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Machine learning security and privacy: a review of threats and countermeasures 机器学习的安全与隐私:威胁与对策综述
IF 3.6 Q1 Computer Science Pub Date : 2024-04-23 DOI: 10.1186/s13635-024-00158-3
Anum Paracha, Junaid Arshad, Mohamed Ben Farah, Khalid Ismail
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引用次数: 0
Intelligent multi-agent model for energy-efficient communication in wireless sensor networks 无线传感器网络节能通信的智能多代理模型
IF 3.6 Q1 Computer Science Pub Date : 2024-04-08 DOI: 10.1186/s13635-024-00155-6
Kiran Saleem, Lei Wang, Salil Bharany, Khmaies Ouahada, Ateeq Ur Rehman, Habib Hamam
The research addresses energy consumption, latency, and network reliability challenges in wireless sensor network communication, especially in military security applications. A multi-agent context-aware model employing the belief-desire-intention (BDI) reasoning mechanism is proposed. This model utilizes a semantic knowledge-based intelligent reasoning network to monitor suspicious activities within a prohibited zone, generating alerts. Additionally, a BDI intelligent multi-level data transmission routing algorithm is proposed to optimize energy consumption constraints and enhance energy-awareness among nodes. The energy optimization analysis involves the Energy Percent Dataset, showcasing the efficiency of four wireless sensor network techniques (E-FEERP, GTEB, HHO-UCRA, EEIMWSN) in maintaining high energy levels. E-FEERP consistently exhibits superior energy efficiency (93 to 98%), emphasizing its effectiveness. The Energy Consumption Dataset provides insights into the joule measurements of energy consumption for each technique, highlighting their diverse energy efficiency characteristics. Latency measurements are presented for four techniques within a fixed transmission range of 5000 m. E-FEERP demonstrates latency ranging from 3.0 to 4.0 s, while multi-hop latency values range from 2.7 to 2.9 s. These values provide valuable insights into the performance characteristics of each technique under specified conditions. The Packet Delivery Ratio (PDR) dataset reveals the consistent performance of the techniques in maintaining successful packet delivery within the specified transmission range. E-FEERP achieves PDR values between 89.5 and 92.3%, demonstrating its reliability. The Packet Received Data further illustrates the efficiency of each technique in receiving transmitted packets. Moreover the network lifetime results show E-FEERP consistently improving from 2550 s to round 925. GTEB and HHO-UCRA exhibit fluctuations around 3100 and 3600 s, indicating variable performance. In contrast, EEIMWSN consistently improves from round 1250 to 4500 s.
该研究解决了无线传感器网络通信,特别是军事安全应用中的能耗、延迟和网络可靠性挑战。研究提出了一种采用信念-愿望-意图(BDI)推理机制的多代理情境感知模型。该模型利用基于语义知识的智能推理网络监控禁区内的可疑活动,并生成警报。此外,还提出了一种 BDI 智能多级数据传输路由算法,以优化能耗约束并增强节点间的能量感知。能量优化分析涉及能量百分比数据集,展示了四种无线传感器网络技术(E-FEERP、GTEB、HHO-UCRA、EEIMWSN)在保持高能量水平方面的效率。E-FEERP 始终表现出卓越的能源效率(93% 至 98%),凸显了其有效性。能耗数据集提供了每种技术的焦耳能耗测量数据,突出了它们不同的能效特性。E-FEERP 的延迟时间为 3.0 至 4.0 秒,而多跳延迟时间为 2.7 至 2.9 秒。数据包交付率 (PDR) 数据集显示,这些技术在指定传输范围内保持数据包成功交付的性能始终如一。E-FEERP 的 PDR 值介于 89.5% 和 92.3% 之间,证明了其可靠性。数据包接收数据进一步说明了每种技术在接收传输数据包方面的效率。此外,网络寿命结果显示,E-FEERP 从 2550 秒持续提高到 925 秒。GTEB 和 HHO-UCRA 在 3100 秒和 3600 秒左右出现波动,表明性能参差不齐。相比之下,EEIMWSN 从 1250 到 4500 秒持续改善。
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引用次数: 0
FEDDBN-IDS: federated deep belief network-based wireless network intrusion detection system FEDDBN-IDS:基于联合深度信念网络的无线网络入侵检测系统
IF 3.6 Q1 Computer Science Pub Date : 2024-04-04 DOI: 10.1186/s13635-024-00156-5
M. Nivaashini, E. Suganya, S. Sountharrajan, M. Prabu, Durga Prasad Bavirisetti
Over the last 20 years, Wi-Fi technology has advanced to the point where most modern devices are small and rely on Wi-Fi to access the internet. Wi-Fi network security is severely questioned since there is no physical barrier separating a wireless network from a wired network, and the security procedures in place are defenseless against a wide range of threats. This study set out to assess federated learning, a new technique, as a possible remedy for privacy issues and the high expense of data collecting in network attack detection. To detect and identify cyber threats, especially in Wi-Fi networks, the research presents FEDDBN-IDS, a revolutionary intrusion detection system (IDS) that makes use of deep belief networks (DBNs) inside a federated deep learning (FDL) framework. Every device has a pre-trained DBN with stacking restricted Boltzmann machines (RBM) to learn low-dimensional characteristics from unlabelled local and private data. Later, these models are combined by a central server using federated learning (FL) to create a global model. The whole model is then enhanced by the central server with fully linked SoftMax layers to form a supervised neural network, which is then trained using publicly accessible labeled AWID datasets. Our federated technique produces a high degree of classification accuracy, ranging from 88% to 98%, according to the results of our studies.
