ICT Express最新文献

英文中文

Hybrid Approach with Membership-Density Based Oversampling for handling multi-class imbalance in Internet Traffic Identification with overlapping and noise 基于成员密度超采样的混合方法，用于处理具有重叠和噪声的互联网流量识别中的多类不平衡问题

IF 4.1 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

ICT Express

Pub Date : 2024-10-01 DOI: 10.1016/j.icte.2024.04.007

Internet Traffic identification is a crucial method for monitoring Internet application activities and is essential for Internet management and security. Internet traffic data typically displays imbalanced distributions. The uneven distribution of instances in each class indicates the class imbalance problem. This problem can cause a decrease in classification performance because the classifier assumes the dataset has a balanced class distribution. Internet Traffic Identification dataset is often accompanied by overlapping and noise. The hybrid approach to handling class imbalances involving data-level and ensemble-based approaches is usually chosen to overcome this problem. Data-level with oversampling using SMOTE is the choice because of its ability to synthesize new samples for minority classes. However, SMOTE-generated samples tend to be noisy and overlap with the majority of samples. This research proposes the application of a Hybrid Approach with Membership-density-based Oversampling to tackle this challenge. This research emphasizes the importance of applying membership degrees in determining samples that will group samples into safe, overlapping, and noisy areas. Then, top samples will be selected based on density ratio, stability, and score for safe and overlapping safe areas. The study findings that the proposed method effectively addresses multi-class imbalances in six Internet Traffic Identification datasets, yielding slightly improved average accuracy,

F_{b} M e a s u r,

and class balance accuracy results compared to other testing methods, though the difference is not statistically significant. The noise and overlapping scenes experiments demonstrate that the average accuracy obtained is superior, showing a considerable difference compared to all test methods.

互联网流量识别是监控互联网应用活动的重要方法，对互联网管理和安全至关重要。互联网流量数据通常呈现不平衡分布。每个类别中实例的不均匀分布表明存在类别不平衡问题。这个问题会导致分类性能下降，因为分类器假定数据集具有均衡的类分布。互联网流量识别数据集往往伴随着重叠和噪声。处理类不平衡的混合方法通常包括数据级方法和基于集合的方法，以克服这一问题。选择使用 SMOTE 进行超采样的数据级方法，是因为它能为少数类别合成新样本。然而，SMOTE 生成的样本往往存在噪声，并与大多数样本重叠。本研究提出了一种基于成员密度的过度采样混合方法来应对这一挑战。本研究强调在确定样本时应用成员度的重要性，这将把样本归类为安全、重叠和嘈杂区域。然后，将根据密度比、稳定性以及安全区域和重叠安全区域的得分来选择顶级样本。研究发现，所提出的方法能有效解决六个互联网流量识别数据集中的多类不平衡问题，与其他测试方法相比，平均准确率、FbMeasur 和类平衡准确率略有提高，但差异在统计学上并不显著。噪声和重叠场景实验表明，所获得的平均准确率更胜一筹，与所有测试方法相比都有相当大的差异。

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引用次数: 0

Deep neural network and trust management approach to secure smart transportation data in sustainable smart cities 深度神经网络和信任管理方法确保可持续智慧城市中智能交通数据的安全

IF 4.1 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

ICT Express

Pub Date : 2024-10-01 DOI: 10.1016/j.icte.2024.08.006

Smart transportation, powered by IoT, transforms mobility with interconnected sensors and devices collecting real-time data on traffic, vehicle locations, and passenger needs. This fosters a safer and more sustainable transportation ecosystem, optimizing traffic flow and enhancing public transit efficiency. However, security and privacy challenges emerge in smart transportation systems. Our proposed solution involves a deep neural network (DNN) model trained on extensive datasets from sustainable cities, incorporating historical information like traffic patterns and sensor readings. This model identifies potential malicious nodes, achieving a 90% accuracy rate in predicting threats such as Denial of Service 88%, Whitewash attacks 80%, and Brute Force attacks 75%. This high precision ensures the security and privacy of passenger vehicle data and routes.

