Resilient Machine Learning for Networked Cyber Physical Systems: A Survey for Machine Learning Security to Securing Machine Learning for CPS

02/14/2021
by   Felix Olowononi, et al.
0

Cyber Physical Systems (CPS) are characterized by their ability to integrate the physical and information or cyber worlds. Their deployment in critical infrastructure have demonstrated a potential to transform the world. However, harnessing this potential is limited by their critical nature and the far reaching effects of cyber attacks on human, infrastructure and the environment. An attraction for cyber concerns in CPS rises from the process of sending information from sensors to actuators over the wireless communication medium, thereby widening the attack surface. Traditionally, CPS security has been investigated from the perspective of preventing intruders from gaining access to the system using cryptography and other access control techniques. Most research work have therefore focused on the detection of attacks in CPS. However, in a world of increasing adversaries, it is becoming more difficult to totally prevent CPS from adversarial attacks, hence the need to focus on making CPS resilient. Resilient CPS are designed to withstand disruptions and remain functional despite the operation of adversaries. One of the dominant methodologies explored for building resilient CPS is dependent on machine learning (ML) algorithms. However, rising from recent research in adversarial ML, we posit that ML algorithms for securing CPS must themselves be resilient. This paper is therefore aimed at comprehensively surveying the interactions between resilient CPS using ML and resilient ML when applied in CPS. The paper concludes with a number of research trends and promising future research directions. Furthermore, with this paper, readers can have a thorough understanding of recent advances on ML-based security and securing ML for CPS and countermeasures, as well as research trends in this active research area.

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 1

page 6

page 10

03/12/2020

ConAML: Constrained Adversarial Machine Learning for Cyber-Physical Systems

Recent research demonstrated that the superficially well-trained machine...
02/16/2021

Machine Learning Based Cyber Attacks Targeting on Controlled Information: A Survey

Stealing attack against controlled information, along with the increasin...
02/21/2022

Poisoning Attacks and Defenses on Artificial Intelligence: A Survey

Machine learning models have been widely adopted in several fields. Howe...
07/14/2020

Robustifying Reinforcement Learning Agents via Action Space Adversarial Training

Adoption of machine learning (ML)-enabled cyber-physical systems (CPS) a...
11/28/2021

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

Machine Learning (ML) has achieved great successes in recent decades, bo...
12/06/2018

On Critical Infrastructures, Their Security and Resilience - Trends and Vision

This short paper is presented in observance and promotion of November, t...
01/24/2020

When Wireless Security Meets Machine Learning: Motivation, Challenges, and Research Directions

Wireless systems are vulnerable to various attacks such as jamming and e...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.