在过去的 20 年里,Wi-Fi 技术已经发展到这样一个地步,即大多数现代设备都是小型的,依靠 Wi-Fi 接入互联网。由于无线网络与有线网络之间没有物理屏障,现有的安全程序无法抵御各种威胁,Wi-Fi 网络的安全性受到严重质疑。本研究旨在评估联合学习这种新技术,以解决网络攻击检测中的隐私问题和高昂的数据收集费用。为了检测和识别网络威胁,尤其是 Wi-Fi 网络中的网络威胁,该研究提出了 FEDDBN-IDS,这是一种革命性的入侵检测系统(IDS),在联合深度学习(FDL)框架内利用深度信念网络(DBN)。每台设备都有一个预先训练好的 DBN,通过堆叠受限玻尔兹曼机(RBM)从未标明的本地数据和私人数据中学习低维特征。随后,中央服务器使用联合学习(FL)将这些模型组合起来,创建一个全局模型。然后,中央服务器通过完全链接的 SoftMax 层对整个模型进行增强,形成一个监督神经网络,然后使用可公开访问的标记 AWID 数据集对其进行训练。根据我们的研究结果,我们的联合技术产生了很高的分类准确率,从 88% 到 98% 不等。
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引用次数: 0
Cancelable templates for secure face verification based on deep learning and random projections 基于深度学习和随机投影的安全人脸验证可取消模板
IF 3.6 Q1 Computer Science Pub Date : 2024-03-08 DOI: 10.1186/s13635-023-00147-y
Arslan Ali, Andrea Migliorati, Tiziano Bianchi, Enrico Magli
Recently, biometric recognition has become a significant field of research. The concept of cancelable biometrics (CB) has been introduced to address security concerns related to the handling of sensitive data. In this paper, we address unconstrained face verification by proposing a deep cancelable framework called BiometricNet+ that employs random projections (RP) to conceal face images and compressive sensing (CS) to reconstruct measurements in the original domain. Our lightweight design enforces the properties of unlinkability, revocability, and non-invertibility of the templates while preserving face recognition accuracy. We compare facial features by learning a regularized metric: at training time, we jointly learn facial features and the metric such that matching and non-matching pairs are mapped onto latent target distributions; then, for biometric verification, features are randomly projected via random matrices changed at every enrollment and query and reconstructed before the latent space mapping is computed. We assess the face recognition accuracy of our framework on challenging datasets such as LFW, CALFW, CPLFW, AgeDB, YTF, CFP, and RFW, showing notable improvements over state-of-the-art techniques while meeting the criteria for secure cancelable template design. Since our method requires no fine-tuning of the learned features, it can be applied to pre-trained networks to increase sensitive data protection.
