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State Space Representation

State Space Representation

The frequency-domain technique, commonly used in analyzing and designing feedback control systems, is effective for linear, time-invariant systems. However, it falls short when dealing with nonlinear, time-varying, and multiple-input multiple-output systems. The time-domain or state-space approach addresses these limitations by utilizing state variables to construct simultaneous, first-order differential equations, known as state equations, for an nth-order system.
Consider an RLC circuit, a...

Assembly of Signaling Complexes

Assembly of Signaling Complexes

Multiprotein signaling complexes are formed in a dynamic process involving protein-protein interactions at the cytoplasmic domain of transmembrane receptors or enzymatic and non-enzymatic proteins associated with the receptor. These complexes ensure the activation and propagation of intracellular signals that regulate cell functions.
Interaction domains in cell signaling
Interaction domains recognize exposed features of their binding partners containing post-translationally modified sequences,...

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Updated: Jan 9, 2026

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

ChainShieldML是一个智能去中心化安全框架,用于下一代无线传感器网络.

Dileep Kumar Murala¹, Shadab Ahmad², V A Sankar Ponnapalli³

¹Department of Computer Science and Engineering, Faculty of Science and Technology, ICFAI Foundation for Higher Education, Hyderabad, 501203, Telangana, India.

Scientific reports

|December 2, 2025

概括

此摘要是机器生成的。

ChainShieldML为无线传感器网络 (WSN) 提供了一种新的混合安全架构. 该系统结合了区块链和机器学习,以在物联网应用中有效,分散和适应性地保护内部威胁.

关键词:

区块链技术是区块链技术.物联网 (IoT) 的物联网 (IoT) 的物联网.侵入检测入侵检测系统可以检测入侵.机器学习是机器学习.安全的安全的安全的安全的安全.无线传感器网络 (WSN) 是指无线传感器网络.

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相关实验视频

Last Updated: Jan 9, 2026

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

Implantation and Control of Wireless, Battery-free Systems for Peripheral Nerve Interfacing

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Published on: October 20, 2021

Integration of 5G Experimentation Infrastructures into a Multi-Site NFV Ecosystem

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Published on: February 3, 2021

科学领域:

计算机科学计算机科学
网络安全网络安全
网络工程网络工程

背景情况:

无线传感器网络 (WSN) 对于下一代物联网 (IoT) 应用至关重要,在关键领域实现智能自动化.
由于固有的局限性,如低功耗,有限的计算能力和对内部威胁的易感性,WSN面临着重大安全挑战.
对于资源有限的WSN,传统的安全方法往往是不够的,需要创新的解决方案.

研究的目的:

介绍ChainShieldML,一种轻量级的混合安全架构用于WSN.
加强WSN的安全性,可信性和数据完整性,以防止复杂的攻击.
为有限的物联网环境提供一个资源效率高,适应性强的防御机制.

主要方法:

一个双边的防御策略,集成一个无许可区块链来实现分散的信任和身份验证,并使用机器学习模块来检测威胁.
利用以太坊生态系统上的智能合约和VBFT共识算法的区块链预防模块,用于安全的节点注册和不可变的日志记录.
采用LightGBM (Light Gradient Boosting Machine) 算法实时检测和排名恶意节点,优化诸如回忆和推断延迟等性能指标.

主要成果:

ChainShieldML在检测内部攻击和加强WSN内部数据保护方面取得了重大改进.
该架构在能源消耗和通信延迟方面实现了高效率,这对于资源有限的WSN至关重要.
性能评估证实LightGBM是WSN安全性,精度和速度平衡的最佳机器学习分类器.

结论:

通过区块链和机器学习的协同作用,ChainShieldML为WSN提供了一种新的,资源高效的,并为未来做好准备的安全解决方案.
混合方法有效地解决了WSN的独特安全漏洞,促进了分散的信任和适应性智能.
这种架构对于确保WSN在关键物联网应用中的可靠运行和安全至关重要.