A Roadmap Toward the Resilient Internet of Things for Cyber-Physical Systems

Ratasich, Denise; Khalid, Faiq; Geißler, Florian; Grosu, Radu; Shafique, Muhammad; Bartocci, Ezio

doi:10.1109/access.2019.2891969

Cited by 108 publications

(117 citation statements)

References 176 publications

(209 reference statements)

Supporting

Mentioning

111

Contrasting

Unclassified

Order By: Relevance

“…First, (re)configuration -known also as self-healing or resilience -is an important topic widely discussed in the domain of cyber-physical systems (CPS), see e.g. (Hehenberger et al 2016;Ratasich et al 2019). Second, both problems can easily be encoded in plain LARS but, at the moment, they can only be solved using methods like RESTART, which translate LARS encodings into ASP.…”

Section: Discussionmentioning

confidence: 99%

Managing caching strategies for stream reasoning with reinforcement learning

Dodaro¹,

Eiter²,

Ogris³

et al. 2020

Theory and Practice of Logic Programming

View full text Add to dashboard Cite

Efficient decision-making over continuously changing data is essential for many application domains such as cyber-physical systems, industry digitalization, etc. Modern stream reasoning frameworks allow one to model and solve various real-world problems using incremental and continuous evaluation of programs as new data arrives in the stream. Applied techniques use, e.g., Datalog-like materialization or truth maintenance algorithms to avoid costly re-computations, thus ensuring low latency and high throughput of a stream reasoner. However, the expressiveness of existing approaches is quite limited and, e.g., they cannot be used to encode problems with constraints, which often appear in practice. In this paper, we suggest a novel approach that uses the Conflict-Driven Constraint Learning (CDCL) to efficiently update legacy solutions by using intelligent management of learned constraints. In particular, we study the applicability of reinforcement learning to continuously assess the utility of learned constraints computed in previous invocations of the solving algorithm for the current one. Evaluations conducted on real-world reconfiguration problems show that providing a CDCL algorithm with relevant learned constraints from previous iterations results in significant performance improvements of the algorithm in stream reasoning scenarios.

show abstract

Section: Discussionmentioning

confidence: 99%

Managing caching strategies for stream reasoning with reinforcement learning

Dodaro¹,

Eiter²,

Ogris³

et al. 2020

Theory and Practice of Logic Programming

View full text Add to dashboard Cite

show abstract

“…-Runtime Verification [13], [20]. A monitor observes the system at runtime to verify the correctness of the execution.…”

Section: A Detectionmentioning

confidence: 99%

“…-Falsification-based Analysis [13]. It extends STL by including a quantitative semantics allowing the return of real values rather than Boolean values.…”

Section: A Detectionmentioning

confidence: 99%

See 1 more Smart Citation

REMIND: A Framework for the Resilient Design of Automotive Systems

Rosenstatter

Strandberg

Jolak

et al. 2020

2020 IEEE Secure Development (SecDev)

View full text Add to dashboard Cite

In the past years, great effort has been spent on enhancing the security and safety of vehicular systems. Current advances in information and communication technology have increased the complexity of these systems and lead to extended functionalities towards self-driving and more connectivity. Unfortunately, these advances open the door for diverse and newly emerging attacks that hamper the security and, thus, the safety of vehicular systems. In this paper, we contribute to supporting the design of resilient automotive systems. We review and analyze scientific literature on resilience techniques, fault tolerance, and dependability. As a result, we present the REMIND resilience framework providing techniques for attack detection, mitigation, recovery, and resilience endurance. Moreover, we provide guidelines on how the REMIND framework can be used against common security threats and attacks and further discuss the trade-offs when applying these guidelines.

show abstract

“…integrated with limited computation capabilities, small memory and low bandwidth. CPS devices are being used in various industrial domains, e.g., manufacturing, healthcare monitoring using sensors, medical applications, IoT assisted living, power generation and distribution, smart aircraft, water management systems, asset management, and so on [3]. In distributed applications (as shown in Fig.…”

Section: Introductionmentioning

confidence: 99%

Cyber-Physical Trust Systems Driven by Blockchain

2020

View full text Add to dashboard Cite

Cyber Physical Trust Systems (CPTS) are Cyber Physical Systems and Internet of Things enriched with trust as an explicit, measurable, testable and verifiable system component. In this paper, we propose to use blockchain, a distributed ledger technology, as the trust enabling system component for CPTS. We propose two schemes for CPTSs driven by blockchain in relation to two typical network model cases. We show that our proposed approach achieves the security properties, such as device identification, authentication, integrity, and non-repudiation, and provides protection against popular attacks, such as replay and spoofing. We provide formal proofs of those properties using the Tamarin Prover tool. We describe results of a proof-of-concept which implements a CPTS driven by blockchain for physical asset management and present a performance analysis of our implementation. We identify use cases in which CPTSs driven by blockchain find applications. INDEX TERMS Cyber physical systems, Internet of Things, distributed ledger technology, blockchain, asset management, supply chain, Industry 4.0, deep leasing.

show abstract

A Roadmap Toward the Resilient Internet of Things for Cyber-Physical Systems

Cited by 108 publications

References 176 publications

Managing caching strategies for stream reasoning with reinforcement learning

Managing caching strategies for stream reasoning with reinforcement learning

REMIND: A Framework for the Resilient Design of Automotive Systems

Cyber-Physical Trust Systems Driven by Blockchain

Contact Info

Product

Resources

About