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Bolstering IoT security with IoT device type Identification using optimized Variational Autoencoder Wasserstein Generative Adversarial Network. 利用优化的变异自动编码器 Wasserstein 生成对抗网络识别物联网设备类型,增强物联网安全性。
IF 1.1 3区 计算机科学 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2024-08-01 Epub Date: 2024-01-31 DOI: 10.1080/0954898X.2024.2304214
Jothi Shri Sankar, Saravanan Dhatchnamurthy, Anitha Mary X, Keerat Kumar Gupta

Due to the massive growth in Internet of Things (IoT) devices, it is necessary to properly identify, authorize, and protect against attacks the devices connected to the particular network. In this manuscript, IoT Device Type Identification based on Variational Auto Encoder Wasserstein Generative Adversarial Network optimized with Pelican Optimization Algorithm (IoT-DTI-VAWGAN-POA) is proposed for Prolonging IoT Security. The proposed technique comprises three phases, such as data collection, feature extraction, and IoT device type detection. Initially, real network traffic dataset is gathered by distinct IoT device types, like baby monitor, security camera, etc. For feature extraction phase, the network traffic feature vector comprises packet sizes, Mean, Variance, Kurtosis derived by Adaptive and concise empirical wavelet transforms. Then, the extracting features are supplied to VAWGAN is used to identify the IoT devices as known or unknown. Then Pelican Optimization Algorithm (POA) is considered to optimize the weight factors of VAWGAN for better IoT device type identification. The proposed IoT-DTI-VAWGAN-POA method is implemented in Python and proficiency is examined under the performance metrics, like accuracy, precision, f-measure, sensitivity, Error rate, computational complexity, and RoC. It provides 33.41%, 32.01%, and 31.65% higher accuracy, and 44.78%, 43.24%, and 48.98% lower error rate compared to the existing methods.

由于物联网(IoT)设备的大规模增长,有必要对连接到特定网络的设备进行适当的识别、授权和防护。本手稿提出了基于变异自动编码器瓦瑟斯坦生成对抗网络(Variational Auto Encoder Wasserstein Generative Adversarial Network)和鹈鹕优化算法(Pelican Optimization Algorithm)的物联网设备类型识别技术(IoT-DTI-VAWGAN-POA),以延长物联网的安全性。所提出的技术包括三个阶段,如数据收集、特征提取和物联网设备类型检测。首先,通过不同的物联网设备类型(如婴儿监视器、安全摄像头等)收集真实的网络流量数据集。在特征提取阶段,网络流量特征向量包括数据包大小、平均值、方差和峰度,由自适应和简明经验小波变换得出。然后,将提取的特征提供给 VAWGAN,用于识别已知或未知的物联网设备。然后,考虑采用鹈鹕优化算法(POA)来优化 VAWGAN 的权重因子,以更好地识别物联网设备类型。所提出的 IoT-DTI-VAWGAN-POA 方法是用 Python 实现的,并根据准确度、精确度、f 值、灵敏度、错误率、计算复杂度和 RoC 等性能指标对其性能进行了检验。与现有方法相比,该方法的准确率分别提高了 33.41%、32.01% 和 31.65%,错误率分别降低了 44.78%、43.24% 和 48.98%。
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引用次数: 0
Improved deep belief network for estimating mango quality indices and grading: A computer vision-based neutrosophic approach. 用于估算芒果质量指标和分级的改进型深度信念网络:基于计算机视觉的中性方法。
IF 1.1 3区 计算机科学 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2024-08-01 Epub Date: 2024-01-15 DOI: 10.1080/0954898X.2023.2299851
Mukesh Kumar Tripathi, Shivendra

This research introduces a revolutionary machinet learning algorithm-based quality estimation and grading system. The suggested work is divided into four main parts: Ppre-processing, neutroscopic model transformation, Feature Extraction, and Grading. The raw images are first pre-processed by following five major stages: read, resize, noise removal, contrast enhancement via CLAHE, and Smoothing via filtering. The pre-processed images are then converted into a neutrosophic domain for more effective mango grading. The image is processed under a new Geometric Mean based neutrosophic approach to transforming it into the neutrosophic domain. Finally, the prediction of TSS for the different chilling conditions is done by Improved Deep Belief Network (IDBN) and based on this; the grading of mango is done automatically as the model is already trained with it. Here, the prediction of TSS is carried out under the consideration of SSC, firmness, and TAC. A comparison between the proposed and traditional methods is carried out to confirm the efficacy of various metrics.

