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This paper focuses on the secure integration of distributed energy resources (DERs), especially pluggable electric vehicles (EVs), with the power grid. We consider the vehicle-togrid (V2G) system where EVs are connected to the power grid through an 'aggregator' 1 . In this paper, we propose a novel Cyber-Physical Anomaly Detection Engine that monitors system behavior and detects anomalies almost instantaneously (worst case inspection time for a packet is 0.165 seconds 2 ). This detection engine ensures that the critical power grid component (viz.,aggregator) remains secure by monitoring (a) cyber messages for various state changes and data constraints along with (b) power data on the V2G cyber network using power measurements from sensors on the physical/power distribution network. Since the V2G system is time-sensitive, the anomaly detection engine also monitors the timing requirements of the protocol messages to enhance the safety of the aggregator. To the best of our knowledge, this is the first piece of work that combines (a) the EV charging/discharging protocols, the (b) cyber network and (c) power measurements from physical network to detect intrusions in the EV to power grid system.

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The data generated by large scale scientific systems such as NASA's Earth Observing System Data and Information System is expected to increase substantially. Consequently, applications processing these huge volumes of data suffer from lack of storage space at the execution site. This poses a critical challenge while sharing data and reproducing application executions w.r.t. specific user inputs in dataintensive applications. To address this issue, we propose IOSPReD (I/O Specialized Packaging of Reduced Datasets), a data-based debloating framework, designed to automatically track and package only necessary chunks of data (along with the application) in a container. IOSPReD uses the specific inputs provided by the user to identify the necessary data chunks. To do so, the high level user inputs are mapped down to low level data file offsets. We evaluate IOSPReD on different realistic NASA datasets to assess (i) the amount of data reduction, (ii) the reproducibility of results across multiple application executions and also (iii) the impact on performance.