Individual-Based Modelling of Animal Brucellosis Spread with the Use of Complex Networks

Pinto, Eduardo Ribeiro; Nepomuceno, Erivelton G.; Campanharo, Andriana S. L. O.

doi:10.53941/ijndi0101011

Cited by 25 publications

(9 citation statements)

References 32 publications

(25 reference statements)

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“…important packets are vulnerable, that is, the attack probability of the important packet is larger than the less important ones. The attack decision is represented by a stochastic variable 𝛿 k = 1 defined in (6). From the defense perspective, an acknowledgment frame (ACK) is employed to generate an acknowledge signal 𝛾 k , which is a variable that indicates whether the packet y k is attacked.…”

Section: Remote State Estimationmentioning

confidence: 99%

“…Leveraging the network for data transmission, as opposed to traditional point-to-point connection methods, the local data processing and storage have been significantly simplified, facilitating rapid decision-making within the system. [4][5][6] Regrettably, the openness of the network has led to a proliferation of security problems in the system control or state estimation. [7][8][9] One of the most profound concerns is that the CPS is vulnerable to malicious attacks, which will result in system performance degradation or even instability.…”

Section: Introductionmentioning

confidence: 99%

“…In general, the components of CPS are connected through a wired/wireless network. Leveraging the network for data transmission, as opposed to traditional point‐to‐point connection methods, the local data processing and storage have been significantly simplified, facilitating rapid decision‐making within the system 4–6 …”

Section: Introductionmentioning

confidence: 99%

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Stochastic important‐data‐based attack model and defense strategies for cyber‐physical system: A data‐driven method

Zhang,

Zhao,

Tian

et al. 2024

Intl J Robust & Nonlinear

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This article investigates the security ensured state estimation problem for cyber‐physical system via a data‐driven method. First, based on the fact that different packets possess varying a degree of significance, that is, some packets play more important roles in the state estimation than others, a novel stochastic important‐data‐based (IDB) attack mechanism is constructed from the attacker's perspective, which can focus on attacking the important packets thus is expected to achieve more destructiveness. Second, as a countermeasure to the proposed IDB attack, a new data‐driven compensation method is proposed, for the first attempt, to compensate for the attack effect and enhance the estimation quality. The designed defense strategy has the following two advantages: (1) only system input and output data are utilized to establish the novel estimator, without knowing the actual system model knowledge, and (2) by constructing a data‐driven output predictor to compensate for the data loss, the accuracy of the state estimation can be efficiently improved. With the aid of the least squares technique and completing square technique, a minimum upper bound matrix for the estimation error covariance is obtained by properly designing the estimator gain. Finally, an illustrative example is given to highlight the destructiveness of the designed stochastic IDB attack and the effectiveness of the proposed novel data‐based compensation method.

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Section: Remote State Estimationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Stochastic important‐data‐based attack model and defense strategies for cyber‐physical system: A data‐driven method

Zhang,

Zhao,

Tian

et al. 2024

Intl J Robust & Nonlinear

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show abstract

“…Recently, artificial neural networks have been successfully applied to RSs owing to their strong feature extraction abilities [7,22,35,39,42,47,49]. For example, a convolutional sequence embedding (Caser) RS has been proposed in [42] for product recommendation, where a convolutional neural network (CNN) is employed to capture the sequential features by analyzing the embedding matrix.…”

Section: Introductionmentioning

confidence: 99%

A unified approach to designing sequence-based personalized food recommendation systems: tackling dynamic user behaviors

Zhang

Wang

Liu

et al. 2023

Int. J. Mach. Learn. & Cyber.

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The recommender system (RS) is a well-known practical application of the state-of-the-art information filtering and machine learning technologies. Traditional recommendation approaches, including collaborative and content-based filtering techniques, have been widely employed to provide suggestions in RSs, where the user-item interaction matrix is the primary data source. In many application domains, interactions between users and items are more likely to be dynamic rather than static, and thus dynamic user behaviors should be taken into account when solving recommendation tasks in order to provide more accurate suggestions. In this work, we consider the sequentially ordered information from user-item interactions in the RSs where a sequence-based recommendation model is put forward with applications to the food recommendation scenario. Furthermore, the long short-term memory (LSTM) network is employed as the building block to establish such a recommendation model, and a collaborative filtering unit is adopted to make personalized food recommendation. The proposed LSTM-based RS is successfully applied to a real-world food recommendation data set. Experimental results demonstrate that the developed method outperforms some currently popular RSs in terms of precision, recall, mean average precision and mean reciprocal rank in food recommendation.

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“…With the developments of the network era, the complex networks (CNs) have been widely used in various fields, such as power grids, 1 disease spread, 2 biological networks, 3 neural networks 4 and social networks 5 . It is worthwhile to mention that the CNs are composed of numerous nodes and edges with massive scales 6‐9 .…”

Section: Introductionmentioning

confidence: 99%

Partial‐nodes‐based state estimation for state‐saturated complex networks with random mixed couplings under dynamic event‐triggered protocol

Lin

Chen

et al. 2023

Adaptive Control & Signal

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The state estimation (SE) problem is addressed for state-saturated complex networks (CNs) contaminated by the random mixed couplings. Such coupling phenomenon consists of the random inner coupling and the random outer coupling, where the random inner coupling is reflected by multiplicative noise and the random outer coupling is described by random variable obeying uniform distribution. In order to reduce resource waste and calculation cost, the additional auxiliary variable method is employed to construct the dynamic event-triggered communication condition, which can more effectively represent the change of information demand. Accordingly, a novel partial-nodes-based (PNB) estimator is designed in the presence of general nonlinearity and state-saturated nonlinearity, which can estimate the whole states of network nodes with the aid of the measurement information from partial nodes. The appropriate estimator gain is obtained to ensure that the upper bound of estimation error covariance (UBEEC) is minimized. Moreover, sufficient condition is given to guarantee that the trace of the UBEEC is uniformly bounded. Finally, the numerical example is presented to demonstrate the feasibility and effectiveness of the developed PNB estimation algorithm.

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Individual-Based Modelling of Animal Brucellosis Spread with the Use of Complex Networks

Cited by 25 publications

References 32 publications

Stochastic important‐data‐based attack model and defense strategies for cyber‐physical system: A data‐driven method

Stochastic important‐data‐based attack model and defense strategies for cyber‐physical system: A data‐driven method

A unified approach to designing sequence-based personalized food recommendation systems: tackling dynamic user behaviors

Partial‐nodes‐based state estimation for state‐saturated complex networks with random mixed couplings under dynamic event‐triggered protocol

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