本研究介绍了一种革命性的基于机器学习算法的质量评估和分级系统。建议的工作分为四个主要部分:预处理、中观模型转换、特征提取和分级。原始图像首先要经过五个主要阶段的预处理:读取、调整大小、去除噪声、通过 CLAHE 增强对比度以及通过滤波平滑。然后将预处理后的图像转换为中性域,以便更有效地进行芒果分级。采用基于几何平均数的新中性方法处理图像,将其转换到中性域。最后,通过改进的深度信念网络(IDBN)对不同冷藏条件下的 TSS 进行预测,并在此基础上自动对芒果进行分级,因为模型已经过训练。在这里,TSS 的预测是在考虑 SSC、硬度和 TAC 的情况下进行的。对所提出的方法和传统方法进行了比较,以确认各种指标的有效性。
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引用次数: 0
M2AI-CVD: Multi-modal AI approach cardiovascular risk prediction system using fundus images. M2AI-CVD:使用眼底图像的多模态人工智能心血管风险预测系统。
IF 1.1 3区 计算机科学 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2024-08-01 Epub Date: 2024-01-27 DOI: 10.1080/0954898X.2024.2306988
Premalatha Gurumurthy, Manjunathan Alagarsamy, Sangeetha Kuppusamy, Niranjana Chitra Ponnusamy

Cardiovascular diseases (CVD) represent a significant global health challenge, often remaining undetected until severe cardiac events, such as heart attacks or strokes, occur. In regions like Qatar, research focused on non-invasive CVD identification methods, such as retinal imaging and dual-energy X-ray absorptiometry (DXA), is limited. This study presents a groundbreaking system known as Multi-Modal Artificial Intelligence for Cardiovascular Disease (M2AI-CVD), designed to provide highly accurate predictions of CVD. The M2AI-CVD framework employs a four-fold methodology: First, it rigorously evaluates image quality and processes lower-quality images for further analysis. Subsequently, it uses the Entropy-based Fuzzy C Means (EnFCM) algorithm for precise image segmentation. The Multi-Modal Boltzmann Machine (MMBM) is then employed to extract relevant features from various data modalities, while the Genetic Algorithm (GA) selects the most informative features. Finally, a ZFNet Convolutional Neural Network (ZFNetCNN) classifies images, effectively distinguishing between CVD and Non-CVD cases. The research's culmination, tested across five distinct datasets, yields outstanding results, with an accuracy of 95.89%, sensitivity of 96.89%, and specificity of 98.7%. This multi-modal AI approach offers a promising solution for the accurate and early detection of cardiovascular diseases, significantly improving the prospects of timely intervention and improved patient outcomes in the realm of cardiovascular health.

心血管疾病(CVD)是全球健康面临的一项重大挑战,通常在心脏病发作或中风等严重心脏事件发生之前都不会被发现。在卡塔尔等地区,对非侵入性心血管疾病识别方法(如视网膜成像和双能 X 射线吸收测量法 (DXA))的研究十分有限。本研究提出了一种开创性的系统,称为心血管疾病多模式人工智能(M2AI-CVD),旨在提供高度准确的心血管疾病预测。M2AI-CVD 框架采用了四种方法:首先,它严格评估图像质量,并处理质量较低的图像以作进一步分析。随后,它使用基于熵的模糊 C 均值(EnFCM)算法进行精确的图像分割。然后使用多模态玻尔兹曼机(MMBM)从各种数据模态中提取相关特征,同时使用遗传算法(GA)选择信息量最大的特征。最后,ZFNet 卷积神经网络 (ZFNetCNN) 对图像进行分类,有效区分心血管疾病和非心血管疾病病例。研究成果在五个不同的数据集上进行了测试,结果非常出色,准确率达到 95.89%,灵敏度达到 96.89%,特异性达到 98.7%。这种多模式人工智能方法为准确、早期检测心血管疾病提供了一种前景广阔的解决方案,大大改善了及时干预的前景,提高了心血管健康领域的患者治疗效果。
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引用次数: 0
State identification for a class of uncertain switched systems by differential neural networks. 用微分神经网络识别一类不确定开关系统的状态。
IF 1.1 3区 计算机科学 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2024-08-01 Epub Date: 2024-01-11 DOI: 10.1080/0954898X.2023.2296115
Isaac Chairez, Alejandro Garcia-Gonzalez, Alberto Luviano-Juarez

