Feedback Finite‐Time Stabilization of Impulsive Linear Systems over Piecewise Quadratic Domains

Huang, Jing; Li, Jianxing; Bu, Linshan; Xu, Honglei

doi:10.1155/2021/7651393

Complexity

2021

DOI: 10.1155/2021/7651393

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Feedback Finite‐Time Stabilization of Impulsive Linear Systems over Piecewise Quadratic Domains

Jing Huang

Jianxing Li

Linshan Bu

et al.

Abstract: This paper investigates the state feedback stabilization problem for a class of impulsive linear time-varying systems over specified time intervals and piecewise quadratic domains (PQDs). First, concepts related to finite-time stability and PQDs are given. Second, finite-time stability analysis over PQDs is implemented, and a variety of stability conditions involving differential linear matrix inequalities are investigated. Then, computationally tractable stability conditions are established for the control de… Show more

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Cited by 3 publications

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Intrusion detection systems in the cloud computing: A comprehensive and deep literature review

Liu

Cheng

et al. 2021

Concurrency and Computation

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Abrupt development of resources and rising expenses of infrastructure are leading institutions to take on cloud computing. Albeit, the cloud environment is vulnerable to various sorts of attacks. So, recognizing malicious software is one of the principal challenges in cloud security governance. Intrusion detection system (IDS) has turned to the most generally utilized element of computer system security that asserts the cloud from diverse sorts of attacks and threats. As evident, no systematic literature review exists that focuses on cloud computing usage within IDS processes. The previous investigations had not considered the statistical analysis method. Hence, this paper examined the IDS mechanisms in cloud computing systematically. Twenty-two articles have been obtained using defined filters divided into four sections: hypervisor-based IDS, network-based IDS, machine learning-based IDS, and hybrid IDS. The comparison is performed depending on the outcomes illustrated in the investigations. It demonstrates that IDS precision, inclusiveness, overhead, and reaction time have been discussed in many studies. Simultaneously, less attention has been paid to cost-sensitivity, functioning, attack tolerance, and intrusion facing. This paper has made an excellent effort to organize literature drawn from multiple sources into a manuscript.

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Intrusion detection systems in the cloud computing: A comprehensive and deep literature review

Liu

Cheng

et al. 2021

Concurrency and Computation

View full text Add to dashboard Cite

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A systematic literature review of intrusion detection systems in the cloud‐basedIoTenvironments

Luo¹,

Chen²,

Mohammed

2022

Concurrency and Computation

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As the number of Internet of Things (IoT) subscribers, services, and applications grows, there is a pressing need for a reliable and lightweight security solution that can be used in IoT contexts. Also, due to the open nature of cloud computing, safety concerns are always challenging. One potential solution for this problem is an intrusion detection system (IDS). An ID that utilizes a machine learning method is gaining popularity since it has the benefit of automatically updating to fight against any new form of attack. Due to the importance of IDS in cloud-based IoT, the main articles and essential techniques in this domain are examined systematically. In cloud-based IoT, IDSs are categorized into three major categories, including learning-based, pattern-based, and rule-based mechanisms. The findings illustrate that the biggest challenge in IDS is precision and detection, which many researchers are trying to improve. Also, with the rise of connected objects, the most frequently utilized centralized (cloud-based) IDS struggles with excessive latency and network overhead, leading to delayed detection of unauthorized users and unresponsiveness to assaults. The results will be valuable for academicians, and they can offer visions for future research.

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A multi‐objective method for virtual machines allocation in cloud data centres using an improved grey wolf optimization algorithm

et al. 2021

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Cloud computing is a rapidly evolving computational technology. It is a distributed computational system that offers dynamically scaled computational resources, such as processing power, storage, and applications, delivered as a service through the Internet. Virtual machines (VMs) allocation is known as one of the most significant problems in cloud computing. It aims to find a suitable location for VMs on physical machines (PMs) to attain predefined aims. So, the main purpose is to reduce energy consumption and improve resource utilization. Because the VM allocation issue is NP-hard, meta-heuristic and heuristic methods are frequently utilized to address it. This paper presents an energy-aware VM allocation method using the improved grey wolf optimization (IGWO) algorithm. Our key goals are to decrease both energy consumption and allocation time. The simulation outcomes from the MATLAB simulator approve the excellence of the algorithm compared to previous works.

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Feedback Finite‐Time Stabilization of Impulsive Linear Systems over Piecewise Quadratic Domains

Cited by 3 publications

References 20 publications

Intrusion detection systems in the cloud computing: A comprehensive and deep literature review

Intrusion detection systems in the cloud computing: A comprehensive and deep literature review

A systematic literature review of intrusion detection systems in the cloud‐basedIoTenvironments

A multi‐objective method for virtual machines allocation in cloud data centres using an improved grey wolf optimization algorithm

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