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A multi-crop disease identification approach based on residual attention learning 基于剩余注意学习的多作物病害识别方法
IF 3 Q2 Computer Science Pub Date : 2023-01-01 DOI: 10.1515/jisys-2022-0248
Kirti, N. Rajpal
Abstract In this work, a technique is proposed to identify the diseases that occur in plants. The system is based on a combination of residual network and attention learning. The work focuses on disease identification from the images of four different plant types by analyzing leaf images of the plants. A total of four datasets are used for the work. The system incorporates attention-aware features computed by the Residual Attention Network (Res-ATTEN). The base of the network is ResNet-18 architecture. Integrating attention learning in the residual network helps improve the system's overall accuracy. Various residual attention units are combined to create a single architecture. Unlike the traditional attention network architectures, which focus only on a single type of attention, the system uses a mixed type of attention learning, i.e., a combination of spatial and channel attention. Our technique achieves state-of-the-art performance with the highest accuracy of 99%. The results show that the proposed system has performed well for both purposes and notably outperformed the traditional systems.
摘要本文提出了一种植物病害识别技术。该系统基于残差网络和注意学习的结合。通过对四种不同类型植物叶片图像的分析,对病害进行了识别。这项工作总共使用了四个数据集。该系统结合了剩余注意网络(res - aten)计算的注意感知特征。网络的基础是ResNet-18架构。残差网络中集成注意力学习有助于提高系统的整体准确率。各种剩余的注意力单元被组合起来创建一个单一的体系结构。与传统的注意力网络架构只关注单一类型的注意力不同,该系统使用混合类型的注意力学习,即空间和通道注意力的结合。我们的技术达到了最先进的性能,准确率高达99%。结果表明,所提出的系统在两方面都表现良好,并且明显优于传统系统。
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引用次数: 1
Towards a better similarity algorithm for host-based intrusion detection system 针对基于主机的入侵检测系统,提出一种更好的相似度算法
IF 3 Q2 Computer Science Pub Date : 2023-01-01 DOI: 10.1515/jisys-2022-0259
Lounis Ouarda, Malika Bourenane, Bouderah Brahim
Abstract An intrusion detection system plays an essential role in system security by discovering and preventing malicious activities. Over the past few years, several research projects on host-based intrusion detection systems (HIDSs) have been carried out utilizing the Australian Defense Force Academy Linux Dataset (ADFA-LD). These HIDS have also been subjected to various algorithm analyses to enhance their detection capability for high accuracy and low false alarms. However, less attention is paid to the actual implementation of real-time HIDS. Our principal objective in this study is to create a performant real-time HIDS. We propose a new model, “Better Similarity Algorithm for Host-based Intrusion Detection System” (BSA-HIDS), using the same dataset ADFA-LD. The proposed model uses three classifications to represent the attack folder according to certain criteria, the entire system call sequence is used. Furthermore, this work uses textual distance and compares five algorithms like Levenshtein, Jaro–Winkler, Jaccard, Hamming, and Dice coefficient, to classify the system call trace as attack or non-attack based on the notions of interclass decoupling and intra-class coupling. The model can detect zero-day attacks because of the threshold definition. The experimental results show a good detection performance in real-time for Levenshtein/Jaro–Winkler algorithms, 99–94% in detection rate, 2–5% in false alarm rate, and 3,300–720 s in running time, respectively.