This paper presents a non-parametric identification scheme for a class of uncertain switched nonlinear systems based on continuous-time neural networks. This scheme is based on a continuous neural network identifier. This adaptive identifier guaranteed the convergence of the identification errors to a small vicinity of the origin. The convergence of the identification error was determined by the Lyapunov theory supported by a practical stability variation for switched systems. The same stability analysis generated the learning laws that adjust the identifier structure. The upper bound of the convergence region was characterized in terms of uncertainties and noises affecting the switched system. A second finite-time convergence learning law was also developed to describe an alternative way of forcing the identification error's stability. The study presented in this paper described a formal technique for analysing the application of adaptive identifiers based on continuous neural networks for uncertain switched systems. The identifier was tested for two basic problems: a simple mechanical system and a switched representation of the human gait model. In both cases, accurate results for the identification problem were achieved.

本文提出了一种基于连续时间神经网络的不确定开关非线性系统的非参数识别方案。该方案基于连续神经网络识别器。这种自适应识别器保证了识别误差收敛到原点附近的小范围内。识别误差的收敛性是由里亚普诺夫理论决定的,该理论得到了开关系统实际稳定性变化的支持。同样的稳定性分析产生了调整识别器结构的学习定律。收敛区域的上限是根据影响开关系统的不确定性和噪声确定的。此外,还开发了第二种有限时间收敛学习定律,以描述迫使识别误差稳定的另一种方法。本文介绍的研究描述了一种正式技术,用于分析基于连续神经网络的自适应识别器在不确定开关系统中的应用。该识别器针对两个基本问题进行了测试:一个简单的机械系统和人类步态模型的切换表示。在这两种情况下,都取得了识别问题的准确结果。
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引用次数: 0
Secure and privacy improved cloud user authentication in biometric multimodal multi fusion using blockchain-based lightweight deep instance-based DetectNet. 使用基于区块链的轻量级深度实例检测网络,在生物识别多模态多融合中提高云用户身份验证的安全性和隐私性。
IF 1.1 3区 计算机科学 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2024-08-01 Epub Date: 2024-01-31 DOI: 10.1080/0954898X.2024.2304707
Selvarani Poomalai, Keerthika Venkatesan, Surendran Subbaraj, Sundar Radha

This research introduces an innovative solution addressing the challenge of user authentication in cloud-based systems, emphasizing heightened security and privacy. The proposed system integrates multimodal biometrics, deep learning (Instance-based learning-based DetectNet-(IL-DN), privacy-preserving techniques, and blockchain technology. Motivated by the escalating need for robust authentication methods in the face of evolving cyber threats, the research aims to overcome the struggle between accuracy and user privacy inherent in current authentication methods. The proposed system swiftly and accurately identifies users using multimodal biometric data through IL-DN. To address privacy concerns, advanced techniques are employed to encode biometric data, ensuring user privacy. Additionally, the system utilizes blockchain technology to establish a decentralized, tamper-proof, and transparent authentication system. This is reinforced by smart contracts and an enhanced Proof of Work (PoW) mechanism. The research rigorously evaluates performance metrics, encompassing authentication accuracy, privacy preservation, security, and resource utilization, offering a comprehensive solution for secure and privacy-enhanced user authentication in cloud-based environments. This work significantly contributes to filling the existing research gap in this critical domain.

