Computers & Electrical Engineering最新文献_第8页

Deep learning models security: A systematic review 深度学习模型的安全性：系统回顾

IF 4 3区计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE

Computers & Electrical Engineering

Pub Date : 2024-10-20 DOI: 10.1016/j.compeleceng.2024.109792

Twinkle Tyagi, Amit Kumar Singh

Deep learning models and the digital records they generate have remarkably increased their adoption of many practical applications. While the success of deep learning in multimedia applications, especially images, helps tackle some of the most challenging problems, one of its copyright violations, ownership conflict, poses a grave concern for many potential applications. Many works on intellectual property protection for such models have proposed to verify ownership. Therefore, it is necessary to conduct a comprehensive study on the security of deep learning models to evaluate their strong background, state-of-the-art solutions, possible attacks, current limitations and notable improvements. This survey attempts to systematically discuss and summarise the recent advanced security solutions for deep learning models through watermarking, encryption and fingerprinting. Our study explores the recent applications, possible attacks, current limitations and notable suggestions regarding deep learning. It also comprehensively evaluates the recent research gaps and opportunities in detail to empower researchers and practitioners to provide additional secure solutions for deep learning models. This extensive survey is the first to consider model security through several notable techniques.

深度学习模型及其生成的数字记录显著提高了它们在许多实际应用中的采用率。虽然深度学习在多媒体应用（尤其是图像）方面的成功有助于解决一些最具挑战性的问题，但其侵犯版权的问题之一--所有权冲突--对许多潜在应用构成了严重威胁。许多针对此类模型的知识产权保护工作都提出了验证所有权的建议。因此，有必要对深度学习模型的安全性进行全面研究，以评估其强大的背景、最先进的解决方案、可能的攻击、当前的局限性和显著的改进。本研究试图通过水印、加密和指纹识别等方法，系统地讨论和总结近期针对深度学习模型的先进安全解决方案。我们的研究探讨了有关深度学习的最新应用、可能的攻击、当前的局限性和值得注意的建议。它还详细全面地评估了最近的研究差距和机遇，以增强研究人员和从业人员为深度学习模型提供更多安全解决方案的能力。这项广泛的调查是首次通过几种著名的技术来考虑模型的安全性。

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引用次数: 0

Mango leaf disease diagnosis using Total Variation Filter Based Variational Mode Decomposition 利用基于总变异滤波器的变模分解诊断芒果叶病

IF 4 3区计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE

Computers & Electrical Engineering

Pub Date : 2024-10-20 DOI: 10.1016/j.compeleceng.2024.109795

Rajneesh Kumar Patel , Ankit Choudhary , Siddharth Singh Chouhan , Krishna Kumar Pandey

Mango leaf diseases significantly threaten mango cultivation, impacting both yield and quality. Accurate and early diagnosis is essential for effectively managing and controlling these diseases. This study introduces a novel approach for diagnosing mango leaf diseases, leveraging Total Variation Filter-based Variational Mode Decomposition. The proposed method enhances the extraction of disease-specific features from leaf images by decomposing them into intrinsic mode functions while simultaneously reducing noise and preserving important edge information. Experimental results demonstrate that the proposed method effectively isolates relevant patterns associated with various mango leaf diseases, improving diagnostic accuracy compared to traditional methods. Deep learning models, DenseNet121 and VGG-19, are used for feature extraction from sub-band images, and extracted features are concatenated and fed to Random Forest for classification. Utilizing tenfold cross-validation, our model demonstrated enhanced classification accuracy (98.85 %), specificity (99.37 %), and sensitivity (98.0 %) in detecting diseases from Mango leaf images. Feature maps and Gradient-weighted Class Activation Mapping analysis was conducted to visualize and scrutinize the essential regions crucial for accurate predictions. Statistical analysis indicates that our proposed architecture outperforms pre-trained models and existing mango leaf disease detection methods. This diagnostic approach can be a rapid disease detection tool for imaging specialists utilizing leaf images. The robustness and efficiency of the presented work in handling complex and noisy image data make it a promising tool for automated agricultural disease diagnosis systems, facilitating timely and precise interventions in mango orchards.