入侵检测系统通过发现和阻止恶意活动,在系统安全中起着至关重要的作用。在过去几年中,利用澳大利亚国防军学院Linux数据集(ADFA-LD)开展了几个基于主机的入侵检测系统(hids)的研究项目。这些HIDS还进行了各种算法分析,以提高其检测能力,实现高精度和低误报。然而,很少有人关注实时HIDS的实际实施。我们在这项研究中的主要目标是创建一个高性能的实时HIDS。我们提出了一个新的模型,“基于主机的入侵检测系统的更好的相似算法”(BSA-HIDS),使用相同的数据集ADFA-LD。该模型按照一定的标准使用三种分类来表示攻击文件夹,使用整个系统调用序列。此外,这项工作使用文本距离并比较五种算法,如Levenshtein, Jaro-Winkler, Jaccard, Hamming和Dice系数,根据类间解耦和类内耦合的概念将系统调用跟踪分类为攻击或非攻击。由于阈值定义,该模型可以检测到零日攻击。实验结果表明,Levenshtein/ Jaro-Winkler算法具有良好的实时检测性能,检测率为99 ~ 94%,虚警率为2 ~ 5%,运行时间为3300 ~ 720 s。
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引用次数: 0
Anomaly detection for maritime navigation based on probability density function of error of reconstruction 基于重构误差概率密度函数的海上导航异常检测
Q2 Computer Science Pub Date : 2023-01-01 DOI: 10.1515/jisys-2022-0270
Zahra Sadeghi, Stan Matwin
Abstract Anomaly detection is a fundamental problem in data science and is one of the highly studied topics in machine learning. This problem has been addressed in different contexts and domains. This article investigates anomalous data within time series data in the maritime sector. Since there is no annotated dataset for this purpose, in this study, we apply an unsupervised approach. Our method benefits from the unsupervised learning feature of autoencoders. We utilize the reconstruction error as a signal for anomaly detection. For this purpose, we estimate the probability density function of the reconstruction error and find different levels of abnormality based on statistical attributes of the density of error. Our results demonstrate the effectiveness of this approach for localizing irregular patterns in the trajectory of vessel movements.
异常检测是数据科学的一个基本问题,也是机器学习领域研究的热点之一。这个问题已经在不同的上下文中和领域得到了解决。本文研究了海事部门时间序列数据中的异常数据。由于没有用于此目的的注释数据集,因此在本研究中,我们采用无监督方法。我们的方法得益于自编码器的无监督学习特性。我们利用重构误差作为异常检测的信号。为此,我们估计重构误差的概率密度函数,并根据误差密度的统计属性找到不同程度的异常。我们的结果证明了这种方法在船舶运动轨迹中定位不规则模式的有效性。
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引用次数: 0
A novel distance vector hop localization method for wireless sensor networks 一种新的无线传感器网络距离矢量跳定位方法
IF 3 Q2 Computer Science Pub Date : 2023-01-01 DOI: 10.1515/jisys-2023-0031
Y. A. A. S. Aldeen, S. Kadhim, N. N. Kadhim, Syed Hamid Hussain Madni
Abstract Wireless sensor networks (WSNs) require accurate localization of sensor nodes for various applications. In this article, we propose the distance vector hop localization method (DVHLM) to address the node dislocation issue in real-time networks. The proposed method combines trilateration and Particle Swarm Optimization techniques to estimate the location of unknown or dislocated nodes. Our methodology includes four steps: coordinate calculation, distance calculation, unknown node position estimation, and estimation correction. To evaluate the proposed method, we conducted simulation experiments and compared its performance with state-of-the-art methods in terms of localization accuracy with known nodes, dislocated nodes, and shadowing effects. Our results demonstrate that DVHLM outperforms the existing methods and achieves better localization accuracy with reduced error. This article provides a valuable contribution to the field of WSNs by proposing a new method with a detailed methodology and superior performance.
