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An Intrusion Detection Model Based on Feature Selection and Improved One-Dimensional Convolutional Neural Network 基于特征选择和改进型一维卷积神经网络的入侵检测模型
IF 2.3 4区 计算机科学 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2023-12-21 DOI: 10.1155/2023/1982173
Qingfeng Li, Bo Li, Linzhi Wen
The problem of intrusion detection has new solutions, thanks to the widespread use of machine learning in the field of network security, but it still has a few issues at this time. Traditional machine learning techniques to intrusion detection rely on expert experience to choose features, and deep learning approaches have a low detection efficiency. In this paper, an intrusion detection model based on feature selection and improved one-dimensional convolutional neural network was proposed. This model first used the extreme gradient boosting decision tree (XGboost) algorithm to sort the preprocessed data, and then it used comparison to weed out 55 features with a higher contribution. Then, the extracted features were fed into the improved one-dimensional convolutional neural network (I1DCNN), and this network training was used to complete the final classification task. The feature selection and improved one-dimensional convolutional neural network (FS-I1DCNN) intrusion detection model not only solved the traditional machine learning method of relying on expert experience to extract features but also improved the detection efficiency of the model, reduced the training time while reducing the dimension, and increased the overall accuracy. In comparison to the I1DCNN model without feature extraction and the conventional one-dimensional convolutional neural network (1DCNN) model, the experimental results demonstrate that the FS-I1DCNN model’s overall accuracy increases by 0.67% and 2.94%, respectively. Its accuracy, precision, recall, and F1-score were significantly better than those of the other intrusion detection models, including SVM and DBN.
由于机器学习在网络安全领域的广泛应用,入侵检测问题有了新的解决方案,但目前仍存在一些问题。传统的机器学习入侵检测技术依赖专家经验来选择特征,深度学习方法的检测效率较低。本文提出了一种基于特征选择和改进一维卷积神经网络的入侵检测模型。该模型首先使用极梯度提升决策树(XGboost)算法对预处理后的数据进行排序,然后使用比较法剔除贡献度较高的 55 个特征。然后,将提取的特征输入改进的一维卷积神经网络(I1DCNN),并利用该网络训练完成最终的分类任务。特征选择和改进的一维卷积神经网络(FS-I1DCNN)入侵检测模型不仅解决了传统机器学习方法中依靠专家经验提取特征的问题,还提高了模型的检测效率,在减少维数的同时缩短了训练时间,提高了整体准确率。实验结果表明,与未进行特征提取的 I1DCNN 模型和传统的一维卷积神经网络(1DCNN)模型相比,FS-I1DCNN 模型的总体准确率分别提高了 0.67% 和 2.94%。其准确率、精确度、召回率和 F1 分数都明显优于其他入侵检测模型,包括 SVM 和 DBN。
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引用次数: 0
Convex Combination for Wireless Localization Using Biased RSS Measurements 利用有偏差的 RSS 测量进行无线定位的凸面组合
IF 2.3 4区 计算机科学 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2023-12-20 DOI: 10.1155/2023/8931636
Qi Wang, Fei Li, Teng Shao, Chao Xu
Received signal strength- (RSS-) based localization in wireless sensor networks (WSNs) has attracted significant attention due to its advantages of low cost and simple implementation. In practice, RSS measurements may suffer from sensor biases, which deteriorates the localization accuracy. However, most of the existing localization methods are designed for bias-free measurements. In this paper, we propose a convex combination method for RSS localization in the presence of sensor biases. The parameter vector composed of unknown location and sensor biases is estimated simultaneously by using a convex combination of some virtual points. These virtual points form a convex hull, into which the parameter vector falls with large probability. By this, the original nonconvex estimation problem is converted to be convex. Numerical examples demonstrate the superiority of the proposed method in terms of localization accuracy, compared to the existing semidefinite programming (SDP) methods.
