Covert Attacks in Cyber-Physical Control Systems
September 29, 2016 Β· Declared Dead Β· π IEEE Transactions on Industrial Informatics
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
A. O. Sa, L. F. R. C. Carmo, R. C. S. Machado
arXiv ID
1609.09537
Category
cs.CR: Cryptography & Security
Cross-listed
eess.SY
Citations
118
Venue
IEEE Transactions on Industrial Informatics
Last Checked
4 months ago
Abstract
The advantages of using communication networks to interconnect controllers and physical plants motivate the increasing number of Networked Control Systems, in industrial and critical infrastructure facilities. However, this integration also exposes such control systems to new threats, typical of the cyber domain. In this context, studies have been conduced, aiming to explore vulnerabilities and propose security solutions for cyber-physical systems. In this paper, it is proposed a covert attack for service degradation, which is planned based on the intelligence gathered by another attack, herein proposed, referred as System Identification attack. The simulation results demonstrate that the joint operation of the two attacks is capable to affect, in a covert and accurate way, the physical behavior of a system.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Cryptography & Security
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
The Limitations of Deep Learning in Adversarial Settings
R.I.P.
π»
Ghosted
Distillation as a Defense to Adversarial Perturbations against Deep Neural Networks
R.I.P.
π»
Ghosted
Spectre Attacks: Exploiting Speculative Execution
R.I.P.
π»
Ghosted
How To Backdoor Federated Learning
R.I.P.
π»
Ghosted
Evasion Attacks against Machine Learning at Test Time
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted