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Gui, J., Li, D., Chen, Z., Rhee, J., Xiao, X., Zhang, M., Jee, K., Li, Z., Chen, H..  2020.  APTrace: A Responsive System for Agile Enterprise Level Causality Analysis. 2020 IEEE 36th International Conference on Data Engineering (ICDE). :1701–1712.
While backtracking analysis has been successful in assisting the investigation of complex security attacks, it faces a critical dependency explosion problem. To address this problem, security analysts currently need to tune backtracking analysis manually with different case-specific heuristics. However, existing systems fail to fulfill two important system requirements to achieve effective backtracking analysis. First, there need flexible abstractions to express various types of heuristics. Second, the system needs to be responsive in providing updates so that the progress of backtracking analysis can be frequently inspected, which typically involves multiple rounds of manual tuning. In this paper, we propose a novel system, APTrace, to meet both of the above requirements. As we demonstrate in the evaluation, security analysts can effectively express heuristics to reduce more than 99.5% of irrelevant events in the backtracking analysis of real-world attack cases. To improve the responsiveness of backtracking analysis, we present a novel execution-window partitioning algorithm that significantly reduces the waiting time between two consecutive updates (especially, 57 times reduction for the top 1% waiting time).
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He, S., Cheng, B., Wang, H., Xiao, X., Cao, Y., Chen, J..  2018.  Data security storage model for fog computing in large-scale IoT application. IEEE INFOCOM 2018 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS). :39–44.

With the scale of big data increasing in large-scale IoT application, fog computing is a recent computing paradigm that is extending cloud computing towards the edge of network in the field. There are a large number of storage resources placed on the edge of the network to form a geographical distributed storage system in fog computing system (FCS). It is used to store the big data collected by the fog computing nodes and to reduce the management costs for moving big data to the cloud. However, the storage of fog nodes at the edge of the network faces a direct attack of external threats. In order to improve the security of the storage of fog nodes in FCS, in this paper, we proposed a data security storage model for fog computing (FCDSSM) to realize the integration of storage and security management in large-scale IoT application. We designed a detail of the FCDSSM system architecture, gave a design of the multi-level trusted domain, cooperative working mechanism, data synchronization and key management strategy for the FCDSSM. Experimental results show that the loss of computing and communication performance caused by data security storage in the FCDSSM is within the acceptable range, and the FCDSSM has good scalability. It can be adapted to big data security storage in large-scale IoT application.