芒果叶病严重威胁芒果种植，影响产量和质量。准确和早期诊断对于有效管理和控制这些病害至关重要。本研究利用基于总变异滤波器的变异模式分解技术，提出了一种诊断芒果叶病的新方法。所提出的方法通过将叶片图像分解为固有模式函数，增强了从叶片图像中提取特定疾病特征的能力，同时还能减少噪声并保留重要的边缘信息。实验结果表明，与传统方法相比，所提出的方法能有效分离出与各种芒果叶疾病相关的模式，提高了诊断的准确性。深度学习模型 DenseNet121 和 VGG-19 用于从子波段图像中提取特征，提取的特征经串联后输入随机森林进行分类。通过十倍交叉验证，我们的模型在从芒果叶图像中检测疾病方面表现出更高的分类准确性（98.85 %）、特异性（99.37 %）和灵敏度（98.0 %）。通过对特征图和梯度加权类激活图谱进行分析，对准确预测的关键区域进行了可视化和仔细检查。统计分析表明，我们提出的架构优于预训练模型和现有的芒果叶疾病检测方法。这种诊断方法可以成为成像专家利用叶片图像快速检测疾病的工具。本成果在处理复杂和高噪声图像数据方面的稳健性和高效性使其成为农业疾病自动诊断系统的理想工具，有助于对芒果园进行及时和精确的干预。

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引用次数: 0

Optimizing energy hub systems: A comprehensive analysis of integration, efficiency, and sustainability 优化能源枢纽系统：对整合、效率和可持续性的全面分析

IF 4 3区计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE

Computers & Electrical Engineering

Pub Date : 2024-10-19 DOI: 10.1016/j.compeleceng.2024.109779

Lei Xu

This study introduces a novel application of modified particle swarm optimization (PSO) for optimizing multi-energy hub systems (EHSs) to enhance efficiency and sustainability. The proposed method leverages PSO to optimize the scheduling of various energy resources, including gas turbines, biomass units, and renewable sources such as solar and wind power. Unlike traditional optimization approaches that rely on genetic algorithm (GA) and complex encoding schemes, the PSO algorithm simplifies the process using real-valued vectors and direct communication within the swarm, which significantly reduces implementation complexity. Key contributions of this work include the development of a tailored PSO algorithm that integrates seamlessly with the multi-objective optimization of EHSs. The algorithm simultaneously targets a reduction in operational costs and carbon emissions, offering a comprehensive solution for energy hub design. The proposed PSO approach has demonstrated a 10.35 % reduction in operating costs and an 85.03 % decrease in CO2 emissions compared to traditional baseline setups. In comparative analysis, the integration of renewable sources using the PSO algorithm resulted in a 77.91 % reduction in total CO2 emissions and an 85.61 % decrease in operating costs, showcasing its effectiveness in advancing both economic and environmental objectives. Furthermore, the study provides a detailed evaluation of various scenarios, revealing that the PSO-optimized EHS configuration achieves a significant reduction in reliance on non-renewable energy sources (RES). For instance, the incorporation of photovoltaics and wind turbines in the EHS setup led to a 46.39 % increase in energy sold to the grid and a 26.82 % decrease in electricity purchased from external sources. These quantitative results underscore the robustness and practical benefits of the proposed PSO method in designing and optimizing energy systems for improved sustainability and cost-effectiveness.