摘要无线传感器网络(WSNs)需要精确定位传感器节点以满足各种应用需求。在本文中,我们提出了距离矢量跳定位方法(DVHLM)来解决实时网络中的节点错位问题。该方法结合了三边测量和粒子群优化技术来估计未知或错位节点的位置。该方法包括坐标计算、距离计算、未知节点位置估计和估计校正四个步骤。为了评估所提出的方法,我们进行了仿真实验,并将其在已知节点、错位节点和阴影效果的定位精度方面与最先进的方法进行了比较。结果表明,该方法优于现有的定位方法,在误差较小的情况下获得了更好的定位精度。本文提出了一种方法详细、性能优越的无线传感器网络新方法,为无线传感器网络领域做出了宝贵的贡献。
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引用次数: 1
Deep learning for content-based image retrieval in FHE algorithms FHE算法中基于内容的图像检索的深度学习
IF 3 Q2 Computer Science Pub Date : 2023-01-01 DOI: 10.1515/jisys-2022-0222
Sura Mahmood Abdullah, Mustafa Musa Jaber
Abstract Content-based image retrieval (CBIR) is a technique used to retrieve image from an image database. However, the CBIR process suffers from less accuracy to retrieve many images from an extensive image database and prove the privacy of images. The aim of this article is to address the issues of accuracy utilizing deep learning techniques such as the CNN method. Also, it provides the necessary privacy for images using fully homomorphic encryption methods by Cheon–Kim–Kim–Song (CKKS). The system has been proposed, namely RCNN_CKKS, which includes two parts. The first part (offline processing) extracts automated high-level features based on a flatting layer in a convolutional neural network (CNN) and then stores these features in a new dataset. In the second part (online processing), the client sends the encrypted image to the server, which depends on the CNN model trained to extract features of the sent image. Next, the extracted features are compared with the stored features using a Hamming distance method to retrieve all similar images. Finally, the server encrypts all retrieved images and sends them to the client. Deep-learning results on plain images were 97.87% for classification and 98.94% for retriever images. At the same time, the NIST test was used to check the security of CKKS when applied to Canadian Institute for Advanced Research (CIFAR-10) dataset. Through these results, researchers conclude that deep learning is an effective method for image retrieval and that a CKKS method is appropriate for image privacy protection.
摘要基于内容的图像检索(CBIR)是一种从图像数据库中检索图像的技术。然而,在从庞大的图像数据库中检索大量图像和证明图像隐私时,CBIR过程的准确性较低。本文的目的是利用CNN方法等深度学习技术解决准确性问题。此外,它使用Cheon-Kim-Kim-Song (CKKS)的全同态加密方法为图像提供必要的隐私。提出了RCNN_CKKS系统,该系统包括两个部分。第一部分(离线处理)基于卷积神经网络(CNN)的平坦层提取自动化高级特征,然后将这些特征存储在新的数据集中。在第二部分(在线处理)中,客户端将加密后的图像发送给服务器,服务器依赖训练好的CNN模型提取发送图像的特征。接下来,将提取的特征与存储的特征进行比较,使用汉明距离法检索所有相似的图像。最后,服务器加密所有检索到的图像并将它们发送给客户机。对普通图像的深度学习分类率为97.87%,对检索图像的深度学习分类率为98.94%。同时,使用NIST测试来检查CKKS应用于加拿大高级研究所(CIFAR-10)数据集时的安全性。通过这些结果,研究人员得出结论,深度学习是一种有效的图像检索方法,CKKS方法适用于图像隐私保护。
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引用次数: 2
Intelligent control system for industrial robots based on multi-source data fusion 基于多源数据融合的工业机器人智能控制系统
IF 3 Q2 Computer Science Pub Date : 2023-01-01 DOI: 10.1515/jisys-2022-0286
Yang Zhang
Abstract Industrialization has advanced quickly, bringing intelligent production and manufacturing into people’s daily lives, but it has also created a number of issues with the ability of intelligent control systems for industrial robots. As a result, a study has been conducted on the use of multi-source data fusion methods in the mechanical industry. First, the research analyzes and discusses the existing research at home and abroad. Then, a robot intelligent control system based on multi-source fusion method is proposed, which combines multi-source data fusion with principal component analysis to better fuse data of multiple control periods; In the process, the experimental results are dynamically evaluated, and the performance of the proposed method is compared with other fusion methods. The results of the study showed that the confidence values and recognition correctness of the intelligent control system under the proposed method were superior compared to the Yu, Murphy, and Deng methods. Applying the method to the comparison of real-time and historical data values, it is found that the predicted data under the proposed method fits better with the actual data values, and the fit can be as high as 0.9945. The dynamic evaluation analysis of single and multi-factor in the simulation stage demonstrates that the control ability in the training samples of 0–100 is often better than the actual results, and the best evaluation results may be obtained at the sample size of 50 per batch. The aforementioned findings demonstrated that the multi-data fusion method that was suggested had a high degree of viability and accuracy for the intelligent control system of industrial robots and could offer a fresh line of enquiry for the advancement and development of the mechanical industrialization field.
