Additive manufacturing is finding increased application in industry. Safety-critical products, such as medical prostheses and parts for aerospace and automotive industries are being printed by additive manufacturing methods, but there currently are no standard methods for verifying the integrity of the parts that are produced. Trustworthy operation of industrial additive manufacturing depends on secure embedded controllers that monitor and control the underlying physical manufacturing processes. This research will investigate a perfectly air-gapped intrusion detection solution for cyber-physical industrial additive manufacturing infrastructures in which some of the controllers may be infected by malicious code. The research will provide guidelines to: i) tie together resilience solutions in software security, control system design, and signal processing, and ii) incorporate reliable and practical cyber-physical attack detection into real-world manufacturing. Educational and technology transfer activities will address the need to improve the applicability of training methods to ensuring the safety and cyber security of physical control systems. Activities will involve The Society of Women Engineers and a large population of underrepresented and low-income minorities with diverse cultural backgrounds and improve the security of existing, real-world, additive manufacturing systems in industry.
Next generation cyber-physical additive manufacturing enables advanced product designs and capabilities, but increasingly relies on highly networked industrial control systems that present opportunities for cyber attacks. The predominant approach to defending against these threats relies on host-based intrusion detectors that sit within the same target controllers, and hence are often the first target of the controller attacks. This project will research contact-less and perfectly air-gapped intrusion detection by analyzing physical side-channels to protect against cyber-physical attacks. This solution requires no runtime overhead on real-time controllers, requires minimal change to legacy systems, and reliably identifies intrusions even if the target system is completely compromised. The work will address solutions for: i) air-gapped intrusion detection on cyber-physical systems while maintaining a perfect air gap, ii) a comprehensive understanding of the types of side-channels available for analysis in different industrial systems, and iii) empirical validation of the various perfectly air-gapped intrusion detection tools, both independently and working in tandem.
|Effective start/end date
|8/1/17 → 7/31/22
- National Science Foundation: $624,473.00