本研究介绍了改进型粒子群优化（PSO）在优化多能源枢纽系统（EHS）中的新应用，以提高效率和可持续性。所提出的方法利用 PSO 来优化各种能源资源的调度，包括燃气轮机、生物质机组以及太阳能和风能等可再生能源。与依赖遗传算法（GA）和复杂编码方案的传统优化方法不同，PSO 算法使用实值向量和蜂群内的直接通信简化了过程，从而大大降低了实施的复杂性。这项工作的主要贡献包括开发了一种量身定制的 PSO 算法，可与 EHS 的多目标优化无缝集成。该算法同时以降低运营成本和碳排放为目标，为能源枢纽设计提供了全面的解决方案。与传统的基线设置相比，所提出的 PSO 方法已证明运营成本降低了 10.35%，二氧化碳排放量减少了 85.03%。在比较分析中，使用 PSO 算法整合可再生能源后，二氧化碳排放总量减少了 77.91%，运营成本降低了 85.61%，这表明该算法在实现经济和环境目标方面都很有效。此外，研究还对各种方案进行了详细评估，结果表明 PSO 优化的 EHS 配置可显著减少对不可再生能源（RES）的依赖。例如，将光伏发电和风力涡轮机纳入 EHS 设置后，向电网出售的能源增加了 46.39%，从外部购买的电力减少了 26.82%。这些定量结果凸显了所提出的 PSO 方法在设计和优化能源系统以提高可持续性和成本效益方面的稳健性和实用性。

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引用次数: 0

An enhanced diffusion-based network for efficient stamp removal 基于扩散的增强型网络可高效清除印章

IF 4 3区计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE

Computers & Electrical Engineering

Pub Date : 2024-10-19 DOI: 10.1016/j.compeleceng.2024.109738

Guohao Cui, Cihui Yang

Current diffusion models excel in computer vision tasks, but stamp removal from documents remains challenging, especially when stamps are light-colored and blend with text. Existing methods struggle to preserve background text and rely heavily on the training set, excelling in either text or table stamp removal, but not both. To address these problems, we propose an enhanced diffusion-based stamp removal model using a Spatial Attention Mechanism and a Simulate Rectified Linear Unit. Spatial Attention Mechanism combines the spatial transformation capabilities of the Spatial Transformer Network with the feature extraction of the Convolutional Block Attention Module for higher-quality images. Simulate Rectified Linear Unit mimics neuronal signal transmission in the human brain, enhancing feature extraction. Our diffusion model achieved a PSNR of 44.7, SSIM of 0.99, and RMSE of 3.47 on the stamp dataset, and performed optimally on the denoising-dirty-documents, CLWD, and DIBCO 2017 datasets. It also attained the highest PSNR of 26.8 on the DIBCO 2013 dataset, with other metrics close to the best. Code is available at https://github.com/GuohaoCui/DiffusionModel.

当前的扩散模型在计算机视觉任务中表现出色，但从文件中去除印章仍是一项挑战，尤其是当印章颜色较浅并与文本混合时。现有的方法难以保留背景文本，而且严重依赖训练集，在文本或表格图章去除方面表现出色，但无法同时去除两者。为了解决这些问题，我们提出了一种基于扩散的增强型图章去除模型，该模型采用了空间注意机制和模拟整流线性单元。空间注意机制结合了空间变换器网络的空间变换功能和卷积块注意模块的特征提取功能，可获得更高质量的图像。模拟整流线性单元模拟人脑中的神经元信号传输，增强了特征提取能力。我们的扩散模型在邮票数据集上的 PSNR 为 44.7，SSIM 为 0.99，RMSE 为 3.47，在去噪-脏文档、CLWD 和 DIBCO 2017 数据集上的表现最佳。它在 DIBCO 2013 数据集上的 PSNR 也达到了最高的 26.8，其他指标也接近最佳。代码见 https://github.com/GuohaoCui/DiffusionModel。

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引用次数: 0

PSO-based Quaternion Fourier Transform steganography: Enhancing imperceptibility and robustness through multi-dimensional frequency embedding 基于 PSO 的四元数傅立叶变换隐写术：通过多维频率嵌入提高不可感知性和鲁棒性

IF 4 3区计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE

Computers & Electrical Engineering

Pub Date : 2024-10-19 DOI: 10.1016/j.compeleceng.2024.109787

Parsa Parsafar

This paper presents a novel steganography technique using the Quaternion Fourier Transform (QFT) in the 4D frequency domain to enhance imperceptibility and robustness in digital image embedding. Steganography, the art of hiding information within media, faces challenges in balancing security, imperceptibility, and robustness. To address this, we leverage the multi-dimensional properties of quaternions, enabling the embedding of secret data in both grayscale and color images. For grayscale images, two quaternion dimensions are utilized for intensity and secret data, while for color images, all four dimensions are employed with one reserved for metadata. The research question centers on how to maximize spatial dispersion and color similarity while maintaining high imperceptibility and robustness against attacks. Experimental results show that the proposed method improves visual imperceptibility by more than 4 % and exhibits a 13 % increase in robustness against common steganalysis attacks compared to the best state-of-the-art existing technique. These advancements highlight the potential of this method for applications in secure communication, digital watermarking, and copyright protection. By combining the quaternion mathematical framework with a novel optimization strategy, this approach significantly improves upon traditional steganography methods.