由物联网驱动的智能交通通过互联传感器和设备收集有关交通、车辆位置和乘客需求的实时数据，改变了交通方式。这促进了更安全、更可持续的交通生态系统，优化了交通流量，提高了公共交通效率。然而，智能交通系统也面临着安全和隐私方面的挑战。我们提出的解决方案涉及一个深度神经网络（DNN）模型，该模型在来自可持续发展城市的大量数据集上进行训练，并结合了交通模式和传感器读数等历史信息。该模型可识别潜在的恶意节点，在预测拒绝服务 88%、洗白攻击 80% 和暴力攻击 75% 等威胁方面的准确率高达 90%。这种高精确度确保了客运车辆数据和路线的安全性和隐私性。

引用次数: 0

The digital pheromone: Building digital identity of smartphone users based on time-varying multivariates 数字信息素：基于时变多变量构建智能手机用户的数字身份

IF 4.1 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

ICT Express

Pub Date : 2024-10-01 DOI: 10.1016/j.icte.2024.07.008

The increasing reliance on smartphones necessitates secure, accurate user identification methods. This study introduces “digital pheromones”, a novel digital identity creation method based on smartphone user behaviors. Represented by a 56-bit binary number, this identity is derived from quantization and concatenation of 14 time-varying smartphone variables. Motivated by the need for enhanced security and privacy, the study employs statistical analysis to measure user behavior dispersion, showing that wider dispersion variables facilitate faster user uniqueness at lower quantization levels. The results demonstrate 100% uniqueness, ensuring no two users share the same 56-bit identity. This research offers a robust framework for future advancements in smartphone user authentication and identification systems.

随着人们对智能手机的依赖程度越来越高，有必要采用安全、准确的用户身份识别方法。本研究介绍了一种基于智能手机用户行为的新型数字身份创建方法--"数字信息素"。该身份由一个 56 位二进制数表示，由 14 个随时间变化的智能手机变量量化和串联而成。出于增强安全性和隐私性的需要，这项研究采用统计分析来测量用户行为的离散性，结果表明，在较低的量化水平下，较宽的离散变量有助于更快地实现用户唯一性。研究结果显示了 100% 的唯一性，确保没有两个用户共享相同的 56 位身份。这项研究为智能手机用户身份验证和识别系统的未来发展提供了一个强大的框架。

引用次数: 0

Symbol-level precoding scheme robust to channel estimation errors in wireless fading channels 对无线衰减信道中的信道估计误差具有鲁棒性的符号级预编码方案

IF 4.1 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

ICT Express

Pub Date : 2024-10-01 DOI: 10.1016/j.icte.2024.03.006

Most studies on symbol-level (SL) precoding have assumed that channel estimation is perfect. However, because interference signals or additive white Gaussian noise exist in the received signal for channel estimation, channel estimation errors always exist. In this paper, we propose an SL precoding scheme that is robust to channel estimation errors. First, using the characteristics of the channel estimation errors, we derive an equation for the worst-case mean squared error (MSE) which is the maximum of the MSE. Then by designing the SL precoding to minimize the worst-case MSE, it has robust characteristics against channel estimation errors.

大多数关于符号级（SL）预编码的研究都假设信道估计是完美的。然而，由于用于信道估计的接收信号中存在干扰信号或加性白高斯噪声，信道估计误差始终存在。本文提出了一种对信道估计误差具有鲁棒性的 SL 预编码方案。首先，我们利用信道估计误差的特点，推导出最坏情况下的均方误差（MSE）方程，即 MSE 的最大值。然后，通过设计 SL 预编码来最小化最坏情况下的 MSE，从而使其对信道估计误差具有稳健特性。

引用次数: 0

Integrated beamforming and trajectory optimization algorithm for RIS-assisted UAV system RIS 辅助无人机系统的波束成形和轨迹优化综合算法

IF 4.1 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

ICT Express

Pub Date : 2024-10-01 DOI: 10.1016/j.icte.2024.04.008

Unmanned aerial vehicles (UAVs) and reconfigurable intelligent surfaces (RISs) have garnered considerable research interest in the fields of 5G and 6G wireless communication due to their remarkable flexibility and cost-effectiveness. However, the inherent openness of wireless communication environments renders these technologies vulnerable to eavesdropping. This paper presents a penalty-based successive convex approximation algorithm and a minorize–maximization algorithm to optimize the transmission beamforming vector, RIS beamforming vector, and UAV–RIS trajectory. The objective of this study was to enhance the physical layer security performance of wireless communication systems using UAVs and RISs. Our simulation results demonstrate that the proposed technique achieves a higher security transmission rate compared to existing techniques.