摘要工业化发展迅速,将智能生产和制造带入人们的日常生活,但同时也给工业机器人智能控制系统的能力带来了一些问题。因此,对多源数据融合方法在机械工业中的应用进行了研究。首先,本研究对国内外已有的研究进行了分析和探讨。然后,提出了一种基于多源融合方法的机器人智能控制系统,将多源数据融合与主成分分析相结合,更好地融合了多个控制周期的数据;在此过程中,对实验结果进行了动态评价,并与其他融合方法进行了性能比较。研究结果表明,与Yu、Murphy和Deng方法相比,该方法下的智能控制系统的置信度值和识别正确性都有所提高。将该方法应用于实时数据值与历史数据值的比较,发现该方法下的预测数据与实际数据值拟合较好,拟合度可高达0.9945。仿真阶段单因素和多因素的动态评价分析表明,0-100个训练样本中的控制能力往往优于实际结果,每批50个样本量时可能获得最佳评价结果。上述研究结果表明,所提出的多数据融合方法对于工业机器人智能控制系统具有较高的可行性和准确性,可以为机械工业化领域的进步和发展提供新的思路。
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引用次数: 0
Replay attack detection based on deformable convolutional neural network and temporal-frequency attention model 基于可变形卷积神经网络和时频注意模型的重放攻击检测
IF 3 Q2 Computer Science Pub Date : 2023-01-01 DOI: 10.1515/jisys-2022-0265
Dang-en Xie, Hai Hu, Qiang Xu
Abstract As an important identity authentication method, speaker verification (SV) has been widely used in many domains, e.g., mobile financials. At the same time, the existing SV systems are insecure under replay spoofing attacks. Toward a more secure and stable SV system, this article proposes a replay attack detection system based on deformable convolutional neural networks (DCNNs) and a time–frequency double-channel attention model. In DCNN, the positions of elements in the convolutional kernel are not fixed. Instead, they are modified by some trainable variable to help the model extract more useful local information from input spectrograms. Meanwhile, a time–frequency domino double-channel attention model is adopted to extract more effective distinctive features to collect valuable information for distinguishing genuine and replay speeches. Experimental results on ASVspoof 2019 dataset show that the proposed model can detect replay attacks accurately.