本文提出了一种新颖的隐写技术，利用四维频域中的四元数傅里叶变换（QFT）来增强数字图像嵌入的不可感知性和稳健性。隐写术是一种在媒体中隐藏信息的艺术，它面临着平衡安全性、不可感知性和稳健性的挑战。为此，我们利用四元数的多维特性，在灰度和彩色图像中嵌入秘密数据。对于灰度图像，强度和秘密数据使用两个四元数维度，而对于彩色图像，则使用所有四个维度，并为元数据保留一个维度。研究问题的核心是如何最大限度地提高空间分散性和色彩相似性，同时保持较高的不可感知性和抗击攻击的鲁棒性。实验结果表明，与最先进的现有技术相比，所提出的方法将视觉不可感知性提高了 4% 以上，对常见隐写分析攻击的鲁棒性提高了 13%。这些进步凸显了该方法在安全通信、数字水印和版权保护方面的应用潜力。通过将四元数学框架与新颖的优化策略相结合，这种方法大大改进了传统的隐写方法。

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引用次数: 0

Nonlinear integral backstepping control based on particle swarm optimization for a grid-connected variable wind energy conversion system during voltage dips 基于粒子群优化的非线性积分反步控制，用于电压骤降期间的并网可变风能转换系统

IF 4 3区计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE

Computers & Electrical Engineering

Pub Date : 2024-10-18 DOI: 10.1016/j.compeleceng.2024.109790

Elmostafa Chetouani , Youssef Errami , Abdellatif Obbadi , Smail Sahnoun , Elhadi Baghaz , Hamid Chojaa , Said Mahfoud

Double-fed induction generator (DFIG) wind turbines connected to the grid are particularly subject to grid problems such as voltage dips. Because of this, it might be challenging to maintain system stability and prevent system disconnections when using a Proportional-Integral (PI) Controller to operate this system. This paper applies the Integral Backstepping Control for the wind power plant system connected to the power grid. The Integral Backstepping is a nonlinear and recursive method that employs the Lyapunov theory to ensure the system's stability. The best selection of gain values for the Lyapunov function guarantees improved system control. These gains are frequently adjusted using the trial-and-error strategy, which is time-consuming and inefficient. This technique becomes more complex when many parameters need to be determined. It also limits the system's performance and restricts this nonlinear approach's advantages. The objective is to apply the particle swarm optimization for computing several constant values of the nonlinear approach by minimizing an integral absolute error criterion index. The weighted sum of errors is employed to solve the multiple objective problems. The proposed controller tracks the maximum power point, maintains the voltage of the DC-Link constant, and controls active and reactive power. The robustness of this method is verified in critical conditions, including system parameter variation and asymmetrical and symmetrical grid faults. The simulation findings highlight the effectiveness and robustness of the combination of Integral Backstepping with Particle Swarm Optimization in terms of reducing response time from 10.6 (ms) to 2 (ms), canceling static error, and improving overshoot compared to the vector control based on PI regulator. Besides, the DC-Link voltage ripples during the asymmetrical grid faults are reduced to ±1 (V) using the suggested controller. The latter can be implemented thanks to advancements in Central Processor Unit technology.