无人飞行器（UAV）和可重构智能表面（RIS）因其出色的灵活性和成本效益，在 5G 和 6G 无线通信领域引起了广泛的研究兴趣。然而，无线通信环境固有的开放性使这些技术容易受到窃听。本文提出了一种基于惩罚的连续凸近似算法和最小化最大化算法来优化传输波束成形向量、RIS 波束成形向量和无人机-RIS 轨迹。这项研究的目的是提高使用无人机和 RIS 的无线通信系统的物理层安全性能。我们的模拟结果表明，与现有技术相比，建议的技术实现了更高的安全传输速率。

引用次数: 0

A hybrid approach of ConvLSTMBNN-DT and GPT-4 for real-time anomaly detection decision support in edge–cloud environments 用于边缘云环境中实时异常检测决策支持的 ConvLSTMBNN-DT 和 GPT-4 混合方法

IF 4.1 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

ICT Express

Pub Date : 2024-10-01 DOI: 10.1016/j.icte.2024.07.007

Anomaly detection is a critical requirement across diverse domains to promptly identify abnormal behavior. Conventional approaches often face limitations with uninterpretable anomaly detection results, impeding efficient decision-making processes. This paper introduces a novel hybrid approach, the convolutional LSTM Bayesian neural network with nonparametric dynamic thresholding (ConvLSTMBNN-DT) for prediction-based anomaly detection. In addition, the model integrates fine-tuned generative pre-training version 4 (GPT-4) to provide human-interpretable explanations in edge–cloud environments. The proposed method demonstrates exceptional performance, achieving an average

F 1 - s c o r e

of 0.91 and an area under the receiver operating characteristic curve (

A U C

) of 0.86. Additionally, it effectively offers comprehensible decision-support explanations.

异常检测是不同领域及时识别异常行为的关键要求。传统方法往往面临无法解读异常检测结果的限制，阻碍了高效的决策过程。本文介绍了一种新颖的混合方法，即具有非参数动态阈值的卷积 LSTM 贝叶斯神经网络（ConvLSTMBNN-DT），用于基于预测的异常检测。此外，该模型还集成了微调生成预训练第 4 版（GPT-4），以便在边缘云环境中提供人类可理解的解释。所提出的方法表现出卓越的性能，平均 F1 分数达到 0.91，接收器工作特征曲线下面积 (AUC) 为 0.86。此外，它还有效地提供了可理解的决策支持解释。

引用次数: 0

Edge computing in future wireless networks: A comprehensive evaluation and vision for 6G and beyond 未来无线网络中的边缘计算：对 6G 及其后的全面评估和展望

IF 4.1 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

ICT Express

Pub Date : 2024-10-01 DOI: 10.1016/j.icte.2024.08.007

Future internet aims to function as a neutral in-network storage and computation platform, essential for enabling 6G and beyond wireless use cases. Information-Centric Networking and Edge Computing are key paradigms driving this vision by offering diversified services with fast response times across heterogeneous networks. This approach requires effective coordination to dynamically utilize resources like links, storage, and computation in near real-time within a non-homogenous and distributed computing environment. Additionally, networks must be aware of resource availability and reputational information to manage unknown and partially observed dynamic systems, ensuring the desired Quality of Experience (QoE). This paper provides a comprehensive evaluation of edge computing technologies, starting with an introduction to its architectural frameworks. We examine contemporary research on essential aspects such as resource allocation, computation delegation, data administration, and network management, highlighting existing research gaps. Furthermore, we explore the synergy between edge computing and 5G, and discuss advancements in 6G that enhance solutions through edge computing. Our study emphasizes the importance of integrating edge computing in future considerations, particularly regarding sustainable energy and standards.

未来互联网的目标是成为一个中立的网内存储和计算平台，这对实现 6G 及更高版本的无线用例至关重要。以信息为中心的网络和边缘计算是推动这一愿景的关键范式，可在异构网络中提供快速响应的多样化服务。这种方法需要有效的协调，以便在非同质和分布式计算环境中近乎实时地动态利用链路、存储和计算等资源。此外，网络还必须了解资源可用性和声誉信息，以管理未知和部分观测到的动态系统，确保所需的体验质量（QoE）。本文全面评估了边缘计算技术，首先介绍了其架构框架。我们考察了资源分配、计算授权、数据管理和网络管理等重要方面的当代研究，并强调了现有的研究空白。此外，我们还探讨了边缘计算与 5G 之间的协同作用，并讨论了通过边缘计算增强解决方案的 6G 进展。我们的研究强调了将边缘计算纳入未来考虑的重要性，特别是在可持续能源和标准方面。

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引用次数: 0

Non-orthogonal resource scheduling with enhanced preamble detection method for cellular random access networks 蜂窝随机接入网络的非正交资源调度与增强型前导码检测方法