最近,生物识别已成为一个重要的研究领域。可取消生物识别(CB)的概念已被引入,以解决与处理敏感数据有关的安全问题。在本文中,我们提出了一种名为 BiometricNet+ 的深度可取消框架,利用随机投影(RP)隐藏人脸图像,并利用压缩传感(CS)重建原始域中的测量值,从而解决无约束人脸验证问题。我们的轻量级设计在保持人脸识别准确性的同时,还强化了模板的不可链接性、可撤销性和不可逆转性。我们通过学习正则化度量来比较面部特征:在训练时,我们共同学习面部特征和度量,从而将匹配和非匹配对映射到潜在目标分布上;然后,在生物识别验证时,通过每次注册和查询时改变的随机矩阵随机投射特征,并在计算潜在空间映射之前进行重建。我们在具有挑战性的数据集(如 LFW、CALFW、CPLFW、AgeDB、YTF、CFP 和 RFW)上评估了我们的框架的人脸识别准确性,结果表明,与最先进的技术相比,我们的框架有显著的改进,同时符合安全可取消模板设计的标准。由于我们的方法不需要对学习到的特征进行微调,因此它可以应用于预训练网络,以加强敏感数据的保护。
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引用次数: 0
RFID tag recognition model for Internet of Things for training room management 用于培训室管理的物联网 RFID 标签识别模型
IF 3.6 Q1 Computer Science Pub Date : 2024-02-24 DOI: 10.1186/s13635-024-00154-7
Shengqi Wu
With the rapid development of the Internet of Things and intelligent technology, the application of Radio Frequency Identification (RFID) technology in training room management is becoming increasingly widespread. An efficient and accurate RFID system can significantly improve the management efficiency and resource utilization of the training room, thereby improving teaching quality and reducing management costs. Although RFID technology has many advantages, there are still some problems in practical applications, such as label collision and recognition of unknown labels. These issues not only affect the performance of the system but may also cause interference with actual teaching and management. This study proposes a grouping-based bit arbitration query tree algorithm and anti-collision technology to solve label collisions and reduce label recognition time in the technology. A new unknown label recognition algorithm is also proposed to improve the recognition efficiency and accuracy of identifying new unknown labels. Related experiments have shown that the recognition accuracy of the algorithm designed this time is 95.86%. Compared with other algorithms, the number of idle time slots is the smallest. When the number of queries is 1000, the algorithm has 1842 queries, and the communication complexity is the best. When the number of unknown tags is 10,000, the actual accuracy rate is 95.642%. Compared with traditional recognition algorithms, the new unknown label recognition algorithm has a smaller frame length in the same label proportion and good recognition performance. On a theoretical level, the research content on RFID technology helps to improve and develop the basic theories of the Internet of Things and intelligent recognition technology and provides solutions and application technologies for equipment management and IoT applications in training rooms. On a practical level, the research results can provide specific guidance for the management of training rooms, help solve equipment management and safety maintenance problems in practical applications, and improve the management efficiency of training rooms.
随着物联网和智能技术的快速发展,射频识别(RFID)技术在实训室管理中的应用也越来越广泛。高效、准确的 RFID 系统能显著提高实训室的管理效率和资源利用率,从而提高教学质量,降低管理成本。虽然 RFID 技术有很多优点,但在实际应用中仍存在一些问题,如标签碰撞和未知标签的识别等。这些问题不仅会影响系统性能,还可能对实际教学和管理造成干扰。本研究提出了一种基于分组的位仲裁查询树算法和防碰撞技术,以解决该技术中的标签碰撞问题并缩短标签识别时间。同时,还提出了一种新的未知标签识别算法,以提高识别效率和识别新的未知标签的准确性。相关实验表明,本次设计的算法识别准确率为 95.86%。与其他算法相比,空闲时隙数最小。当查询次数为 1000 次时,该算法的查询次数为 1842 次,通信复杂度最好。当未知标签数量为 10,000 个时,实际准确率为 95.642%。与传统识别算法相比,新的未知标签识别算法在相同标签比例下,帧长较小,识别性能良好。在理论层面,RFID 技术的研究内容有助于完善和发展物联网和智能识别技术的基础理论,为实训室设备管理和物联网应用提供解决方案和应用技术。在实践层面,研究成果可为实训室管理提供具体指导,有助于解决实际应用中的设备管理和安全维护问题,提高实训室的管理效率。
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引用次数: 0
Network security threat detection technology based on EPSO-BP algorithm 基于 EPSO-BP 算法的网络安全威胁检测技术
IF 3.6 Q1 Computer Science Pub Date : 2024-02-24 DOI: 10.1186/s13635-024-00152-9
Zhu Lan
With the development of Internet technology, the large number of network nodes and dynamic structure makes network security detection more complex, which requires the use of a multi-layer feedforward neural network to build a security threat detection model to improve network security protection. Therefore, the entropy model is adopted to optimize the particle swarm algorithm to decode particles, and then the single-peak and multi-peak functions are used to test and compare the particle entropy and fitness values to optimize the weights and thresholds in the multi-layer feedforward neural network. Finally, Suspicious Network Event Recognition Dataset discovered by data mining is sampled and applied to the entropy model particle swarm optimization for training. The test results show that there are four functions for the optimal mean and standard deviation in this algorithm, with values of 5.712e − 02, 4.805e − 02, 4.914e − 01, 1.066e − 01, 1.577e − 01, 1.343e − 01, and 2.089e + 01, 5.926, respectively. Overall, the algorithm proposed in the study is the best. Finally, the detection rate of attack types is calculated. The multi-layer feedforward neural network algorithm is 83.80%, the particle swarm optimization neural network algorithm is 91.00%, and the entropy model particle swarm optimization algorithm is 95.00%. The experiment shows that the research model has high accuracy in detecting network security threats, which can provide technical support and theoretical assistance for network security protection.