与电网相连的双馈异步发电机（DFIG）风力涡轮机尤其容易受到电压骤降等电网问题的影响。因此，在使用比例积分（PI）控制器运行该系统时，要保持系统稳定并防止系统断开可能具有挑战性。本文对与电网相连的风力发电厂系统采用了积分反向步进控制。积分反步法是一种非线性递归方法，采用 Lyapunov 理论来确保系统的稳定性。为 Lyapunov 函数选择最佳增益值可确保改善系统控制。这些增益经常使用试错策略进行调整，既费时又低效。当需要确定许多参数时，这种技术就会变得更加复杂。它还限制了系统的性能，限制了这种非线性方法的优势。我们的目标是应用粒子群优化技术，通过最小化积分绝对误差标准指数来计算非线性方法的几个常量值。采用误差加权和来解决多目标问题。所提出的控制器可跟踪最大功率点，保持直流链路电压恒定，并控制有功功率和无功功率。该方法的鲁棒性在关键条件下得到了验证，包括系统参数变化以及非对称和对称电网故障。仿真结果表明，与基于 PI 调节器的矢量控制相比，积分反向步法与粒子群优化的结合既有效又稳健，响应时间从 10.6 (ms) 缩短到 2 (ms)，消除了静态误差，改善了过冲。此外，使用建议的控制器，不对称电网故障期间的直流链路电压纹波可降至 ±1 (V)。由于中央处理器单元技术的进步，后者得以实现。

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引用次数: 0

Review on ensemble meta-heuristics and reinforcement learning for manufacturing scheduling problems 针对生产调度问题的集合元启发式和强化学习综述

IF 4 3区计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE

Computers & Electrical Engineering

Pub Date : 2024-10-18 DOI: 10.1016/j.compeleceng.2024.109780

Yaping Fu , Yifeng Wang , Kaizhou Gao , Min Huang

With the development of Artificial Intelligence, Internet of Things and Big Data, intelligent manufacturing has become a new and popular trend in manufacturing industries. Manufacturing scheduling is one of the most critical components in intelligent manufacturing systems. It aims to optimize some specific objectives, e.g., production cost, customer satisfaction and energy efficiency, by making optimal decisions of processing routes, machine assignment, operation sequence, etc. Due to manufacturing scheduling problems featured with large scale, strong coupling and real-time optimization requirements, it is a huge challenge to effectively cope with them. As the extensive and successful applications of artificial intelligence in manufacturing areas, meta-heuristics and reinforcement learning methods achieve great breakthroughs in addressing manufacturing scheduling problems. It is noted that a hybridization of meta-heuristic and reinforcement learning algorithms has been recently proposed to solve such complicated problems. Firstly, this work summarizes the designs of meta-heuristics and reinforcement learning methods for dealing with manufacturing scheduling problems, respectively. Secondly, we review the hybridization of meta-heuristics and reinforcement learning methods in solving manufacturing scheduling problems, where the essential roles of reinforcement learning for meta-heuristics are analyzed and discussed from the views of ensemble methods, optimization criteria, scheduling models, performance evaluation metrics and stopping conditions. Finally, we conclude this work and sum up future research directions regarding the hybridization methods in handling manufacturing scheduling problems.

随着人工智能、物联网和大数据的发展，智能制造已成为制造业的一种新的流行趋势。生产调度是智能制造系统中最关键的组成部分之一。它旨在通过对加工路线、机器分配、操作顺序等做出最优决策，优化某些特定目标，如生产成本、客户满意度和能效。由于生产调度问题具有规模大、耦合性强、实时优化等特点，如何有效应对这些问题是一个巨大的挑战。随着人工智能在制造领域的广泛和成功应用，元启发式和强化学习方法在解决制造调度问题上取得了重大突破。最近，有人提出了元启发式算法和强化学习算法的混合算法来解决此类复杂问题。首先，本文总结了元启发式和强化学习方法分别用于处理生产调度问题的设计。其次，我们回顾了元启发式和强化学习方法在解决生产调度问题中的混合应用，从集合方法、优化准则、调度模型、性能评价指标和停止条件等方面分析和讨论了强化学习对元启发式的重要作用。最后，我们对这项工作进行了总结，并归纳了混合方法在处理生产调度问题方面的未来研究方向。

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引用次数: 0

Optimizing size and location of UPFC for enhanced system dynamic stability using hybrid approach 采用混合方法优化 UPFC 的大小和位置，增强系统动态稳定性

