related papers (2023-10-24T14:01:53Z) - A Synthetic Dataset for 5G UAV Attacks Based on Observable Network
Parameters [3.468596481227013]
This paper presents the first synthetic dataset for Unmanned Aerial Vehicle (UAV) attacks in 5G and beyond networks.
The main objective of this data is to enable deep network development for UAV communication security.
The proposed dataset provides insights into network functionality when static or moving UAV attackers target authenticated UAVs in an urban environment.
arXiv Detail & Related papers (2022-11-05T15:12:51Z) - TRoVE: Transforming Road Scene Datasets into Photorealistic Virtual
Environments [84.6017003787244]
This work proposes a synthetic data generation pipeline to address the difficulties and domain-gaps present in simulated datasets.
We show that using annotations and visual cues from existing datasets, we can facilitate automated multi-modal data generation.
arXiv Detail & Related papers (2022-08-16T20:46:08Z) - CARLA-GeAR: a Dataset Generator for a Systematic Evaluation of
Adversarial Robustness of Vision Models [61.68061613161187]
This paper presents CARLA-GeAR, a tool for the automatic generation of synthetic datasets for evaluating the robustness of neural models against physical adversarial patches.
The tool is built on the CARLA simulator, using its Python API, and allows the generation of datasets for several vision tasks in the context of autonomous driving.
The paper presents an experimental study to evaluate the performance of some defense methods against such attacks, showing how the datasets generated with CARLA-GeAR might be used in future work as a benchmark for adversarial defense in the real world.
arXiv Detail & Related papers (2022-06-09T09:17:38Z) - Multi-Source Data Fusion for Cyberattack Detection in Power Systems [1.8914160585516038]
We show that fusing information from multiple data sources can help identify cyber-induced incidents and reduce false positives.
We perform multi-source data fusion for training IDS in a cyber-physical power system testbed.
Results are presented using the proposed data fusion application to infer False Data and Command injection-based Man-in- The-Middle attacks.
arXiv Detail & Related papers (2021-01-18T06:34:45Z) - Deep Learning based Covert Attack Identification for Industrial Control
Systems [5.299113288020827]
We develop a data-driven framework that can be used to detect, diagnose, and localize a type of cyberattack called covert attacks on smart grids.
The framework has a hybrid design that combines an autoencoder, a recurrent neural network (RNN) with a Long-Short-Term-Memory layer, and a Deep Neural Network (DNN)
arXiv Detail & Related papers (2020-09-25T17:48:43Z) - PicoDomain: A Compact High-Fidelity Cybersecurity Dataset [0.9281671380673305]
Current cybersecurity datasets either offer no ground truth or do so with anonymized data.
Most existing datasets are large enough to make them unwieldy during prototype development.
In this paper we have developed the PicoDomain dataset, a compact high-fidelity collection of Zeek logs from a realistic intrusion.
arXiv Detail & Related papers (2020-08-20T20:18:04Z) - Risk-Averse MPC via Visual-Inertial Input and Recurrent Networks for
Online Collision Avoidance [95.86944752753564]
We propose an online path planning architecture that extends the model predictive control (MPC) formulation to consider future location uncertainties.
Our algorithm combines an object detection pipeline with a recurrent neural network (RNN) which infers the covariance of state estimates.
The robustness of our methods is validated on complex quadruped robot dynamics and can be generally applied to most robotic platforms.
arXiv Detail & Related papers (2020-07-28T07:34:30Z) - A Privacy-Preserving Distributed Architecture for
Deep-Learning-as-a-Service [68.84245063902908]
This paper introduces a novel distributed architecture for deep-learning-as-a-service.
It is able to preserve the user sensitive data while providing Cloud-based machine and deep learning services.
arXiv Detail & Related papers (2020-03-30T15:12:03Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.