作为一种重要的身份认证方法,说话人验证(SV)在移动金融等领域得到了广泛应用。同时,现有的SV系统在重放欺骗攻击下是不安全的。为了使SV系统更加安全稳定,本文提出了一种基于可变形卷积神经网络(DCNNs)和时频双通道注意力模型的重放攻击检测系统。在DCNN中,卷积核中元素的位置是不固定的。相反,它们被一些可训练的变量修改,以帮助模型从输入谱图中提取更多有用的局部信息。同时,采用时频骨牌双通道注意模型提取更有效的显著特征,为区分真实演讲和重播演讲收集有价值的信息。在ASVspoof 2019数据集上的实验结果表明,该模型能够准确检测重放攻击。
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引用次数: 0
Deep learning in distributed denial-of-service attacks detection method for Internet of Things networks 物联网网络分布式拒绝服务攻击检测方法中的深度学习
IF 3 Q2 Computer Science Pub Date : 2023-01-01 DOI: 10.1515/jisys-2022-0155
Firas Mohammed Aswad, Ali Ahmed, N. A. M. Alhammadi, Bashar Ahmad Khalaf, S. Mostafa
Abstract With the rapid growth of informatics systems’ technology in this modern age, the Internet of Things (IoT) has become more valuable and vital to everyday life in many ways. IoT applications are now more popular than they used to be due to the availability of many gadgets that work as IoT enablers, including smartwatches, smartphones, security cameras, and smart sensors. However, the insecure nature of IoT devices has led to several difficulties, one of which is distributed denial-of-service (DDoS) attacks. IoT systems have several security limitations due to their disreputability characteristics, like dynamic communication between IoT devices. The dynamic communications resulted from the limited resources of these devices, such as their data storage and processing units. Recently, many attempts have been made to develop intelligent models to protect IoT networks against DDoS attacks. The main ongoing research issue is developing a model capable of protecting the network from DDoS attacks that is sensitive to various classes of DDoS and can recognize legitimate traffic to avoid false alarms. Subsequently, this study proposes combining three deep learning algorithms, namely recurrent neural network (RNN), long short-term memory (LSTM)-RNN, and convolutional neural network (CNN), to build a bidirectional CNN-BiLSTM DDoS detection model. The RNN, CNN, LSTM, and CNN-BiLSTM are implemented and tested to determine the most effective model against DDoS attacks that can accurately detect and distinguish DDoS from legitimate traffic. The intrusion detection evaluation dataset (CICIDS2017) is used to provide more realistic detection. The CICIDS2017 dataset includes benign and up-to-date examples of typical attacks, closely matching real-world data of Packet Capture. The four models are tested and assessed using Confusion Metrix against four commonly used criteria: accuracy, precision, recall, and F-measure. The performance of the models is quite effective as they obtain an accuracy rate of around 99.00%, except for the CNN model, which achieves an accuracy of 98.82%. The CNN-BiLSTM achieves the best accuracy of 99.76% and precision of 98.90%.
随着现代信息系统技术的快速发展,物联网(IoT)在许多方面对人们的日常生活变得越来越有价值和重要。物联网应用现在比以前更受欢迎,这是由于许多可以作为物联网推动者的小工具的可用性,包括智能手表、智能手机、安全摄像头和智能传感器。然而,物联网设备的不安全特性导致了一些困难,其中之一是分布式拒绝服务(DDoS)攻击。物联网系统由于其不声誉特性(如物联网设备之间的动态通信)而存在一些安全限制。动态通信是由于这些设备有限的资源造成的,例如它们的数据存储和处理单元。最近,人们尝试开发智能模型来保护物联网网络免受DDoS攻击。目前正在进行的主要研究问题是开发一种能够保护网络免受DDoS攻击的模型,该模型对各种类型的DDoS很敏感,并且可以识别合法流量以避免误报。随后,本研究提出结合递归神经网络(RNN)、长短期记忆(LSTM)-RNN和卷积神经网络(CNN)三种深度学习算法,构建双向CNN- bilstm DDoS检测模型。通过对RNN、CNN、LSTM、CNN- bilstm的实现和测试,确定最有效的DDoS攻击模型,能够准确地检测和区分DDoS和合法流量。使用入侵检测评估数据集(CICIDS2017)提供更真实的检测。CICIDS2017数据集包括良性和最新的典型攻击示例,与数据包捕获的真实数据密切匹配。使用混淆矩阵对四个常用标准进行测试和评估:准确性、精度、召回率和F-measure。除了CNN模型的准确率为98.82%外,其他模型的性能都非常有效,准确率在99.00%左右。CNN-BiLSTM的准确率为99.76%,精密度为98.90%。
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引用次数: 5
RES-KELM fusion model based on non-iterative deterministic learning classifier for classification of Covid19 chest X-ray images 基于非迭代确定性学习分类器的RES-KELM融合模型用于新冠肺炎胸片图像分类
IF 3 Q2 Computer Science Pub Date : 2023-01-01 DOI: 10.1515/jisys-2022-0235
Arshi Husain, Virendra P. Vishvakarma
Abstract In this research, a novel real time approach has been proposed for detection and analysis of Covid19 using chest X-ray images based on a non-iterative deterministic classifier, kernel extreme learning machine (KELM), and a pretrained network ResNet50. The information extraction capability of deep learning and non-iterative deterministic training nature of KELM has been incorporated in the proposed novel fusion model. The binary classification is carried out with a non-iterative deterministic learning based classifier, KELM. Our proposed approach is able to minimize the average testing error up to 2.76 on first dataset, and up to 0.79 on the second one, demonstrating its effectiveness after experimental confirmation. A comparative analysis of the approach with other existing state-of-the-art methods is also presented in this research and the classification performance confirm the advantages and superiority of our novel approach called RES-KELM algorithm.