随着互联网技术的发展,大量的网络节点和动态结构使得网络安全检测变得更加复杂,这就需要利用多层前馈神经网络构建安全威胁检测模型,提高网络安全防护能力。因此,采用熵模型优化粒子群算法对粒子进行解码,然后利用单峰函数和多峰函数对粒子熵和适度值进行测试和比较,优化多层前馈神经网络中的权值和阈值。最后,对数据挖掘发现的可疑网络事件识别数据集进行采样,并应用熵模型粒子群优化进行训练。测试结果表明,该算法的最优均值和标准差有四个函数,分别为 5.712e - 02、4.805e - 02、4.914e - 01、1.066e - 01、1.577e - 01、1.343e - 01 和 2.089e + 01、5.926。总体而言,本研究提出的算法是最好的。最后,计算攻击类型的检测率。多层前馈神经网络算法为 83.80%,粒子群优化神经网络算法为 91.00%,熵模型粒子群优化算法为 95.00%。实验表明,该研究模型在检测网络安全威胁方面具有较高的准确性,可为网络安全防护提供技术支持和理论帮助。
{"title":"Network security threat detection technology based on EPSO-BP algorithm","authors":"Zhu Lan","doi":"10.1186/s13635-024-00152-9","DOIUrl":"https://doi.org/10.1186/s13635-024-00152-9","url":null,"abstract":"With the development of Internet technology, the large number of network nodes and dynamic structure makes network security detection more complex, which requires the use of a multi-layer feedforward neural network to build a security threat detection model to improve network security protection. Therefore, the entropy model is adopted to optimize the particle swarm algorithm to decode particles, and then the single-peak and multi-peak functions are used to test and compare the particle entropy and fitness values to optimize the weights and thresholds in the multi-layer feedforward neural network. Finally, Suspicious Network Event Recognition Dataset discovered by data mining is sampled and applied to the entropy model particle swarm optimization for training. The test results show that there are four functions for the optimal mean and standard deviation in this algorithm, with values of 5.712e − 02, 4.805e − 02, 4.914e − 01, 1.066e − 01, 1.577e − 01, 1.343e − 01, and 2.089e + 01, 5.926, respectively. Overall, the algorithm proposed in the study is the best. Finally, the detection rate of attack types is calculated. The multi-layer feedforward neural network algorithm is 83.80%, the particle swarm optimization neural network algorithm is 91.00%, and the entropy model particle swarm optimization algorithm is 95.00%. The experiment shows that the research model has high accuracy in detecting network security threats, which can provide technical support and theoretical assistance for network security protection.","PeriodicalId":46070,"journal":{"name":"EURASIP Journal on Information Security","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2024-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139956255","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Efficient identity security authentication method based on improved R-LWE algorithm in IoT environment 物联网环境中基于改进的 R-LWE 算法的高效身份安全认证方法
IF 3.6 Q1 Computer Science Pub Date : 2024-02-22 DOI: 10.1186/s13635-024-00153-8
Lin Yang
In recent years, various smart devices based on IoT technology, such as smart homes, healthcare, detection, and logistics systems, have emerged. However, as the number of IoT-connected devices increases, securing the IoT is becoming increasingly challenging. To tackle the increasing security challenges caused by the proliferation of IoT devices, this research proposes an innovative method for IoT identity authentication. The method is based on an improved ring-learning with errors (R-LWE) algorithm, which encrypts and decrypts communication between devices and servers effectively using polynomial modular multiplication and modular addition operations. The main innovation of this study is the improvement of the traditional R-LWE algorithm, enhancing its efficiency and security. Experimental