基于接收信号强度(RSS)的无线传感器网络(WSN)定位因其成本低、实施简单等优点而备受关注。实际上,RSS 测量可能会受到传感器偏差的影响,从而降低定位精度。然而,现有的定位方法大多是针对无偏差测量而设计的。本文提出了一种在存在传感器偏差的情况下进行 RSS 定位的凸组合方法。通过使用一些虚拟点的凸组合来同时估计由未知位置和传感器偏差组成的参数向量。这些虚拟点形成一个凸壳,参数矢量很有可能落入该凸壳中。这样,原来的非凸估计问题就转换成了凸估计问题。数值实例证明,与现有的半定量编程(SDP)方法相比,所提出的方法在定位精度方面更胜一筹。
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引用次数: 0
Research on Visual SLAM Navigation Techniques for Dynamic Environments 动态环境下视觉SLAM导航技术研究
IF 2.3 4区 计算机科学 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2023-09-01 DOI: 10.1155/2023/2025844
Tongjun Wang, Peijun Zhao
Synchronous positioning and mapping mainly realize the functions of self-positioning and environment map construction for intelligent navigation technology. In order to solve the problems of low positioning accuracy and poor mapping effect of existing SLAM (simultaneous localization and mapping) systems in indoor dynamic environments and to improve the positioning accuracy, timeliness, and robustness of visual SLAM systems in dynamic environments, an improved visual SLAM method is proposed. Aiming at the inconsistency between the direction of dynamic objects and static background optical flow, this method adopts a high-real-time dynamic region mask detection algorithm to eliminate the feature points in the dynamic region mask, remove the camera motion optical flow according to the original feature information, and then cluster the optical flow amplitude of dynamic objects so as to realize the dynamic region mask detection and eliminate the dynamic signpost points combined with the polar geometric constraints. In order to verify the effectiveness of the improved algorithm, the three evaluation indexes of system accuracy, real-time performance, and the amount of drift are analyzed and verified, respectively, on the TUM dataset. The results show that the proposed algorithm not only has good real-time performance but also improves the accuracy of the system and reduces the amount of drift.
同步定位测绘主要实现智能导航技术的自定位和环境图构建功能。为了解决现有SLAM系统在室内动态环境中定位精度低、映射效果差的问题,提高视觉SLAM系统的定位精度、及时性和鲁棒性,提出了一种改进的视觉SLAM方法。针对动态物体方向与静态背景光流不一致的问题,该方法采用高实时的动态区域掩模检测算法,去除动态区域掩膜中的特征点,根据原始特征信息去除相机运动光流,然后对动态物体的光流幅度进行聚类,实现动态区域掩模检测,并结合极几何约束消除动态路标点。为了验证改进算法的有效性,分别在TUM数据集上分析和验证了系统精度、实时性能和漂移量三个评价指标。结果表明,该算法不仅具有良好的实时性,而且提高了系统的精度,减少了漂移量。
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引用次数: 0
Improved Private Data Protection Scheme for Blockchain Smart Contracts 改进的区块链智能合约私有数据保护方案
IF 2.3 4区 计算机科学 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2023-08-24 DOI: 10.1155/2023/5963039
Sheng Hu
Data security and privacy protection are critical challenges that constrain the advancement of edge computing. Similarly, blockchain technology faces constraints in addressing security issues linked with edge computing due to its scalability limitations. To tackle these challenges and promote the development of blockchain technology, this paper presents a scheme that enhances privacy data protection in blockchain smart contracts using edge computing and a master-slave multichain architecture. Firstly, we propose a master-slave multichain architecture based on the traditional single chain and integrate it with a three-layer edge computing structure to address security issues on the edge side. We also design a signature authentication scheme utilizing ECC integrated with blockchain encryption technology. Secondly, we incorporate the role-based access control (RBAC) model with smart contracts to finely divide user privileges, construct an interdomain role-based access control (ID-RBAC) model, and provide detailed access authentication process designs for both within and between domains. Finally, experimental results demonstrate that our proposed scheme can effectively resist various attacks, significantly improve algorithm efficiency, and maintain a system overhead of less than 160 p, with a maximum transaction throughput of nearly 310 tx/s.
