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Register a new userGraph-based Model of Smart Grid Architectures
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Added by
Martin Henze
Publication date
2020
fields
Informatics Engineering
and research's language is
English
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The rising use of information and communication technology in smart grids likewise increases the risk of failures that endanger the security of power supply, e.g., due to errors in the communication configuration, faulty control algorithms, or cyber-attacks. Co-simulations can be used to investigate such effects, but require precise modeling of the energy, communication, and information domain within an integrated smart grid infrastructure model. Given the complexity and lack of detailed publicly available communication network models for smart grid scenarios, there is a need for an automated and systematic approach to creating such coupled models. In this paper, we present an approach to automatically generate smart grid infrastructure models based on an arbitrary electrical distribution grid model using a generic architectural template. We demonstrate the applicability and unique features of our approach alongside examples concerning network planning, co-simulation setup, and specification of domain-specific intrusion detection systems.
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Smart contracts are automated or self-enforcing contracts that can be used to exchange assets without having to place trust in third parties. Many commercial transactions use smart contracts due to their potential benefits in terms of secure peer-to-peer transactions independent of external parties. Experience shows that many commonly used smart contracts are vulnerable to serious malicious attacks which may enable attackers to steal valuable assets of involving parties. There is therefore a need to apply analysis and automated repair techniques to detect and repair bugs in smart contracts before being deployed. In this work, we present the first general-purpose automated smart contract repair approach that is also gas-aware. Our repair method is search-based and searches among mutations of the buggy contract. Our method also considers the gas usage of the candidate patches by leveraging our novel notion of gas dominance relationship. We have made our smart contract repair tool SCRepair available open-source, for investigation by the wider community.
The emerging Internet of Things (IoT) is facing significant scalability and security challenges. On the one hand, IoT devices are weak and need external assistance. Edge computing provides a promising direction addressing the deficiency of centralized cloud computing in scaling massive number of devices. On the other hand, IoT devices are also relatively vulnerable facing malicious hackers due to resource constraints. The emerging blockchain and smart contracts technologies bring a series of new security features for IoT and edge computing. In this paper, to address the challenges, we design and prototype an edge-IoT framework named EdgeChain based on blockchain and smart contracts. The core idea is to integrate a permissioned blockchain and the internal currency or coin system to link the edge cloud resource pool with each IoT device account and resource usage, and hence behavior of the IoT devices. EdgeChain uses a credit-based resource management system to control how much resource IoT devices can obtain from edge servers, based on pre-defined rules on priority, application types and past behaviors. Smart contracts are used to enforce the rules and policies to regulate the IoT device behavior in a non-deniable and automated manner. All the IoT activities and transactions are recorded into blockchain for secure data logging and auditing. We implement an EdgeChain prototype and conduct extensive experiments to evaluate the ideas. The results show that while gaining the security benefits of blockchain and smart contracts, the cost of integrating them into EdgeChain is within a reasonable and acceptable range.
Complex interconnections between information technology and digital control systems have significantly increased cybersecurity vulnerabilities in smart grids. Cyberattacks involving data integrity can be very disruptive because of their potential to compromise physical control by manipulating measurement data. This is especially true in large and complex electric networks that often rely on traditional intrusion detection systems focused on monitoring network traffic. In this paper, we develop an online detection algorithm to detect and localize covert attacks on smart grids. Using a network system model, we develop a theoretical framework by characterizing a covert attack on a generator bus in the network as sparse features in the state-estimation residuals. We leverage such sparsity via a regularized linear regression method to detect and localize covert attacks based on the regression coefficients. We conduct a comprehensive numerical study on both linear and nonlinear system models to validate our proposed method. The results show that our method outperforms conventional methods in both detection delay and localization accuracy.
With the prosperity of smart contracts and the blockchain technology, various security analyzers have been proposed from both the academia and industry to address the associated risks. Yet, there does not exist a high-quality benchmark of smart contract vulnerability for security research. In this study, we propose an approach towards building a high-quality vulnerability benchmark. Our approach consists of two parts. First, to improve recall, we propose to search for similar vulnerabilities in an automated way by leveraging the abstract vulnerability signature (AVS). Second, to remove the false positives (FPs) due to AVS-based matching, we summarize the detection rules of existing tools and apply the refined rules by considering various defense mechanisms (DMs). By integrating AVS-based code matching and the refined detection rules (RDR), our approach achieves higher precision and recall. On the collected 76,354 contracts, we build a benchmark consisting of 1,219 vulnerabilities covering five different vulnerability types identified together by our tool (DOUBLADE) and other three scanners. Additionally, we conduct a comparison between DOUBLADE and the others, on an additional 17,770 contracts. Results show that DOUBLADE can yield a better detection accuracy with similar execution time.
With the widely used smart meters in the energy sector, anomaly detection becomes a crucial mean to study the unusual consumption behaviors of customers, and to discover unexpected events of using energy promptly. Detecting consumption anomalies is, essentially, a real-time big data analytics problem, which does data mining on a large amount of parallel data streams from smart meters. In this paper, we propose a supervised learning and statistical-based anomaly detection method, and implement a Lambda system using the in-memory distributed computing framework, Spark and its extension Spark Streaming. The system supports not only iterative detection model refreshment from scalable data sets, but also real-time detection on scalable live data streams. This paper empirically evaluates the system and the detection algorithm, and the results show the effectiveness and the scalability of the proposed lambda detection system.
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