IF 4 3区计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE

Computers & Electrical Engineering

Pub Date : 2024-10-18 DOI: 10.1016/j.compeleceng.2024.109777

N. Anil , G. Balaji , G. Sireesha , S. Vijaya madhavi , V. Naresh

This paper proposed a novel hybrid optimization technique, combining the Enhanced Multi-Strategic Sparrow Search Algorithm (EMSA) and White Shark Optimizer (WSO), to determine the optimal location and size of a Unified Power Flow Controller (UPFC) for enhancing power system dynamic stability. The proposed method offers improved search capabilities, reduced randomness, and lower computational complexity compared to existing approaches. Generator faults can significantly impact system dynamic stability constraints, including voltage and power loss. EMSA algorithm is employed to identify optimal location for UPFC placement by selecting bus with minimum power loss. Subsequently, WSO algorithm is used to optimize UPFC's capacity, ensuring that affected system parameters and dynamic stability constraints are restored within safe limits. Optimized UPFC is then installed at identified location, and system's power flow is analyzed. Proposed method is implemented in MATLAB/Simulink environment and tested on both IEEE 30 and IEEE 14 standard benchmark systems. Proposed method's performance is evaluated by comparison with existing methods.

本文提出了一种新颖的混合优化技术，结合了增强型多策略麻雀搜索算法（EMSA）和白鲨优化器（WSO），以确定统一功率流控制器（UPFC）的最佳位置和大小，从而增强电力系统的动态稳定性。与现有方法相比，所提出的方法提高了搜索能力，减少了随机性，降低了计算复杂度。发电机故障会严重影响系统动态稳定性约束，包括电压和功率损耗。EMSA 算法通过选择功率损耗最小的母线来确定 UPFC 的最佳布置位置。随后，采用 WSO 算法优化 UPFC 的容量，确保受影响的系统参数和动态稳定约束恢复到安全范围内。然后在确定的位置安装优化后的 UPFC，并分析系统的功率流。建议的方法在 MATLAB/Simulink 环境中实现，并在 IEEE 30 和 IEEE 14 标准基准系统上进行了测试。通过与现有方法的比较，对拟议方法的性能进行了评估。

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引用次数: 0

vDefender: An explainable and introspection-based approach for identifying emerging malware behaviour at hypervisor-layer in virtualization environment vDefender：一种可解释的、基于内省的方法，用于识别虚拟化环境中管理程序层新出现的恶意软件行为

IF 4 3区计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE

Computers & Electrical Engineering

Pub Date : 2024-10-18 DOI: 10.1016/j.compeleceng.2024.109742

Avantika Gaur , Preeti Mishra , Vinod P. , Arjun Singh , Vijay Varadharajan , Uday Tupakula , Mauro Conti

Virtualization can be defined as the backbone of cloud computing services, which has gathered significant attention from organizations and users. Due to the increasing number of cyberattacks, virtualization security has become a crucial area of study. In this paper, we propose an explainable and introspection-based malware detection approach called vDefender for fine-grain monitoring of virtual machine (VM) processes at the hypervisor to identify the malicious behaviour of 17 different malware families of Windows exhibiting new evolving behaviour. Initially, it performs a basic security check to detect hidden processes and ensures the presence of security-critical processes. Then, deep memory introspection is performed using a software breakpoints injection approach to intercept the execution of processes. Various process activity logs are captured that include process-related, file manipulation, kernel heap object creation, exception-related activities, etc. Hybrid feature vectors are derived from these logs, which are reconstructed using the proposed mechanism to eliminate the redundant behaviour. The features are then learnt using Random Forest (RF) algorithm to classify distinct malware families. The interpretation and analysis of RF results involve the use of explainability techniques. The proposed approach achieves an accuracy of 95.49%, F1-score of 95.82% with 0.05% false alarms when evaluated using an emerging malware dataset. The contribution includes a comprehensive discussion of results, accompanied by a comparative analysis of current approaches that gives readers insight towards future research directions.

