Autonomous Cyber Defense for Resilient Cyber-Physical Systems

In this dissertation research, we design and analyze resilient cyber-physical systems (CPSs) under high network dynamics, adversarial attacks, and various uncertainties. We focus on three key system attributes to build resilient CPSs by developing a suite of the autonomous cyber defense mechanisms. First, we consider network adaptability to achieve the resilience of a CPS. Network adaptability represents the network ability to maintain its security and connectivity level when faced with incoming attacks. We address this by network topology adaptation. Network topology adaptation can contribute to quickly identifying and updating the network topology to confuse attacks by changing attack paths. We leverage deep reinforcement learning (DRL) to develop CPSs using network topology adaptation. Second, we consider the fault-tolerance of a CPS as another attribute to ensure system resilience. We aim to build a resilient CPS under severe resource constraints, adversarial attacks, and various uncertainties. We chose a solar sensor-based smart farm as one example of the CPS applications and develop a resource-aware monitoring system for the smart farms. We leverage DRL and uncertainty quantification using a belief theory, called Subjective Logic, to optimize critical tradeoffs between system performance and security under the contested CPS environments. Lastly, we study system resilience in terms of system recoverability. The system recoverability refers to the system's ability to recover from performance degradation or failure. In this task, we mainly focus on developing an automated intrusion response system (IRS) for CPSs. We aim to design the IRS with effective and efficient responses by reducing a false alarm rate and defense cost, respectively. Specifically, We build a lightweight IRS for an in-vehicle controller area network (CAN) bus system operating with DRL-based autonomous driving.

Name thesis:degree_name

Doctor of Philosophy

Level thesis:degree_level

doctoral

Discipline thesis:degree_discipline

Computer Science & Applications

Department dc:contributor.department

Computer Science and Applications

Grantor dc:publisher

Virginia Tech

Year dc:date.issued

2024

Author dc:creator

• Zhang, Qisheng

Chair dc:contributor.committeechair

• Cho, Jin-Hee

Committee members dc:contributor.committeemember

• Lu, Chang Tien
• Chen, Feng
• Moore, Terrence J.
• Chen, Ing Ray

• Cyber-physical systems
• network resilience
• network security
• network adaptation
• software diversity
• deep reinforcement learning
• fault-tolerance
• recoverability
• intrusion prevention
• intrusion response

Statement dc:rights

• In Copyright

Licence dc:rights.uri

• http://rightsstatements.org/vocab/InC/1.0/

Language dc:language.iso

en

Dc Identifier Other

vt_gsexam:39354

OAI identifier oai:identifier

oai:vtechworks.lib.vt.edu:10919/117329