results demonstrated that, when compared to number theory-based algorithms and elliptic curve cryptography algorithms at a 256-bit security level, the enhanced algorithm achieves significant advantages. The improved algorithm encrypted 20 data points with an average runtime of only 3.6 ms, compared to 7.3 ms and 7.7 ms for the other algorithms. Similarly, decrypting the same amount of data had an average runtime of 2.9 ms, as opposed to 7.3 ms and 8 ms for the other algorithms. Additionally, the improved R-LWE algorithm had significant advantages in terms of communication and storage costs. Compared to the number theory-based algorithm, the R-LWE algorithm reduced communication and storage costs by 3 °C each, and compared to elliptic curve cryptography, it reduced them by 4 °C each. This achievement not only enhances the efficiency of encryption and decryption but also lowers the overall operational costs of the algorithm. The research has made significant strides in improving the security and efficiency of IoT device identity authentication by enhancing the R-LWE algorithm. This study provides theoretical and practical foundations for the development and application of related technologies, as well as new solutions for IoT security.
近年来,基于物联网技术的各种智能设备不断涌现,如智能家居、医疗保健、检测和物流系统等。然而,随着物联网连接设备数量的增加,确保物联网安全也变得越来越具有挑战性。为了应对物联网设备激增带来的日益严峻的安全挑战,本研究提出了一种创新的物联网身份验证方法。该方法基于改进的带误差环学习(R-LWE)算法,利用多项式模块乘法和模块加法运算有效地加密和解密设备与服务器之间的通信。这项研究的主要创新点在于改进了传统的 R-LWE 算法,提高了其效率和安全性。实验结果表明,与基于数论的算法和 256 位安全级别的椭圆曲线加密算法相比,改进算法具有显著优势。改进算法加密 20 个数据点的平均运行时间仅为 3.6 毫秒,而其他算法分别为 7.3 毫秒和 7.7 毫秒。同样,解密相同数量数据的平均运行时间为 2.9 毫秒,而其他算法分别为 7.3 毫秒和 8 毫秒。此外,改进的 R-LWE 算法在通信和存储成本方面也有显著优势。与基于数论的算法相比,R-LWE 算法的通信和存储成本各降低了 3 ℃,而与椭圆曲线加密法相比,则各降低了 4 ℃。这一成果不仅提高了加密和解密的效率,还降低了算法的总体运行成本。该研究通过增强 R-LWE 算法,在提高物联网设备身份验证的安全性和效率方面取得了重大进展。这项研究为相关技术的开发和应用提供了理论和实践基础,也为物联网安全提供了新的解决方案。
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引用次数: 0
Improved RFID mutual authentication protocol against exhaustive attack in the context of big data 大数据背景下针对穷举攻击的改进型 RFID 相互验证协议
IF 3.6 Q1 Computer Science Pub Date : 2024-01-31 DOI: 10.1186/s13635-024-00151-w
Kongze Li
The development of big data has epromoted the development of Internet technology, but it has brought more network security and privacy problems. Therefore, how to solve network security problems is the main research direction of current network technology development. In order to deal with the harm of network attacks to personal privacy security, this paper studies and proposes an RFID mutual authentication protocol against exhaustive attacks based on improved Hash function, and proposes a security proof based on BAN logic rules. At the same time, to enhance the computing resources of the improved protocol, this paper proposes an improved authentication query protocol for multi-source RFID tags. In the performance analysis, when the distance between the reader and the tag reaches 10 m, the improved protocol can still be higher than 90%. The application test shows that the improved protocol proposed in the study is capable of resisting exhaustive attacks, its execution time is short, and it is less affected by the number of tags. The above results show that in the context of big data, the improved RFID mutual authentication protocol proposed by the research against network exhaustive attacks has a more significant defense effect, can effectively protect user privacy, and has a greater reference value in network security research.