IF 4.1 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

ICT Express

Pub Date : 2024-10-01 DOI: 10.1016/j.icte.2024.08.005

In this article, we propose a non-orthogonal resource scheduling (NORS) scheme with an enhanced preamble detection (PD) method for cellular random access (RA) systems. The enhanced PD method is designed based on a deep learning technique that can classify three PD statuses: idle, collision-free, and collision. Because of this collision resolution capability, the base station (BS) can exploit radio resources non-orthogonally in contention-based RA protocols. In addition, we mathematically analyze the performance of our proposed NORS scheme in terms of RA success probability and resource utilization efficiency and conduct a comparison with the baseline orthogonal resource scheduling (ORS) scheme. Through simulations, we verify the validity of our mathematical analysis. As a result, our results will be a useful guideline for resource scheduling and provide support for massive connectivity.

在本文中，我们为蜂窝随机接入（RA）系统提出了一种具有增强型前导码检测（PD）方法的非正交资源调度（NORS）方案。增强型前导码检测方法是基于深度学习技术设计的，可对三种前导码状态进行分类：空闲、无碰撞和碰撞。由于这种碰撞解决能力，基站（BS）可以在基于竞争的 RA 协议中非正交地利用无线电资源。此外，我们还从 RA 成功概率和资源利用效率的角度对我们提出的 NORS 方案的性能进行了数学分析，并与基线正交资源调度（ORS）方案进行了比较。通过仿真，我们验证了数学分析的有效性。因此，我们的结果将为资源调度提供有用的指导，并为大规模连接提供支持。

引用次数: 0

Performance analysis of multi-hop low earth orbit satellite network over mixed RF/FSO links 通过射频/FSO 混合链路的多跳低地球轨道卫星网络性能分析

IF 4.1 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

ICT Express

Pub Date : 2024-10-01 DOI: 10.1016/j.icte.2024.03.004

Low earth orbit satellite (LEOS) communication has emerged as a promising technology to achieve global coverage, while further reducing the end-to-end latency by establishing communication link between LEOSs. In this paper, we analyze the performance of multi-hop LEOS network in terms of bit error rate (BER) and outage probability over mixed radio frequency (RF)/free space optical (FSO) links where RF link is used for the link between ground terminal and LEOS and FSO link is used for establishing inter-satellite links (ISLs). The derived closed-form results are confirmed via Monte Carlo simulations and we show the effect of the number of satellite hops on outage probability and average BER performance by comparing scenarios both with and without ISLs.

低地球轨道卫星（LEOS）通信已成为实现全球覆盖的一项前景广阔的技术，同时通过在低地球轨道卫星之间建立通信链路进一步减少了端到端延迟。本文分析了多跳 LEOS 网络在射频/自由空间光学（FSO）混合链路上的误码率（BER）和中断概率性能，其中射频链路用于地面终端和 LEOS 之间的链路，FSO 链路用于建立卫星间链路（ISL）。我们通过蒙特卡罗模拟证实了得出的闭式结果，并通过比较有 ISL 和无 ISL 的情况，展示了卫星跳数对中断概率和平均误码率性能的影响。

引用次数: 0

DC-Net: A Distant-range Content Interaction Network for Radio Map construction DC-Net：用于无线电地图构建的远距离内容交互网络

IF 4.1 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

ICT Express

Pub Date : 2024-10-01 DOI: 10.1016/j.icte.2024.08.008

The Radio Map (RM) stands as a powerful tool in wireless communication and radio regulation. Currently, several RM construction models based on Convolutional Neural Networks (CNNs) have been proposed. However, due to the inherent limitations of vanilla convolutions, capturing distant-range information of the radio propagation environment becomes challenge. Therefore, this letter introduces a novel RM construction model called Distant-range Content interaction Network (DC-Net) and validates its effectiveness using the RadioMapSeer dataset. Experiment results reveal that DC-Net achieves state-of-the-art performance in mapping accuracy for both known and unknown transmitter locations in RM construction.

无线电地图（RM）是无线通信和无线电管理的有力工具。目前，已经提出了几种基于卷积神经网络（CNN）的无线电地图构建模型。然而，由于香草卷积的固有局限性，捕捉无线电传播环境的远距离信息成为难题。因此，本文介绍了一种名为 "远距离内容交互网络（DC-Net）"的新型 RM 构建模型，并使用 RadioMapSeer 数据集验证了其有效性。实验结果表明，在 RM 构建过程中，DC-Net 对已知和未知发射机位置的映射精度都达到了最先进的水平。

引用次数: 0

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