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Modern electric grids that integrate smart grid technologies require different approaches to grid operations. There has been a shift towards increased reliance on distributed sensors to monitor bidirectional power flows and machine learning based load forecasting methods (e.g., using deep learning). These methods are fairly accurate under normal circumstances, but become highly vulnerable to stealthy adversarial attacks that could be deployed on the load forecasters. This paper provides a novel model-based Testbed for Simulation-based Evaluation of Resilience (TeSER) that enables evaluating deep learning based load forecasters against stealthy adversarial attacks. The testbed leverages three existing technologies, viz. DeepForge: for designing neural networks and machine learning pipelines, GridLAB-D: for electric grid distribution system simulation, and WebGME: for creating web-based collaborative metamodeling environments. The testbed architecture is described, and a case study to demonstrate its capabilities for evaluating load forecasters is provided. 

Recent advances in machine learning enable wider applications of prediction models in cyber-physical systems. Smart grids are increasingly using distributed sensor settings for distributed sensor fusion and information processing. Load forecasting systems use these sensors to predict future loads to incorporate into dynamic pricing of power and grid maintenance. However, these inference predictors are highly complex and thus vulnerable to adversarial attacks. Moreover, the adversarial attacks are synthetic norm-bounded modifications to a limited number of sensors that can greatly affect the accuracy of the overall predictor. It can be much cheaper and effective to incorporate elements of security and resilience at the earliest stages of design. In this paper, we demonstrate how to analyze the security and resilience of learning-based prediction models in power distribution networks by utilizing a domain-specific deep-learning and testing framework. This framework is developed using DeepForge and enables rapid design and analysis of attack scenarios against distributed smart meters in a power distribution network. It runs the attack simulations in the cloud backend. In addition to the predictor model, we have integrated an anomaly detector to detect adversarial attacks targeting the predictor. We formulate the stealthy adversarial attacks as an optimization problem to maximize prediction loss while minimizing the required perturbations. Under the worst-case setting, where the attacker has full knowledge of both the predictor and the detector, an iterative attack method has been developed to solve for the adversarial perturbation. We demonstrate the framework capabilities using a GridLAB-D based power distribution network model and show how stealthy adversarial attacks can affect smart grid prediction systems even with a partial control of network. 

Owing1 to an immense growth of internet-connected and learning-enabled cyber-physical systems (CPSs) [1], several new types of attack vectors have emerged. Analyzing security and resilience of these complex CPSs is difficult as it requires evaluating many subsystems and factors in an integrated manner. Integrated simulation of physical systems and communication network can provide an underlying framework for creating a reusable and configurable testbed for such analyses. Using a model-based integration approach and the IEEE High-Level Architecture (HLA) [2] based distributed simulation software; we have created a testbed for integrated evaluation of large-scale CPS systems. Our tested supports web-based collaborative metamodeling and modeling of CPS system and experiments and a cloud computing environment for executing integrated networked co-simulations. A modular and extensible cyber-attack library enables validating the CPS under a variety of configurable cyber-attacks, such as DDoS and integrity attacks. Hardware-in-the-loop simulation is also supported along with several hardware attacks. Further, a scenario modeling language allows modeling of alternative paths (Courses of Actions) that enables validating CPS under different what-if scenarios as well as conducting cyber-gaming experiments. These capabilities make our testbed well suited for analyzing security and resilience of CPS. In addition, the web-based modeling and cloud-hosted execution infrastructure enables one to exercise the entire testbed using simply a web-browser, with integrated live experimental results display. 

Abstract: The paper introduces a visual programming language and corresponding web- and cloud-based development environment called NetsBlox. NetsBlox is an extension of Snap! and it builds upon its visual formalism as well as its open source code base. NetsBlox adds distributed programming capabilities to Snap! by introducing two simple abstractions: messages and NetsBlox services. Messages containing data can be exchanged by two or more NetsBlox programs running on different computers connected to the Internet. Services are called on a client program and are executed on the NetsBlox server. These two abstractions make it possible to create distributed programs, for example multi-player games or client-server applications. We believe that NetsBlox provides increased motivation to high-school students to become creators and not just consumers of technology. At the same time, it helps teach them basic distributed programming concepts. 

This paper introduces NetsBlox, a visual programming environment for learning distributed programming principles. Extending both the visual formalism and open source code base of Snap!, NetsBlox provides two accessible distributed programming abstractions to simplify the process of creating networked applications: message passing and Remote Procedure Calls (RPC). Messaging passing allows NetsBlox applications to send data to other connected NetsBlox clients. Remote Procedure Calls enable seamless integration of third party services, such as Google Maps, weather, traffic and other public domain data sources, into NetsBlox applications. Other RPCs help coordinating distributed clients which may be difficult for novice programmers allowing the user to more quickly create captivating and sophisticated applications. These abstractions empower users to develop networked programs, including multi-player games and client-server applications. By providing networking support, NetsBlox not only allows users to learn distribute programming concepts but also makes programming more engaging by incorporating diverse services available on the web.