ConAML: Constrained Adversarial Machine Learning for Cyber-Physical Systems

by   Jiangnan Li, et al.

Recent research demonstrated that the superficially well-trained machine learning (ML) models are highly vulnerable to adversarial examples. As ML techniques are rapidly employed in cyber-physical systems (CPSs), the security of these applications is of concern. However, current studies on adversarial machine learning (AML) mainly focus on computer vision and related fields. The risks the adversarial examples can bring to the CPS applications have not been well investigated. In particular, due to the distributed property of data sources and the inherent physical constraints imposed by CPSs, the widely-used threat models in previous research and the state-of-the-art AML algorithms are no longer practical when applied to CPS applications. We study the vulnerabilities of ML applied in CPSs by proposing Constrained Adversarial Machine Learning (ConAML), which generates adversarial examples used as ML model input that meet the intrinsic constraints of the physical systems. We first summarize the difference between AML in CPSs and AML in existing cyber systems and propose a general threat model for ConAML. We then design a best-effort search algorithm to iteratively generate adversarial examples with linear physical constraints. As proofs of concept, we evaluate the vulnerabilities of ML models used in the electric power grid and water treatment systems. The results show that our ConAML algorithms can effectively generate adversarial examples which significantly decrease the performance of the ML models even under practical physical constraints.


page 3

page 11

page 12


Wild Networks: Exposure of 5G Network Infrastructures to Adversarial Examples

Fifth Generation (5G) networks must support billions of heterogeneous de...

Semantic Adversarial Deep Learning

Fueled by massive amounts of data, models produced by machine-learning (...

Learning Physical Concepts in Cyber-Physical Systems: A Case Study

Machine Learning (ML) has achieved great successes in recent decades, bo...

A White-Box Adversarial Attack Against a Digital Twin

Recent research has shown that Machine Learning/Deep Learning (ML/DL) mo...

Design Methodology for Deep Out-of-Distribution Detectors in Real-Time Cyber-Physical Systems

When machine learning (ML) models are supplied with data outside their t...

Statistical Detection of Adversarial examples in Blockchain-based Federated Forest In-vehicle Network Intrusion Detection Systems

The internet-of-Vehicle (IoV) can facilitate seamless connectivity betwe...

Please sign up or login with your details

Forgot password? Click here to reset