本研究提出了一种基于非迭代确定性分类器、核极限学习机(KELM)和预训练网络ResNet50的胸部x线图像实时检测和分析新冠肺炎的新方法。该融合模型结合了深度学习的信息提取能力和KELM的非迭代确定性训练特性。使用基于非迭代确定性学习的分类器KELM进行二元分类。我们提出的方法在第一个数据集上的平均测试误差最小,达到2.76,在第二个数据集上的平均测试误差最小,达到0.79,实验验证了该方法的有效性。本研究还将该方法与其他现有的最先进的方法进行了比较分析,分类性能证实了我们的新方法RES-KELM算法的优势和优越性。
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引用次数: 1
Efficient mutual authentication using Kerberos for resource constraint smart meter in advanced metering infrastructure 在高级计量基础设施中使用Kerberos实现资源约束智能电表的高效相互认证
IF 3 Q2 Computer Science Pub Date : 2023-01-01 DOI: 10.1515/jisys-2021-0095
Md. Mehedi Hasan, Noor Afiza Mohd Ariffin, N. F. M. Sani
Abstract The continuous development of information communication technology facilitates the conventional grid in transforming into an automated modern system. Internet-of-Things solutions are used along with the evolving services of end-users to the electricity service provider for smart grid applications. In terms of various devices and machine integration, adequate authentication is the key to an accurate source and destination in advanced metering infrastructure (AMI). Various protocols are deployed to lead the identification between two parties, which require high computation time and communicational bit operations for system development. Therefore, Kerberos-based authentication protocols were designed in this study with the assistance of elliptic curve cryptography to manage the mutual authentication between two parties and reduce the time and bit operations. The protocols were evaluated in a widely adopted tool, AVISPA, which builds an understanding of the proposed protocol and ensures mutual authentication without unauthorized knowledge. In addition, upon comparing security and performance assessments to the current schemes, it was found that the protocol in this study required less time and bits to transmit information. Consequently, it effectively provides multiple security features making it suitable for resource constraint smart meters in AMI.
信息通信技术的不断发展促进了传统电网向自动化的现代化电网的转变。物联网解决方案与最终用户不断发展的服务一起用于智能电网应用的电力服务提供商。就各种设备和机器集成而言,在高级计量基础设施(AMI)中,充分的身份验证是准确的源和目的的关键。在系统开发过程中,需要大量的计算时间和通信位操作来实现双方的身份识别。因此,本文设计了基于kerberos的认证协议,借助椭圆曲线密码学来管理双方的相互认证,减少时间和比特操作。在广泛采用的工具AVISPA中对协议进行了评估,该工具建立了对拟议协议的理解,并确保在未经授权的情况下进行相互认证。此外,将安全性和性能评估与现有方案进行比较,发现本研究中的协议传输信息所需的时间和比特数更少。因此,它有效地提供了多种安全特性,使其适合AMI中的资源约束智能电表。
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引用次数: 2
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Journal of Intelligent Systems
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