• Journal of the Korea Institute of Information Security & Cryptology
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• v.32 no.6
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• pp.1091-1102
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• 2022

Robotic systems have developed very rapidly over the past decade. Robot Operating System is an open source-based software framework for the efficient development of robot operating systems and applications, and is widely used in various research and industrial fields. ROS applications may contain various vulnerabilities. Various studies have been conducted to monitor the excution of these ROS applications at runtime. In this study, we propose a real-time attack detection system using event-based runtime monitoring in ROS 2. Our attack detection system extends tracetools of ros2_tracing to instrument events into core libraries of ROS 2 middleware layer and monitors the events during runtime to detect attacks on the application layer through out-of-order execution of the APIs.

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.11
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• pp.1074-1081
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• 2011

As robotics systems are becoming more complex there is the need to promote component based robot development, where systems can be constructed as the composition and integration of reusable building block. One of the most important challenges facing component based robot development is safeguarding against software component failures and malfunctions. The health monitoring of the robot software is most fundamental factors not only to manage system at runtime but also to analysis information of software component in design phase of the robot application. And also as a lot of monitoring events are occurred during the execution of the robot software components, a simple data treatment and efficient memory management method is required. In this paper, we propose an efficient events monitoring and data management method by modeling robot software component and monitoring factors based on robot software framework. The monitoring factors, such as component execution runtime exception, Input/Output data, execution time, checkpoint-rollback are deduced and the detail monitoring events are defined. Furthermore, we define event record and monitor record pool suitable for robot software components and propose a efficient data management method. To verify the effectiveness and usefulness of the proposed approach, a monitoring module and user interface has been implemented using OPRoS robot software framework. The proposed monitoring module can be used as monitoring tool to analysis the software components in robot design phase and plugged into self-healing system to monitor the system health status at runtime in robot systems.

• Journal of KIISE:Software and Applications
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• v.37 no.12
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• pp.892-908
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• 2010

Services in SOA are typically perceived as black-box to service consumers, and can be dynamically evolved at runtime, and run on a number of unknown and heterogeneous environments. Because of these characteristics of the services, effective and efficient monitoring of various aspects on services is an essential functionality for autonomous management of service. But the problem with or limitation in conventional or existing approaches is, that they focus on services themselves, ignoring the effects by business processes. Consequently, there is a room for service monitoring which provides more useful information of business level by acquisition of only external monitoring data that depend on specific BPEL engine and middleware. Moreover, there is a strong demand to present effective methods to reduce monitoring overhead which can degrade quality of services. EDA can cope with such limitations in SOA by collecting and analyzing events efficiently. In this paper, we first describe EDA benefits in service monitoring, and classify monitorring target, and present an appropriate monitoring method for each monitoring target. Also to provide the applicability of our approach, an event meta-model is defined, and event processing model and architecture based on the meta-model are proposed. And, with the proposed architecture and method, we implement a prototype of an event-driven dynamic monitoring framework which can collect and process internal and external data at runtime. Finally, we present the result of a case study to demonstrate the effectiveness and applicability of the proposed approach.

• ETRI Journal
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• v.39 no.2
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• pp.202-212
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• 2017

With the advent of the Internet-of-Things paradigm, the amount of data production has grown exponentially and the user demand for responsive consumption of data has increased significantly. Herein, we present DART, a fast and lightweight stream processing framework for the IoT environment. Because the DART framework targets a geospatially distributed environment of heterogeneous devices, the framework provides (1) an end-user tool for device registration and application authoring, (2) automatic worker node monitoring and task allocations, and (3) runtime management of user applications with fault tolerance. To maximize performance, the DART framework adopts an actor model in which applications are segmented into microtasks and assigned to an actor following a single responsibility. To prove the feasibility of the proposed framework, we implemented the DART system. We also conducted experiments to show that the system can significantly reduce computing burdens and alleviate network load by utilizing the idle resources of intermediate edge devices.