数据安全和隐私保护是制约边缘计算发展的关键挑战。同样,由于可扩展性的限制,区块链技术在解决与边缘计算相关的安全问题方面也面临限制。为了应对这些挑战,促进区块链技术的发展,本文提出了一种利用边缘计算和主从多链架构增强区块链智能合约中隐私数据保护的方案。首先,我们在传统单链的基础上提出主从多链架构,并将其与三层边缘计算结构相结合,解决边缘侧的安全问题。我们还设计了一个集成了区块链加密技术的ECC签名认证方案。其次,将基于角色的访问控制(RBAC)模型与智能合约相结合,精细划分用户权限,构建域间基于角色的访问控制(ID-RBAC)模型,详细设计域内和域间的访问认证流程。实验结果表明,该方案能够有效抵御各种攻击,显著提高算法效率,系统开销保持在160 p以内,最大事务吞吐量接近310 tx/s。
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引用次数: 0
Parameter Identification of Frame Structures by considering Shear Deformation 考虑剪切变形的框架结构参数辨识
IF 2.3 4区 计算机科学 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2023-08-16 DOI: 10.1155/2023/6631716
F. Xiao, Weiwei Zhu, Xiangwei Meng, Gang S. Chen
This paper presents a method to identify the damages in frame structures with slender beams. This method adjusts the parameters of the structure to match the analytical and the measured displacements. The effect of transverse shear deformation on the nodal analytical displacement is analyzed, and the parameter identification of frame structures with slender beams is performed. The results demonstrate that parameter-identification accuracy can be considerably improved by considering the transverse shear deformation in the frame structure with slender beams. The proposed method can accurately identify the damages in frame structures with slender beams using displacement measurements.
本文提出了一种识别细长梁框架结构损伤的方法。该方法调整结构的参数以匹配分析位移和测量位移。分析了横向剪切变形对节点解析位移的影响,并对细长梁框架结构进行了参数识别。结果表明,考虑细长梁框架结构的横向剪切变形,可以显著提高参数识别的精度。该方法可以通过位移测量准确识别细长梁框架结构的损伤。
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引用次数: 4
FSAS: An IoT-Based Security System for Crop Field Storage FSAS:基于物联网的作物田间存储安全系统
IF 2.3 4区 计算机科学 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2023-07-25 DOI: 10.1155/2023/2367167
Chandra Prakash, Anurag Barthwal, S. Avikal, Gyanendra Kumar Singh
Internet of Things abstracts the ability to remotely associate and observe things or objects over the Internet. When it comes to agriculture, this idea has been incorporated to make agriculture-related tasks smart, secure, and automated. Agriculture is vital for economic growth and also for the survival of humans. Farmers living in rural areas of India face a common problem of the theft of equipment like induction motors from small storage houses meant for storing commodities in crop fields. In this study, we present a remote security management framework for monitoring the crop field storage house, known as the farm security alert system (FSAS). FSAS is a small, energy efficient, low cost, and accurate security management system that uses microcontroller-based passive infrared (PIR) sensor and global system for mobile communication (GSM) module to generate an alert to the farm owner if there is an intrusion event at the crop field store. The microcontroller board utilized in the proposed model is the Arduino Uno, and PIR motion sensor is used to recognize the intruder. In addition, FSAS also can be used for monitoring of induction motor by utilizing a similar arrangement of sensors. The sensor signal is transmitted to the cloud whenever the intruder is within the sensing range of the sensor node. Naive Bayes’ prediction model is used to identify the level of encroachment as no (green), mild (yellow), or high (red) threat. The status and the alarms can be received by the farm owners, either on their smartphones as application alerts or as a short message/phone call, at any distance, and independent of whether their cell phones are connected to the Internet.
物联网抽象了通过互联网远程关联和观察事物或对象的能力。当涉及到农业时,这一想法已被纳入农业相关任务的智能,安全和自动化。农业对经济增长和人类的生存至关重要。生活在印度农村地区的农民面临着一个共同的问题,即用于储存农作物的小型仓库的感应电机等设备被盗。在这项研究中,我们提出了一个远程安全管理框架,用于监控农田仓库,被称为农场安全警报系统(FSAS)。FSAS是一种小型、节能、低成本和精确的安全管理系统,它使用基于微控制器的无源红外(PIR)传感器和全球移动通信系统(GSM)模块,在作物田间存储发生入侵事件时向农场所有者发出警报。所提出的模型使用的微控制器板是Arduino Uno,并使用PIR运动传感器来识别入侵者。此外,FSAS也可以用于监测感应电机利用类似的传感器安排。只要入侵者在传感器节点的感知范围内,传感器信号就被传输到云。朴素贝叶斯的预测模型被用来识别入侵的程度为无(绿色)、轻度(黄色)或高(红色)威胁。农场主人可以在任何距离通过智能手机接收状态和警报,无论是作为应用程序警报,还是作为短消息/电话,并且与他们的手机是否连接到互联网无关。
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引用次数: 1
Source Localization Using RSS Measurements with Sensor Position Uncertainty 利用传感器位置不确定的RSS测量进行源定位
IF 2.3 4区 计算机科学 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2023-07-17 DOI: 10.1155/2023/9274297
Qi Wang, Xianqing Li
Received signal strength- (RSS-) based localization has attracted considerable attention for its low cost and easy implementation. In plenty of existing work, sensor positions, which play an important role in source localization, are usually assumed perfectly known. Unfortunately, they are often subject to uncertainties, which directly leads to effect on localization result. To tackle this problem, we study the RSS-based source localization with sensor position uncertainty. Sensor position uncertainty will be modeled as two types: Gaussian random variable and unknown nonrandom variable. For either of the models, two semidefinite programming (SDP) methods are proposed, i.e., SDP-1 and SDP-2. The SDP-1 method proceeds from the nonconvex problem with respect to the maximum likelihood (ML) estimation and then obtains an SDP problem using proper approximation and relaxation. The SDP-2 method first transfers the sensor position uncertainties to the source position and then obtains an SDP formulation following a similar idea as in SDP-1 method. Numerical examples demonstrate the performance superiority of the proposed methods, compared to some existing methods assuming perfect sensor position information.