本研究针对云系统中用户身份验证所面临的挑战提出了一种创新解决方案,强调提高安全性和隐私性。拟议的系统集成了多模态生物识别、深度学习(基于实例学习的 DetectNet-(IL-DN))、隐私保护技术和区块链技术。面对不断发展的网络威胁,人们对强大的身份验证方法的需求不断升级,这项研究旨在克服当前身份验证方法固有的准确性和用户隐私之间的矛盾。所提出的系统通过 IL-DN 使用多模态生物识别数据迅速准确地识别用户。为解决隐私问题,系统采用先进技术对生物识别数据进行编码,确保用户隐私。此外,该系统还利用区块链技术建立了一个去中心化、防篡改和透明的身份验证系统。智能合约和增强型工作量证明(PoW)机制强化了这一点。研究严格评估了性能指标,包括认证准确性、隐私保护、安全性和资源利用率,为基于云的环境中安全和隐私增强型用户认证提供了全面的解决方案。这项工作极大地填补了这一关键领域现有的研究空白。
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引用次数: 0
Identifying city bus passenger ridership patterns: a mixed-method analysis 识别城市公交乘客的乘车模式:混合方法分析
IF 2.5 4区 计算机科学 Q2 COMPUTER SCIENCE, CYBERNETICS Pub Date : 2024-07-16 DOI: 10.1108/k-01-2024-0113
Keng-Chieh Yang

Purpose

This study uses big data analysis aimed at discovering city bus passenger ridership patterns. Hence, marketing managers can get sufficient insights to formulate effective business plans and make timely decisions about company operations.

Design/methodology/approach

This study uses a mixed-method analysis to analyze the results. First uses the RFM (recency, frequency, and monetary) model combined with a big data technique (K-means) to analyze bus passenger boarding behavior. In order to improve the validity and quality of the research, this study also conducted interviews with senior managers of the bus company from which the data was obtained.

Findings

The study identifies six distinct groups of passengers with different boarding behaviors, ranging from “general passengers” to “most valuable passengers”. General passengers constituted the largest group. As such, they should be the main target for municipal governments when promoting bus ridership as part of energy conservation and carbon-reduction activities. This group of passengers should be encouraged to take public transport vehicles more, instead of relying on personal vehicles. The fourth group identified included elderly passengers with hospitals as their destinations. Bus companies can cooperate with municipal government to provide morning “medical bus” services for the elderly. Interviews with bus company managers confirmed that the analytical results of this study correspond with the observations, experiences, and actual business operating plans of bus companies.

Originality/value

Only few studies have analyzed passengers' boarding behavior applying a mixed-method analysis.

目的本研究采用大数据分析,旨在发现城市公交乘客的乘车模式。本研究采用混合方法分析结果。首先使用 RFM(重复性、频率和货币)模型结合大数据技术(K-means)来分析公交乘客的上车行为。为了提高研究的有效性和质量,本研究还对获取数据的公交公司的高级管理人员进行了访谈。 研究结果本研究确定了六个不同的乘客群体,他们的上车行为各不相同,从 "一般乘客 "到 "最有价值乘客 "不等。普通乘客是最大的群体。因此,作为节能减碳活动的一部分,市政府在促进公交车乘客数量时应以他们为主要目标。应鼓励这部分乘客更多地乘坐公共交通工具,而不是依赖私家车。第四类乘客包括以医院为目的地的老年乘客。公交公司可与市政府合作,为老年人提供 "医疗早班车 "服务。与公交公司管理人员的访谈证实,本研究的分析结果符合公交公司的观察、经验和实际业务运营计划。
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引用次数: 0
Cooperative supply game and its revenue allocation method considering location of transportation hub 考虑交通枢纽位置的合作供应博弈及其收益分配方法
IF 2.5 4区 计算机科学 Q2 COMPUTER SCIENCE, CYBERNETICS Pub Date : 2024-07-16 DOI: 10.1108/k-12-2023-2769
Guang Zhang, Jingyi Ge

Purpose

This paper aims to study the establishment of cooperative supply game model considering transportation hub location, and design the profit allocation rule of the cooperative supply coalition.

Design/methodology/approach

Based on the economic lost-sizing (ELS) game model and considering the location of transportation hub and the topology design of basic traffic network, we build a supply game model to maximize the profit of cooperative supply coalition. Based on the principle of proportion and the method of process allocation, we suppose the procedural proportional solution of the supplier cooperative supply game.

Findings

Through numerical examples, the validity and applicability of the proposed model and the procedural proportional solution were verified by comparing the procedural proportional solution with the weighted Shapley value, the equal division solution and the proportional rule.

Originality/value

This paper constructs a feasible mixed integer programming model for cooperative supply game. We also provide the algorithm of the allocation rule of cooperative supply game and the property analysis of the allocation rule.

