Quantized control for a class of neural networks with adaptive event‐triggered scheme and complex cyber‐attacks

Liu, Jinliang; Suo, Wei; Xie, Xiangpeng; Yue, Dong; Cao, Jinde

doi:10.1002/rnc.5500

Cited by 42 publications

(31 citation statements)

References 42 publications

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“…In the networked environment, the cyber-attacks occur frequently which pose a serious threat on the network security and the system performance. [24][25][26] According to different attack characteristics, common cyber-attacks include the following three categories: deception attacks, 27 replay attacks, 28 and denial-of-service 29 attacks. Specifically, the deception attacks including false data injection and data modification do not prevent data transmission while destroying the authenticity of transmitted data.…”

Section: Introductionmentioning

confidence: 99%

“…In the networked environment, the cyber‐attacks occur frequently which pose a serious threat on the network security and the system performance 24‐26 . According to different attack characteristics, common cyber‐attacks include the following three categories: deception attacks, 27 replay attacks, 28 and denial‐of‐service 29 attacks.…”

Section: Introductionmentioning

confidence: 99%

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Event‐triggered fault estimation and nonfragile synchronization control for multiweighted complex dynamical networks with deception attacks

Sakthivel

Kwon

Park

et al. 2022

Intl J Robust & Nonlinear

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This article addresses the fault estimation and nonfragile synchronization control for multiweighted complex dynamical networks against deception attacks. An event‐triggered mechanism is proposed to reduce the redundant data transmission over the communication networks. Considering the network communication security, this article investigates deception attacks in the observer, where the adversary attempts to inject some false data in the measurement output to modify the transmitted signal in the communication networks. Some stochastic variables obeying the Bernoulli distribution are expressed to describe the probability of the deception attacks, which is determined by an attacker. Then based on Lyapunov stability theory, robust analysis method, some novel solvability criteria are presented such that the resultant augmented error system is asymptotically stable with a guaranteed H∞$$ {H}_{\infty } $$ performance index. Further, the existence criterion of the desired event‐triggered estimator is proposed in terms of a set of linear matrix inequalities. Finally, two illustrative examples including an F‐404 aircraft engine model are provided to verify the effectiveness of the developed estimator design method.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Event‐triggered fault estimation and nonfragile synchronization control for multiweighted complex dynamical networks with deception attacks

Sakthivel

Kwon

Park

et al. 2022

Intl J Robust & Nonlinear

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“…In an early attempt to categorize music reviews, for example, work on multi-class genre categorization and star rating prediction is mentioned. Using an innovative method for predicting how people would use music, known as agglomerative clustering, they discovered that bigram characteristics were more informative than unigram features in their research [ 17 ]. Thus, POS tags and pattern mining techniques are employed to extract descriptive patterns for discriminating between positive and negative reviews.…”

Section: Introductionmentioning

confidence: 99%

The Classification of Music and Art Genres under the Visual Threshold of Deep Learning

Zheng

2022

Computational Intelligence and Neuroscience

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Wireless networks are commonly employed for ambient assisted living applications, and artificial intelligence-enabled event detection and classification processes have become familiar. However, music is a kind of time-series data, and it is challenging to design an effective music genre classification (MGC) system due to a large quantity of music data. Robust MGC techniques necessitate a massive amount of data, which is time-consuming, laborious, and requires expert knowledge. Few studies have focused on the design of music representations extracted directly from input waveforms. In recent times, deep learning (DL) models have been widely used due to their characteristics of automatic extracting advanced features and contextual representation from actual music or processed data. This paper aims to develop a novel deep learning-enabled music genre classification (DLE-MGC) technique. The proposed DLE-MGC technique effectively classifies the music genres into multiple classes by using three subprocesses, namely preprocessing, classification, and hyperparameter optimization. At the initial stage, the Pitch to Vector (Pitch2vec) approach is applied as a preprocessing step where the pitches in the input musical instrument digital interface (MIDI) files are transformed into the vector sequences. Besides, the DLE-MGC technique involves the design of a cat swarm optimization (CSO) with bidirectional long-term memory (BiLSTM) model for the classification process. The DBTMPE technique has gained a moderately increased accuracy of 94.27%, and the DLE-MGC technique has accomplished a better accuracy of 95.87%. The performance validation of the DLE-MGC technique was carried out using the Lakh MIDI music dataset, and the comparative results verified the promising performance of the DLE-MGC technique over current methods.

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“…To decrease transmission frequency while ensuring the stability of the system, event-triggered scheme was proposed to save the limited network resources, under which the sampled data to be released into the network only when the corresponding event occurs. [14][15][16][17] The effectiveness of the event-triggered scheme in getting rid of the redundant data has been illustrated by some researchers. For instance, Yue et al proposed an event-triggered scheme based on period sampling 14 and discussed the stability of NCS under the developed event-triggered scheme.…”

Section: Introductionmentioning

confidence: 99%

An event‐triggered approach to security control for networked systems using hybrid attack model

Liu

Wang

Zha

et al. 2021

Intl J Robust & Nonlinear

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This article presents an event-triggered approach to security control for networked systems by using hybrid attack model. Stochastic deception attacks, replay attacks, and denial of service attacks are assumed to occur in the communication network. The main contribution of the article is that an event-triggered control strategy is presented to stabilize the networked systems based on the hybrid attacks and the network resource limitations. A novel event-based model for networked control systems under hybrid attacks is established. Based on the proposed model, sufficient conditions are acquired to guarantee the closed-loop system stability and the controller design method is developed. Finally, two simulation examples are given to validate the feasibility of the proposed method.

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Quantized control for a class of neural networks with adaptive event‐triggered scheme and complex cyber‐attacks

Cited by 42 publications

References 42 publications

Event‐triggered fault estimation and nonfragile synchronization control for multiweighted complex dynamical networks with deception attacks

Event‐triggered fault estimation and nonfragile synchronization control for multiweighted complex dynamical networks with deception attacks

The Classification of Music and Art Genres under the Visual Threshold of Deep Learning

An event‐triggered approach to security control for networked systems using hybrid attack model

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