Detecting and Processing Anomalies in a Factory of the Future
- Presentation of new methods for detecting anomalies caused by malicious or accidental events for distributed systems in dynamic factory environments.
- Presentation of new approaches to mitigate such anomalies.
- Integration of the presented methods to develop a holistic anomaly handling approach to increase the resilience of the FoF.
2. Background and Related Work
2.1. Attack Vectors
2.2. Threat Actors
- Mass fraud and automated hacking: Use of automated tools (with as little human effort as possible) to monetize large-scale fraud.
- Providers of criminal infrastructures: These actors try to infect as many systems as possible in order to exploit them in a criminal infrastructure (e.g., botnets). They may also sell/rent the exploitation of this infrastructure to third parties.
- Skilled professionals: Expend significant effort to attack individual high-value targets. This type of attack may use specially designed malware with a significant effort, or the attacks are carried out across supply chain partners. High-value targets in an organization are also targeted by email and phone scams, using social engineering skills to extend the attack .
2.3. Anomaly Detection and Mitigation
3. Use-Case Introduction
4. Anomalies and Sources
4.1. Unrecognized AGV Failure
4.2. AGV under Cyber-Attack
4.3. Adversarial Attack
5. Methodology for Anomaly Detection and Mitigation
5.1. Anomaly Detection
5.1.1. Unrecognized Failure in AGV
Finalized Tasks per Hour
Driven Distances of Each AGV
Length of Local Queue of Each AGV
5.1.2. AGV under Cyber-Attack
5.1.3. Anomaly Information Exchange
5.2.1. Reconfiguration of AGV Services
- Laser scanner running locally on AGV1,
- A cooperative communication-service that sends and receives all necessary messages,
- An AMCL-service provided by AGV2 and
- Local-cost map running on AGV1.
5.2.2. Global Reconfiguration
5.2.3. AGV under Cyber-Attack
5.2.4. Mitigation against Adversarial Attack
Remove Adversarial Noise
6.1. Integration of the Methods
6.2. Benefits, Limitations and Future Work
Institutional Review Board Statement
Informed Consent Statement
Conflicts of Interest
|AMCL||adaptive Monte Carlo landmark|
|AGV||Automated Guided Vehilce|
|CSCA||Cyber Supply Chain Attacks|
|FoF||Factory of the Future|
|KPI||key performance indicator|
|MES||manufacturing execution system|
|MOOP||multi-objective optimization problem|
|MRTA||Multi Robot Task Assignment|
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|Anomaly Indicator||Process indicator||No transport jobs from broken machine||High number of transport jobs from suddenly high active machine||Malicious or dangerous manipulated process data|
|Component indicator||Lower driven distance of AGV with broken self-localization module||Length of local queue of each robot longer than usual due to unexpected transport request peak||Deviation of AGV-internal software list|
|Communication indicator||No messages received from AGV with broken WIFI module||High number of messages from MES received||Wrong agent (e.g., AGV) publishes transport job|
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Feeken, L.; Kern, E.; Szanto, A.; Winnicki, A.; Kao, C.-Y.; Wudka, B.; Glawe, M.; Mirzaei, E.; Borchers, P.; Burghardt, C. Detecting and Processing Anomalies in a Factory of the Future. Appl. Sci. 2022, 12, 8181. https://doi.org/10.3390/app12168181
Feeken L, Kern E, Szanto A, Winnicki A, Kao C-Y, Wudka B, Glawe M, Mirzaei E, Borchers P, Burghardt C. Detecting and Processing Anomalies in a Factory of the Future. Applied Sciences. 2022; 12(16):8181. https://doi.org/10.3390/app12168181Chicago/Turabian Style
Feeken, Linda, Esther Kern, Alexander Szanto, Alexander Winnicki, Ching-Yu Kao, Björn Wudka, Matthias Glawe, Elham Mirzaei, Philipp Borchers, and Christian Burghardt. 2022. "Detecting and Processing Anomalies in a Factory of the Future" Applied Sciences 12, no. 16: 8181. https://doi.org/10.3390/app12168181