A Method for Identifying Critical Elements of a Cyber-Physical System Under Data Attack

Fan, Youping; Li, Jingjiao; Zhang, Dai

doi:10.1109/access.2018.2812812

Cited by 18 publications

(26 citation statements)

References 44 publications

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“…F2 is a distance protection function containing nine LNs. Once the impedance, admittance, or reactance of the line calculated by the TCTR and TVTR exceeds the preset PDIS limit, the line distance protection will be triggered and the XCBR will be open [18]. The modified hypergraph model of an SAS describes the connection between two LNs by edge and the relation between LNs and functions by hyperedge, which overcomes the drawbacks of simple graph or hypergraph methods.…”

Section: Modified Hypergraph Model Of the Sasmentioning

confidence: 99%

“…Hypergraph theory has been used to model the logical structure of SASs because it makes modeling a network with heterogeneous nodes or a network of networks feasible. In our previous research work [18], each logical function consisting of several logical nodes in an SAS is defined as a hyperedge in hypergraph theory. Then the efficiency indexes are defined by choosing some indexes from the hyper-network model of each SAS.…”

Section: Introductionmentioning

confidence: 99%

“…A CPS' effectiveness model is established taking the functional influence after an attack into account to identify the critical elements of the CPS. In addition, Reference [18] has introduced the cyberattack process procedures and divided the data attacks on SASs into two categories, data tampering attack and data jamming attack, according to their impact. Also, it has provided a new avenue for the research in this paper and the contrastive test results.…”

Section: Introductionmentioning

confidence: 99%

Section: Introduction To Sas Structurementioning

confidence: 99%

See 3 more Smart Citations

Supporting Sustainable Maintenance of Substations under Cyber-Threats: An Evaluation Method of Cybersecurity Risk for Power CPS

Fan

Zhang

et al. 2019

Sustainability

Self Cite

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In the increasingly complex cyber-environment, appropriate sustainable maintenance of substation auto systems (SASs) can lead to many positive effects on power cyber-physical systems (CPSs). Evaluating the cybersecurity risk of power CPSs is the first step in creating sustainable maintenance plans for SASs. In this paper, a mathematical framework for evaluating the cybersecurity risk of a power CPS is proposed considering both the probability of successful cyberattacks on SASs and their consequences for the power system. First, the cyberattacks and their countermeasures are introduced, and the probability of successful cyber-intruding on SASs is modeled from the defender’s perspective. Then, a modified hypergraph model of the SAS’s logical structure is established to quantitatively analyze the impacts of cyberattacks on an SAS. The impacts will ultimately act on the physical systems of the power CPS. The modified hypergraph model can describe more information than a graph or hypergraph model and potentially can analyze complex networks like CPSs. Finally, the feasibility and effectiveness of the proposed evaluation method is verified by the IEEE 14-bus system, and the test results demonstrate that this proposed method is more reasonable to assess the cybersecurity risk of power CPS compared with some other models.

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Section: Modified Hypergraph Model Of the Sasmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introduction To Sas Structurementioning

confidence: 99%

See 2 more Smart Citations

Supporting Sustainable Maintenance of Substations under Cyber-Threats: An Evaluation Method of Cybersecurity Risk for Power CPS

Fan

Zhang

et al. 2019

Sustainability

Self Cite

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“…However, standard machine learning algorithms typically assume clean labels and overlook the risk of noisy labels. Moreover, recent studies point out the increasing dirty data attacks that can maliciously alter the anomaly labels to mislead the machine learning models [10], [15], [18]. As a result, anomaly detection algorithms need to capture not only anomalies that are entangled with system dynamics but also the unreliable nature of anomaly labels.…”

Section: Introductionmentioning

confidence: 99%

Robust Anomaly Detection on Unreliable Data

Zhao

Cerf

Birke

et al. 2019

2019 49th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN)

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Classification algorithms have been widely adopted to detect anomalies for various systems, e.g., IoT, cloud and face recognition, under the common assumption that the data source is clean, i.e., features and labels are correctly set. However, data collected from the wild can be unreliable due to careless annotations or malicious data transformation for incorrect anomaly detection. In this paper, we present a two-layer on-line learning framework for robust anomaly detection (RAD) in the presence of unreliable anomaly labels, where the first layer is to filter out the suspicious data, and the second layer detects the anomaly patterns from the remaining data. To adapt to the on-line nature of anomaly detection, we extend RAD with additional features of repetitively cleaning, conflicting opinions of classifiers, and oracle knowledge. We on-line learn from the incoming data streams and continuously cleanse the data, so as to adapt to the increasing learning capacity from the larger accumulated data set. Moreover, we explore the concept of oracle learning that provides additional information of true labels for difficult data points. We specifically focus on three use cases, (i) detecting 10 classes of IoT attacks, (ii) predicting 4 classes of task failures of big data jobs, (iii) recognising 20 celebrities faces. Our evaluation results show that RAD can robustly improve the accuracy of anomaly detection, to reach up to 98% for IoT device attacks (i.e., +11%), up to 84% for cloud task failures (i.e., +20%) under 40% noise, and up to 74% for face recognition (i.e., +28%) under 30% noisy labels. The proposed RAD is general and can be applied to different anomaly detection algorithms.

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Applications, challenges, and solutions to single- and multi-objective critical node detection problems: a survey

Megzari,

Pravija Raj,

Osamy

et al. 2023

J Supercomput

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A Method for Identifying Critical Elements of a Cyber-Physical System Under Data Attack

Cited by 18 publications

References 44 publications

Supporting Sustainable Maintenance of Substations under Cyber-Threats: An Evaluation Method of Cybersecurity Risk for Power CPS

Supporting Sustainable Maintenance of Substations under Cyber-Threats: An Evaluation Method of Cybersecurity Risk for Power CPS

Robust Anomaly Detection on Unreliable Data

Applications, challenges, and solutions to single- and multi-objective critical node detection problems: a survey

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