目的 本文旨在研究考虑交通枢纽位置的合作供应博弈模型的建立,并设计合作供应联盟的利润分配规则。设计/方法/途径基于经济损失规模(ELS)博弈模型,并考虑交通枢纽位置和基本交通网络的拓扑设计,我们建立了一个供应博弈模型,以实现合作供应联盟的利润最大化。研究结果通过数值实例,比较了程序比例解与加权夏普利值、等分解和比例规则,验证了所提模型和程序比例解的有效性和适用性。本文构建了一个可行的合作供给博弈混合整数编程模型,并提供了合作供给博弈分配规则的算法和分配规则的性质分析。
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引用次数: 0
Direction-dependent bending resistance of 3D printed bio-inspired composites with asymmetric 3D articulated tiles. 具有非对称三维铰接瓦片的三维打印生物启发复合材料的抗弯曲性与方向有关。
IF 3.1 3区 计算机科学 Q1 ENGINEERING, MULTIDISCIPLINARY Pub Date : 2024-07-15 DOI: 10.1088/1748-3190/ad5ee7
Richard J Nash, Yaning Li

Inspired by the protective armors in nature, composites with asymmetric 3D articulated tiles attached to a soft layer are designed and fabricated via a multi-material 3D printer. The bending resistance of the new designs are characterized via three-point bending experiments. Bending rigidity, strength, and final deflection of the designs are quantified and compared when loaded in two different in-plane and two different out-of-plane directions. It is found that in general, the designs with articulated tiles show direction-dependent bending behaviors with significantly increased bending rigidity, strength, and deflection to final failure in certain loading directions, as is attributed to the asymmetric tile articulation (asymmetric about the mid-plane of tiles) and an interesting sliding-induced auxetic effect. Analytical, numerical, and experimental analyses are conducted to unveil the underlying mechanisms.

受自然界防护盔甲的启发,我们设计并通过多材料三维打印机制造了软层上附有非对称三维铰接瓦片的复合材料。新设计的抗弯性通过三点弯曲实验进行了表征。在两个不同的平面内和两个不同的平面外方向加载时,对设计的弯曲刚度、强度和最终挠度进行量化和比较。研究发现,一般来说,带有铰接瓦片的设计会表现出与方向相关的弯曲行为,在某些加载方向上,弯曲刚度、强度和最终破坏时的挠度都会显著增加,这归因于非对称瓦片铰接(瓦片中平面的非对称)和有趣的滑动诱导辅助效应。通过分析、数值和实验分析,揭示了其中的内在机理。
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引用次数: 0
Characterization of shark skin properties and biomimetic replication. 鲨鱼皮的特性和生物仿真复制。
IF 3.1 3区 计算机科学 Q1 ENGINEERING, MULTIDISCIPLINARY Pub Date : 2024-07-15 DOI: 10.1088/1748-3190/ad5c25
Stan R R Baeten, Ana Kochovski, Jovana Jovanova, Aimée Sakes

This review explores the present knowledge of the unique properties of shark skin and possible applications of its functionalities, including drag reduction and swimming efficiency. Tooth-like denticles, with varied morphologies, sizes, and densities across the shark's body, significantly influence the flow and interaction of fluids. Examining dermal denticle morphology, this study unveils the functional properties of real shark skin, including mechanical properties such as stiffness, stress-strain characteristics, and denticle density's impact on tensile properties. The adaptive capabilities of the Mako shark scales, especially in high-speed swimming, are explored, emphasizing their passive flow-actuated dynamic micro-roughness. This research contains an overview of various studies on real shark skin, categorizing them into skin properties, morphology, and hydrodynamics. The paper extends exploration into industrial applications, detailing fabrication techniques and potential uses in vessels, aircraft, and water pipes for friction reduction. Three manufacturing approaches, bio-replicated forming, direct fabrication, and indirect manufacturing, are examined, with 3D printing and photoconfiguration technology emerging as promising alternatives. Investigations into the mechanical properties of shark skin fabrics reveal the impact of denticle size on tensile strength, stress, and strain. Beyond drag reduction, the study highlights the shark skin's role in enhancing thrust and lift during locomotion. The paper identifies future research directions, emphasizing live shark testing and developing synthetic skin with the help of 3D printing incorporating the bristling effect.

