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Xu, Zhiheng, Ng, Daniel Jun Xian, Easwaran, Arvind.  2019.  Automatic Generation of Hierarchical Contracts for Resilience in Cyber-Physical Systems. 2019 IEEE 25th International Conference on Embedded and Real-Time Computing Systems and Applications (RTCSA). :1–11.

With the growing scale of Cyber-Physical Systems (CPSs), it is challenging to maintain their stability under all operating conditions. How to reduce the downtime and locate the failures becomes a core issue in system design. In this paper, we employ a hierarchical contract-based resilience framework to guarantee the stability of CPS. In this framework, we use Assume Guarantee (A-G) contracts to monitor the non-functional properties of individual components (e.g., power and latency), and hierarchically compose such contracts to deduce information about faults at the system level. The hierarchical contracts enable rapid fault detection in large-scale CPS. However, due to the vast number of components in CPS, manually designing numerous contracts and the hierarchy becomes challenging. To address this issue, we propose a technique to automatically decompose a root contract into multiple lower-level contracts depending on I/O dependencies between components. We then formulate a multi-objective optimization problem to search the optimal parameters of each lower-level contract. This enables automatic contract refinement taking into consideration the communication overhead between components. Finally, we use a case study from the manufacturing domain to experimentally demonstrate the benefits of the proposed framework.

Zaman, Tarannum Shaila, Han, Xue, Yu, Tingting.  2019.  SCMiner: Localizing System-Level Concurrency Faults from Large System Call Traces. 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE). :515—526.

Localizing concurrency faults that occur in production is hard because, (1) detailed field data, such as user input, file content and interleaving schedule, may not be available to developers to reproduce the failure; (2) it is often impractical to assume the availability of multiple failing executions to localize the faults using existing techniques; (3) it is challenging to search for buggy locations in an application given limited runtime data; and, (4) concurrency failures at the system level often involve multiple processes or event handlers (e.g., software signals), which can not be handled by existing tools for diagnosing intra-process(thread-level) failures. To address these problems, we present SCMiner, a practical online bug diagnosis tool to help developers understand how a system-level concurrency fault happens based on the logs collected by the default system audit tools. SCMiner achieves online bug diagnosis to obviate the need for offline bug reproduction. SCMiner does not require code instrumentation on the production system or rely on the assumption of the availability of multiple failing executions. Specifically, after the system call traces are collected, SCMiner uses data mining and statistical anomaly detection techniques to identify the failure-inducing system call sequences. It then maps each abnormal sequence to specific application functions. We have conducted an empirical study on 19 real-world benchmarks. The results show that SCMiner is both effective and efficient at localizing system-level concurrency faults.

Koul, Ajay, Kaur, Harinder.  2017.  Quality of Service Oriented Secure Routing Model for Mobile Ad Hoc Networks. Proceedings of the 2017 International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence. :88–92.

Mobile Ad hoc Networks (MANETs) always bring challenges to the designers in terms of its security deployment due to their dynamic and infrastructure less nature. In the past few years different researchers have proposed different solutions for providing security to MANETs. In most of the cases however, the solution prevents either a particular attack or provides security at the cost of sacrificing the QoS. In this paper we introduce a model that deploys security in MANETs and takes care of the Quality of Services issues to some extent. We have adopted the concept of analyzing the behavior of the node as we believe that if nodes behave properly and in a coordinated fashion, the insecurity level goes drastically down. Our methodology gives the advantage of using this approach