基于接收信号强度(RSS)的定位以其低成本和易于实现而引起了人们的广泛关注。在大量现有工作中,传感器位置在源定位中起着重要作用,通常被认为是完全已知的。遗憾的是,它们经常受到不确定性的影响,这直接导致对定位结果的影响。为了解决这个问题,我们研究了具有传感器位置不确定性的基于RSS的源定位。传感器位置不确定性将被建模为两种类型:高斯随机变量和未知非随机变量。对于任何一种模型,都提出了两种半定规划(SDP)方法,即SDP-1和SDP-2。SDP-1方法从关于最大似然(ML)估计的非凸问题开始,然后使用适当的近似和松弛来获得SDP问题。SDP-2方法首先将传感器位置不确定性转移到源位置,然后按照与SDP-1方法类似的思想获得SDP公式。数值算例表明,与假设传感器位置信息完美的现有方法相比,所提出的方法具有性能优势。
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引用次数: 0
Modeling and Performance Analysis of Flying Mesh Network 飞网状网络建模与性能分析
IF 2.3 4区 计算机科学 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2023-06-27 DOI: 10.1155/2023/8815835
Shenghong Qin, Renhui Xu, Laixian Peng, Xingchen Wei, Xiaohui Wu
Maintaining good connectivity is a major concern when constructing a robust flying mesh network, known as FlyMesh. In a FlyMesh, multiple unmanned aerial vehicles (UAVs) collaborate to provide continuous network service for mobile devices on the ground. To determine the connectivity probability of the aerial link between two UAVs, the Poisson point process (PPP) is used to describe the spatial distribution of UAVs equipped with omnidirectional antennas. However, the PPP fails to reflect the fact that there is a minimum distance restriction between two neighboring UAVs. In this paper, the β -Ginibre point process ( β -GPP) is adopted to model the spatial distribution of UAVs, with β representing the repulsion between nearby UAVs. Additionally, a large-scale fading method is used to model the route channel between UAVs equipped with directional antennas, allowing the monitoring of the impact of signal interference on network connectivity. Based on the β -GPP model, an analytical expression for the connectivity probability is derived. Numerical tests are conducted to demonstrate the effects of repulsion factor β , UAV intensity ρ , and beamwidth θ on network connectivity. The results indicate that an increase in UAV intensity decreases network connectivity when the repulsion factor β remains constant. These findings provide valuable insights for enhancing the service quality of the FlyMesh.
