TY - GEN
T1 - ARCS-R
T2 - 70th Annual Reliability and Maintainability Symposium, RAMS 2024
AU - Van Bossuyt, Douglas L.
AU - Papakonstantinou, Nikolaos
AU - Hale, Britta
AU - Arlitt, Ryan
AU - Palatheerdham, Srinivasa Rao
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - This paper explores how reliability analysis and cyber-security analysis can be combined using Artificial Intelligence and Machine Learning (AI/ML), and Large Language Models (LLM) to produce a continuously updated resilience analysis. This is achieved by modeling both the hardware and software of the system, and employing LLMs and AI/ML to continuously search for new software vulnerabilities and feed that information into continuously updating resilience models. A case study of a drone is presented that demonstrates the promise of the proposed method. It is expected that using the proposed method, named Assessment for Risk in Cybersecurity and Safety - Resilience (ARCS-R), will reduce failure rate of mission-critical cyber-physical systems by reducing the likelihood of a potential initiating event causing a prolonged degradation in system performance that impacts system resilience.
AB - This paper explores how reliability analysis and cyber-security analysis can be combined using Artificial Intelligence and Machine Learning (AI/ML), and Large Language Models (LLM) to produce a continuously updated resilience analysis. This is achieved by modeling both the hardware and software of the system, and employing LLMs and AI/ML to continuously search for new software vulnerabilities and feed that information into continuously updating resilience models. A case study of a drone is presented that demonstrates the promise of the proposed method. It is expected that using the proposed method, named Assessment for Risk in Cybersecurity and Safety - Resilience (ARCS-R), will reduce failure rate of mission-critical cyber-physical systems by reducing the likelihood of a potential initiating event causing a prolonged degradation in system performance that impacts system resilience.
KW - AI
KW - Artificial Intelligence
KW - Cyber-Physical System
KW - Failure Rate
KW - Large Language Model
KW - LLM
KW - Machine Learning
KW - ML
KW - Resilience
UR - http://www.scopus.com/inward/record.url?scp=85189340563&partnerID=8YFLogxK
U2 - 10.1109/RAMS51492.2024.10457626
DO - 10.1109/RAMS51492.2024.10457626
M3 - Conference article in proceedings
AN - SCOPUS:85189340563
T3 - Proceedings - Annual Reliability and Maintainability Symposium
BT - RAMS 2024 - Annual Reliability and Maintainability Symposium
PB - IEEE Institute of Electrical and Electronic Engineers
Y2 - 22 January 2024 through 25 January 2024
ER -