Separated Linux Process Logging Mechanism for Embedded Systems

Amontamavut, Praween; Nakagawa, Yuki; Hayakawa, Eiichi

doi:10.1109/rtcsa.2012.64

Cited by 5 publications

(3 citation statements)

References 3 publications

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“…In this work, the Linux kernel is the unmodified Linux kernel, and predictability and reliability are achieved by a nonpatched real-time scheduler module. Praween Amontamavut et al proposed a separate Linux process logging model for embedded systems [29] that can be used to log the behavior of Linux processes in Android. To capture the traffic from a large number of CAN buses in industrial automation systems, Michal Sojka et al studied in depth the performance evaluation of Linux CAN-related system calls [30].…”

Section: Embedded Systems In Smart Applicationsmentioning

confidence: 99%

Sustainable Secure Management Against APT Attacks for Intelligent Embedded-Enabled Smart Manufacturing

Dong

Ota

et al. 2020

IEEE Trans. Sustain. Comput.

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Intelligent embedded-enable smart manufacturing is an important infrastructure for future industries. Increasing security threats are disturbing the normal operations of smart manufacturing. As a novel type of threat, an advanced persistent threat (APT) has the novel features of strong concealment, latency and long-term entanglement, which can penetrate the core systems of smart manufacturing, especially for intelligent embedded systems, and cause great destruction from the cyber side to physical side. However, the existing security schemes cannot provide sustainable resource management, which causes the core system in smart manufacturing not to perform sustainable secure detection and defense against APTs. To address this challenge, this paper proposes a sustainable secure management mechanism for smart manufacturing against APTs. The proposed mechanism includes two parts: sustainable threat intelligence analysis and sustainable secure resource management. Sustainable threat intelligence analysis provides sustainable discovery of the indications of potential APTs , which has features of a weak signal, low correlation and slow time variation. The sustainable secure resource management provides deep and continuous protection for intelligent embedded systems in smart manufacturing. The evaluations show the defense capabilities and the feasibility of the proposed mechanism.

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Section: Embedded Systems In Smart Applicationsmentioning

confidence: 99%

Sustainable Secure Management Against APT Attacks for Intelligent Embedded-Enabled Smart Manufacturing

Dong

Ota

et al. 2020

IEEE Trans. Sustain. Comput.

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“…Anomaly detection using limited hardware has also been explored, using remote processing [21], look-up tables [22] and model-based comparison [23]. These solutions, however, do not readily account for systems dynamics and hence are unsuitable for gas turbine system anomaly detection.…”

Section: Introductionmentioning

confidence: 99%

Distributed digital twins for health monitoring: resource constrained aero-engine fleet management

Hartwell,

Montana,

Jacobs

et al. 2024

Aeronaut. j.

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Sensed data from high-value engineering systems is being increasingly exploited to optimise their operation and maintenance. In aerospace, returning all measured data to a central repository is prohibitively expensive, often causing useful, high-value data to be discarded. The ability to detect, prioritise and return useful data on asset and in real-time is vital to move toward more sustainable maintenance activities. We present a data-driven solution for on-line detection and prioritisation of anomalous data that is centrally processed and used to update individualised digital twins (DT) distributed onto remote machines. The DT is embodied as a convolutional neural network (CNN) optimised for real-time execution on a resource constrained gas turbine monitoring computer. The CNN generates a state prediction with uncertainty, which is used as a metric to select informative data for transfer to a remote fleet monitoring system. The received data is screened for faults before updating the weights on the CNN, which are synchronised between real and virtual asset. Results show the successful detection of a known in-flight engine fault and the collection of data related to high novelty pre-cursor events that were previously unrecognised. We demonstrate that data related to novel operation are also identified for transfer to the fleet monitoring system, allowing model improvement by retraining. In addition to these industrial dataset results, reproducible examples are provided for a public domain NASA dataset. The data prioritisation solution is capable of running in real-time on production-standard low-power embedded hardware and is deployed on the Rolls-Royce Pearl 15 engines.

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“…Anomaly detection using limited hardware has also been explored, using remote processing [22], look-up tables [23], model-based comparison [24], and in other heavily band-limited system [25]. These solutions, however, are not suited to gas turbine anomaly detection due to the complexity of modelling the highly non-linear asset behaviours.…”

Section: Introductionmentioning

confidence: 99%

In-flight Novelty Detection with Convolutional Neural Networks

Hartwell¹,

Montana²,

Jacobs³

et al. 2021

Preprint

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Gas turbine engines are complex machines that typically generate a vast amount of data, and require careful monitoring to allow for cost-effective preventative maintenance. In aerospace applications, returning all measured data to ground is prohibitively expensive, often causing useful, high value, data to be discarded. The ability to detect, prioritise, and return useful data in real-time is therefore vital. This paper proposes that system output measurements, described by a convolutional neural network model of normality, are prioritised in real-time for the attention of preventative maintenance decision makers.Due to the complexity of gas turbine engine timevarying behaviours, deriving accurate physical models is difficult, and often leads to models with low prediction accuracy and incompatibility with real-time execution. Data-driven modelling is a desirable alternative producing high accuracy, asset specific models without the need for derivation from first principles.We present a data-driven system for online detection and prioritisation of anomalous data. Biased data assessment deriving from novel operating conditions is avoided by uncertainty management integrated into the deep neural predictive model. Testing is performed on real and synthetic data, showing sensitivity to both real and synthetic faults. The system is capable of running in real-time on low-power embedded hardware and is currently in deployment on the Rolls-Royce Pearl 15 engine flight trials.

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Separated Linux Process Logging Mechanism for Embedded Systems

Cited by 5 publications

References 3 publications

Sustainable Secure Management Against APT Attacks for Intelligent Embedded-Enabled Smart Manufacturing

Sustainable Secure Management Against APT Attacks for Intelligent Embedded-Enabled Smart Manufacturing

Distributed digital twins for health monitoring: resource constrained aero-engine fleet management

In-flight Novelty Detection with Convolutional Neural Networks

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