Unmanned aerial vehicles (UAVs) are becoming increasingly pervasive in everyday life, supporting diverse use cases such as aerial photography, delivery of goods, or disaster reconnaissance and management. UAVs are cyber-physical systems (CPS): they integrate computation (embedded software and control systems) with physical components (the UAVs flying in the physical world). UAVs in particular and CPS in general require monitoring capabilities to detect and possibly mitigate erroneous and safety-critical behavior at runtime. Existing monitoring approaches mostly do not adequately address UAV CPS characteristics such as the high number of dynamically instantiated components, the tight integration of elements, and the massive amounts of data that need to be processed. In this paper we report results of a case study on monitoring in UAVs. We discuss CPS-specific monitoring challenges and present a prototype we implemented by extending REMINDS, a framework for software monitoring so far mainly used in the domain of metallurgical plants. Additionally, we demonstrate the applicability and scalability of our approach by monitoring a real control and management system for UAVs in simulations with up to 30 drones flying in an urban area.
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Harm-DoS: Hash Algorithm Replacement for Mitigating Denial-of-Service Vulnerabilities in Binary Executables
Binary analysis of closed-source, low-level, and
embedded systems software has emerged at the heart of cyberphysical vulnerability assessment of third-party or legacy devices
in safety-critical systems. In particular, recovering the semantics
of the source algorithmic implementations enables analysts to
understand the context of a particular binary program snippet.
However, experimentation and evaluation of binary analysis
techniques on real-world embedded cyber-physical systems are
limited to domain-specific testbeds with a low number of use
cases–insufficient to support emerging data-driven techniques.
Moreover, the use cases rarely have the source mathematical
expressions, algorithms, and compiled binaries. In this paper, we
present AUTOCPS, a framework for generating a large corpus
of control systems binaries along with their source algorithmic
expressions and source code. AUTOCPS enables researchers to
tune the control system binary data generation by varying different permutations of cyber-physical modules, e.g., the underlying
control algorithm, while ensuring a semantically valid binary. We
initially constrain AUTOCPS to the flight software domain and
generate over 4000 semantically different control systems source
representations, which are then used to generate hundreds of
thousands of binaries. We describe current and future use cases
of AUTOCPS towards cyber-physical vulnerability assessment of
safety-critical systems.
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- Award ID(s):
- 2051101
- NSF-PAR ID:
- 10396202
- Date Published:
- Journal Name:
- 25th International Symposium on Research in Attacks, Intrusions and Defenses (RAID 2022)
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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