在构建称为FlyMesh的强大飞行网格网络时,保持良好的连接是一个主要问题。在FlyMesh中,多个无人机协同为地面移动设备提供连续的网络服务。为了确定两架无人机之间空中链路的连接概率,使用泊松点过程(PPP)来描述配备全向天线的无人机的空间分布。然而,PPP未能反映两个相邻无人机之间存在最小距离限制的事实。本文采用β-GPP对无人机的空间分布进行建模,其中β表示附近无人机之间的排斥。此外,还使用大规模衰落方法对配备定向天线的无人机之间的路由信道进行建模,从而监测信号干扰对网络连接的影响。基于β-GPP模型,导出了连通概率的解析表达式。进行了数值测试,以证明排斥因子β、无人机强度ρ和波束宽度θ对网络连通性的影响。结果表明,当排斥因子β保持不变时,无人机强度的增加会降低网络连通性。这些发现为提高FlyMesh的服务质量提供了宝贵的见解。
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引用次数: 0
Weak Fault Feature Extraction for Rolling Element Bearing Based on a Two-Stage Method 基于两阶段法的滚动轴承弱故障特征提取
IF 2.3 4区 计算机科学 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2023-06-21 DOI: 10.1155/2023/6671730
Lianhui Jia, Lijie Jiang, Yongliang Wen, Hongchao Wang
Timely and effective feature extraction is the key for fault diagnosis of rolling element bearing (REB). However, fault feature extraction will become very difficult in the early weak fault stage of REB due to the interference of strong background noise. To solve the above difficulty, a two-stage feature extraction method for early weak fault of REB is proposed, which mainly combines feature mode decomposition (FMD) with a blind deconvolution (BD) method. Firstly, based on the impulsiveness and cyclostationary characteristics of the vibration signal of faulty REB, FMD is used to decompose the complex original vibration signal into several modes containing single component. Subsequently, the sparse index (SI) is calculated for each mode, and the mode containing sensitive fault feature is selected for further analysis. Subsequently, apply the deconvolution method on the selected mode for further enhancing the impulsive characteristic. At last, traditional envelope spectrum (ES) analysis is applied on the filtered signal, and satisfactory fault features are extracted. Effectiveness and advantages of the proposed method are verified through experimental and engineering signals of REBs.
及时有效的特征提取是滚动轴承故障诊断的关键。然而,由于强背景噪声的干扰,在REB的早期弱断层阶段,断层特征提取将变得非常困难。针对上述困难,提出了一种以特征模态分解(FMD)与盲反卷积(BD)相结合的REB早期弱故障两阶段特征提取方法。首先,基于故障REB振动信号的冲动性和周期平稳性特点,利用FMD将复杂的原始振动信号分解为包含单分量的多个模态;然后,计算每个模态的稀疏指数(SI),选择包含敏感故障特征的模态进行进一步分析。然后,对选择的模式进行反卷积,进一步增强脉冲特性。最后,对滤波后的信号进行传统的包络谱分析,提取出满意的故障特征。通过实验和工程信号验证了该方法的有效性和优越性。
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引用次数: 0
Collaborative Energy Optimization of Multiple Chargers Based on Node Collaborative Scheduling 基于节点协同调度的多充电器协同能量优化
IF 2.3 4区 计算机科学 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2023-06-05 DOI: 10.1155/2023/5092972
Minghua Wang, Yingcong Zeng, Jiaqing Li, Yan Wang
Wireless rechargeable sensor network (WRSN) uses mobile chargers (MCs) to charge sensor nodes wirelessly to solve the energy problems faced by traditional wireless sensor network. In WRSN, mobile charging schemes with multiple MCs supplementing energy are quite common. How to properly plan the MC’s moving path to reduce the charge energy loss and deploy nodes to improve network coverage rate has become a huge research challenge. In this paper, a collaborative energy optimization algorithm (CEOA) is proposed for multiple chargers based on k-mean++ and node collaborative scheduling. The CEOA combines internal energy optimization and external device power supply, effectively prolongs network lifetime, and improves network coverage rate. It uses the k-mean++ to cluster nodes in the network; then, the nodes in the network are scheduled to sleep based on the confident information coverage (CIC) model. Finally, the CEOA uses a main mobile charger to carry multiple auxiliary mobile chargers to charge all the nodes in the cluster. Simulation results show that the proposed algorithm increases the network lifetime by more than 8 times and the coverage rate by about 20%.
无线可充电传感器网络(WRSN)使用移动充电器(MC)对传感器节点进行无线充电,以解决传统无线传感器网络面临的能源问题。在WRSN中,具有多个MC补充能量的移动充电方案非常常见。如何正确规划MC的移动路径以减少电荷能量损失,并部署节点以提高网络覆盖率,已成为一个巨大的研究挑战。本文提出了一种基于k-均值++和节点协同调度的多充电器协同能量优化算法(CEOA)。CEOA结合了内部能源优化和外部设备供电,有效延长了网络寿命,提高了网络覆盖率。它使用k-均值++对网络中的节点进行聚类;然后,基于置信信息覆盖(CIC)模型来调度网络中的节点休眠。最后,CEOA使用一个主移动充电器携带多个辅助移动充电器为集群中的所有节点充电。仿真结果表明,该算法将网络寿命提高了8倍以上,覆盖率提高了20%左右。
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引用次数: 0
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International Journal of Distributed Sensor Networks
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