虚拟化可以被定义为云计算服务的支柱，它已受到企业和用户的极大关注。由于网络攻击日益增多，虚拟化安全已成为一个重要的研究领域。在本文中，我们提出了一种名为 vDefender 的可解释和基于内省的恶意软件检测方法，用于在管理程序上对虚拟机（VM）进程进行细粒度监控，以识别 17 种不同的 Windows 恶意软件家族的恶意行为，这些恶意软件家族表现出不断演变的新行为。最初，它执行基本的安全检查，以检测隐藏的进程，并确保安全关键进程的存在。然后，使用软件断点注入方法进行深度内存反省，以拦截进程的执行。捕获的各种进程活动日志包括进程相关活动、文件操作、内核堆对象创建、异常相关活动等。从这些日志中提取混合特征向量，并使用建议的机制对其进行重构，以消除冗余行为。然后使用随机森林（RF）算法学习这些特征，对不同的恶意软件家族进行分类。RF 结果的解释和分析涉及可解释性技术的使用。在使用新出现的恶意软件数据集进行评估时，所提出的方法达到了 95.49% 的准确率和 95.82% 的 F1 分数，误报率为 0.05%。论文包括对结果的全面讨论，以及对当前方法的比较分析，为读者指明了未来的研究方向。

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引用次数: 0

Placement and routing approach for MQCA-based designs with BFS and A* algorithms 采用 BFS 和 A* 算法的基于 MQCA 设计的布局和路由方法

IF 4 3区计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE

Computers & Electrical Engineering

Pub Date : 2024-10-18 DOI: 10.1016/j.compeleceng.2024.109771

Vineet Jaiswal , Trailokya Nath Sasamal

Magnetic Quantum-dot Cellular Automata (MQCA) based technologies hold significant promise in outperforming CMOS technology due to their reduced power consumption and increased device density. This new technology has several challenges in carrying tasks like circuit mapping, placement, and routing. This study presents a method for automatically mapping and routing a gate-level circuit using a Nanomagnetic Logic (NML) layout. Our approach leverages the Breadth-First Search algorithm for placement and the A* algorithm for each node’s circuit traversal and route generation. Clock synchronization, layout area, and other essential circuit design elements are skilfully integrated into the proposed algorithms. To validate the effectiveness of the proposed algorithms, we implemented various circuits, including 2:1 & 4:1 multiplexers, 1-bit & 2-bit full adders, XOR gate, and the C17 ISCAS 85 benchmark circuit. Moreover, to demonstrate the scalability of the algorithms, we also present ripple carry adders (RCAs) of different sizes. For a 64-bit RCA, our algorithms achieve significant improvements, with reductions of

\sim

91%–98% in clock zones,

\sim

91%–99% in nanomagnet counts, and a

\sim

99% reduction in the total bounded area compared to the state-of-the-art designs. Furthermore, to ensure the correctness of the proposed algorithms, we provide a detailed simulation analysis of implemented circuits using the NMLSim 2.0 micromagnetic simulator.

基于磁量子点蜂窝自动机（MQCA）的技术因其功耗低、器件密度高，在超越 CMOS 技术方面大有可为。这种新技术在执行电路映射、布局和布线等任务时面临一些挑战。本研究提出了一种使用纳米磁逻辑（NML）布局自动映射和布线门级电路的方法。我们的方法利用广度优先搜索算法进行布局，利用 A* 算法进行每个节点的电路遍历和路由生成。时钟同步、布局面积和其他重要的电路设计元素都巧妙地集成到了所提出的算法中。为了验证所提算法的有效性，我们实现了各种电路，包括 2:1 和 4:1 多路复用器、1 位和 2 位全加器、XOR 门和 C17 ISCAS 85 基准电路。此外，为了展示算法的可扩展性，我们还介绍了不同大小的纹波携带加法器（RCA）。对于 64 位 RCA，我们的算法取得了显著的改进，与最先进的设计相比，时钟区减少了 ∼91%-98% ，纳米磁体数量减少了 ∼91%-99% ，总边界面积减少了 ∼99% 。此外，为了确保所提算法的正确性，我们使用 NMLSim 2.0 微磁模拟器对实现的电路进行了详细的模拟分析。

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引用次数: 0