这篇综述探讨了目前对鲨鱼皮肤独特性质的了解,以及鲨鱼皮肤功能的可能应用,包括减少阻力和提高游泳效率。鲨鱼全身不同形态、大小和密度的齿状小齿对流体的流动和相互作用有重大影响。这项研究通过考察真皮层的齿状突起形态,揭示了真正鲨鱼皮肤的功能特性,包括机械特性,如硬度、应力应变特性以及齿状突起密度对拉伸特性的影响。研究还探讨了鲭鲨鳞片的适应能力,尤其是在高速游泳时的适应能力,强调了其被动流动的动态微粗糙度。本研究概述了对真实鲨鱼皮肤的各种研究,并将其分为皮肤特性、形态和流体力学三类。论文将探索延伸到工业应用领域,介绍了制造技术以及在船舶、飞机和水管中减少摩擦的潜在用途。论文对生物复制成型、直接制造和间接制造三种制造方法进行了研究,并将三维打印和光子成型技术作为有前途的替代技术。对鲨鱼皮织物机械性能的研究揭示了齿粒大小对拉伸强度、应力和应变的影响。除了减少阻力,研究还强调了鲨鱼皮在运动过程中增强推力和升力的作用。论文指出了未来的研究方向,强调对鲨鱼进行活体测试,并借助三维打印技术开发出具有刚毛效应的合成皮肤。
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引用次数: 0
Enhancement of cyber security in IoT based on ant colony optimized artificial neural adaptive Tensor flow. 基于蚁群优化的人工神经自适应张量流增强物联网网络安全
IF 1.1 3区 计算机科学 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2024-07-15 DOI: 10.1080/0954898X.2024.2336058
Vijaya Bhaskar Sadu, Kumar Abhishek, Omaia Mohammed Al-Omari, Sandhya Rani Nallola, Rajeev Kumar Sharma, Mohammad Shadab Khan

The Internet of Things (IoT) is a network that connects various hardware, software, data storage, and applications. These interconnected devices provide services to businesses and can potentially serve as entry points for cyber-attacks. The privacy of IoT devices is increasingly vulnerable, particularly to threats like viruses and illegal software distribution lead to the theft of critical information. Ant Colony-Optimized Artificial Neural-Adaptive Tensorflow (ACO-ANT) technique is proposed to detect malicious software illicitly disseminated through the IoT. To emphasize the significance of each token in source duplicate data, the noise data undergoes processing using tokenization and weighted attribute techniques. Deep learning (DL) methods are then employed to identify source code duplication. Also the Multi-Objective Recurrent Neural Network (M-RNN) is used to identify suspicious activities within an IoT environment. The performance of proposed technique is examined using Loss, accuracy, F measure, precision to identify its efficiency. The experimental outcomes demonstrate that the proposed method ACO-ANT on Malimg dataset provides 12.35%, 14.75%, 11.84% higher precision and 10.95%, 15.78%, 13.89% higher f-measure compared to the existing methods. Further, leveraging block chain for malware detection is a promising direction for future research the fact that could enhance the security of IoT and identify malware threats.

物联网(IoT)是一个连接各种硬件、软件、数据存储和应用程序的网络。这些互联设备为企业提供服务,也可能成为网络攻击的切入点。物联网设备的隐私越来越易受攻击,特别是病毒和非法软件分发等威胁,导致关键信息被盗。我们提出了蚁群优化人工神经网络-自适应张量流(ACO-ANT)技术来检测通过物联网非法传播的恶意软件。为了强调源重复数据中每个标记的重要性,噪声数据使用标记化和加权属性技术进行处理。然后采用深度学习(DL)方法来识别源代码重复。此外,还使用多目标循环神经网络(M-RNN)来识别物联网环境中的可疑活动。我们使用损失率、准确率、F 值、精确度来检测所提议技术的性能,以确定其效率。实验结果表明,与现有方法相比,在 Malimg 数据集上提出的 ACO-ANT 方法的精确度分别提高了 12.35%、14.75% 和 11.84%,F 值分别提高了 10.95%、15.78% 和 13.89%。此外,利用区块链进行恶意软件检测是未来研究的一个很有前景的方向,因为它可以增强物联网的安全性并识别恶意软件威胁。
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引用次数: 0
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