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 bemore »
This content will become publicly available on December 27, 2023
Bioinspired and Low-Power 2D Machine Vision with Adaptive Machine Learning and Forgetting
- Award ID(s):
- 2042154
- Publication Date:
- NSF-PAR ID:
- 10388508
- Journal Name:
- ACS Nano
- Volume:
- 16
- Issue:
- 12
- Page Range or eLocation-ID:
- 20010 to 20020
- ISSN:
- 1936-0851
- Sponsoring Org:
- National Science Foundation
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