Agile services and analysis framework for autonomous and autonomic critical infrastructure

• Published in: Innovations in systems and software engineering Vol. 19; no. 2; pp. 145 - 156
• Main Authors: Maurio, Joe, Wood, Paul, Zanlongo, Sebastian, Silbermann, Josh, Sookoor, Tamim, Lorenzo, Alberto, Sleight, Randy, Rogers, James, Muller, Dan, Armiger, Noah, Rouff, Christopher, Watkins, Lanier
• Format: Journal Article
• Language: English
• Published: London Springer London 01.06.2023; Springer Nature B.V
• Subjects: Algorithms; Artificial Intelligence; Computer Applications; Computer Science; Control systems; Critical infrastructure; Cyber-physical systems; Cybersecurity; Failure; Industrial electronics; Intelligent agents; Original Article; Resilience; Retrofitting; Software Engineering; Traffic control; Unmanned aerial vehicles; Autonomy; Resiliency; Cyber physical systems; Autonomic
• ISSN: 1614-5046; 1614-5054
• DOI: 10.1007/s11334-021-00411-9

Many cyber physical systems have little or no cybersecurity mechanisms due to their limited computing capabilities or their history of running on isolated networks. As these systems have become interconnected and connected to corporate networks, they have become more vulnerable to cyberattacks. Providing cyber physical systems with autonomic properties will allow them to become more self-aware and react in near real time to attacks and failures. Testing these systems for their susceptibility to intelligent attacks is also needed to provide assurance of their resilience. This paper describes two approaches to providing assurances to cyber physical systems. The first approach retrofits industrial control systems with autonomic properties that will allow them to automatically detect and recover from cyberattacks and other failures through the use of microservices that reconfigure the systems dynamically during attacks or failures. The second approach uses intelligent agents in a modeling and simulation framework to test the resiliency of autonomous unmanned aerial systems. Agents are orchestrated using a range of algorithms and subjected to stressful environments to measure the efficiency and safety of their operations in a simulate multi-UAS air-traffic control problem.