TY - GEN
T1 - Towards Adaptive Inspection for Fraud in I4.0 Supply Chains
AU - Welsh, Thomas
AU - Alrimawi, Faeq
AU - Farahani, Ali
AU - Hassett, Diane
AU - Zisman, Andrea
AU - Nuseibeh, Bashar
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - The effective functioning of society is increasingly reliant on supply chains which are susceptible to fraud, such as the distribution of adulterated products. Inspection is a key tool for mitigating fraud, however it has traditionally been constrained by physical characteristics of supply chains such as their size and geographical distribution. The increasingly cyber-physical nature of supply chains, their autonomy, and their data richness, extends their attack surfaces and thus increases opportunities for fraud. However, it also presents new opportunities for increased and dynamic inspection, which in turn requires more targeted and flexible inspection regimes. In this paper we explore opportunities to engineer adaptive inspection of cyber-physical supply chains to support efforts to reduce fraud. Through using structural representations of supply chains (topological models) we propose defining optimal inspection zones. Such zones circumscribe assets of interest to optimise observation while reducing the intrusiveness of inspection. Using a motivating example of adulterated pharmaceuticals and a proof-of-concept tool we illustrate adaptive inspection, and surface challenges to its realisation, such as value metrics, forensic readiness integration and managing contrasting local and global perspectives.
AB - The effective functioning of society is increasingly reliant on supply chains which are susceptible to fraud, such as the distribution of adulterated products. Inspection is a key tool for mitigating fraud, however it has traditionally been constrained by physical characteristics of supply chains such as their size and geographical distribution. The increasingly cyber-physical nature of supply chains, their autonomy, and their data richness, extends their attack surfaces and thus increases opportunities for fraud. However, it also presents new opportunities for increased and dynamic inspection, which in turn requires more targeted and flexible inspection regimes. In this paper we explore opportunities to engineer adaptive inspection of cyber-physical supply chains to support efforts to reduce fraud. Through using structural representations of supply chains (topological models) we propose defining optimal inspection zones. Such zones circumscribe assets of interest to optimise observation while reducing the intrusiveness of inspection. Using a motivating example of adulterated pharmaceuticals and a proof-of-concept tool we illustrate adaptive inspection, and surface challenges to its realisation, such as value metrics, forensic readiness integration and managing contrasting local and global perspectives.
KW - Adaptive
KW - Fraud
KW - I4.0
KW - Inspection
KW - Supply chains
UR - http://www.scopus.com/inward/record.url?scp=85122934593&partnerID=8YFLogxK
U2 - 10.1109/ETFA45728.2021.9613693
DO - 10.1109/ETFA45728.2021.9613693
M3 - Conference contribution
AN - SCOPUS:85122934593
T3 - IEEE International Conference on Emerging Technologies and Factory Automation, ETFA
BT - Proceedings - 2021 26th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 26th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2021
Y2 - 7 September 2021 through 10 September 2021
ER -