大数据的发展推动了互联网技术的发展,但也带来了更多的网络安全和隐私问题。因此,如何解决网络安全问题是当前网络技术发展的主要研究方向。针对网络攻击对个人隐私安全的危害,本文研究并提出了一种基于改进哈希函数的 RFID 互认证协议,并提出了基于 BAN 逻辑规则的安全证明。同时,为了提高改进协议的计算资源,本文提出了一种针对多源 RFID 标签的改进认证查询协议。在性能分析中,当读写器与标签之间的距离达到 10 m 时,改进协议的识别率仍能高于 90%。应用测试表明,本研究提出的改进协议能够抵御穷举攻击,执行时间短,受标签数量的影响较小。以上结果表明,在大数据背景下,研究提出的针对网络穷举攻击的改进RFID相互认证协议具有较为显著的防御效果,能有效保护用户隐私,在网络安全研究中具有较大的参考价值。
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引用次数: 0
Research on privacy and secure storage protection of personalized medical data based on hybrid encryption 基于混合加密的个性化医疗数据隐私和安全存储保护研究
IF 3.6 Q1 Computer Science Pub Date : 2024-01-02 DOI: 10.1186/s13635-023-00150-3
Jialu Lv
Personalized medical data privacy and secure storage protection face serious challenges, especially in terms of data security and storage efficiency. Traditional encryption and storage solutions cannot meet the needs of modern medical data protection, which has led to an urgent need for new data protection strategies. Research personalized medical data privacy and secure storage protection based on hybrid encryption, in order to improve the security and efficiency of data storage. A hybrid encryption mechanism was proposed, which uses user attributes as keys for data encryption. The results show that the storage consumption of user attribute keys increases with the number of user attributes, but the consumption of hybrid encryption privacy storage technology is much smaller than that of traditional schemes. In the test, when the number of users increased to 30, the processing time first reached 1200 ms. During the increase in data volume, both test data and real data showed a brief decrease in attack frequency, but after the data volume reached 730–780, the attack frequency increased. It is worth noting that the performance of test data is better than that of real data. Personalized medical data privacy and secure storage protection based on hybrid encryption can not only effectively improve data security and reduce the risk of attack, but also greatly outperform traditional solutions in storage consumption and processing time. It has important practical significance for modern medical data storage protection.
个性化医疗数据隐私和安全存储保护面临严峻挑战,尤其是在数据安全和存储效率方面。传统的加密和存储解决方案无法满足现代医疗数据保护的需求,因此迫切需要新的数据保护策略。研究基于混合加密的个性化医疗数据隐私和安全存储保护,以提高数据存储的安全性和效率。提出了一种混合加密机制,将用户属性作为数据加密的密钥。结果表明,用户属性密钥的存储消耗随用户属性数量的增加而增加,但混合加密隐私存储技术的消耗远小于传统方案。在测试中,当用户数量增加到 30 个时,处理时间首先达到了 1200 毫秒。在数据量增加的过程中,测试数据和真实数据的攻击频率都出现了短暂的下降,但在数据量达到 730-780 后,攻击频率有所上升。值得注意的是,测试数据的性能优于真实数据。基于混合加密的个性化医疗数据隐私安全存储保护不仅能有效提高数据安全性,降低攻击风险,而且在存储消耗和处理时间上大大优于传统解决方案。这对现代医疗数据存储保护具有重要的现实意义。
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引用次数: 0
IoT devices and data availability optimization by ANN and KNN 利用 ANN 和 KNN 优化物联网设备和数据可用性
IF 3.6 Q1 Computer Science Pub Date : 2024-01-02 DOI: 10.1186/s13635-023-00145-0
Zhiqiang Chen, Zhihua Song, Tao Zhang, Yong Wei
Extensive research has been conducted to enhance the availability of IoT devices and data by focusing on the rapid prediction of instantaneous fault rates and temperatures. Temperature plays a crucial role in device availability as it significantly impacts equipment performance and lifespan. It serves as a vital indicator for predicting equipment failure and enables the improvement of availability and efficiency through effective temperature management. In the proposed optimization scheme for IoT device and data availability, the artificial neural network (ANN) algorithm and the K-Nearest Neighbours (KNN) algorithm are utilized to drive a neural network. The preliminary algorithm for availability optimization is chosen, and the target is divided into two parts: data optimization and equipment optimization. Suitable models are constructed for each part, and the KNN-driven neural network algorithm is employed to solve the proposed optimization model. The effectiveness of the proposed scheme is clearly demonstrated by the verification results. When compared to the benchmark method, the availability forward fault-tolerant method, and the heuristic optimization algorithm, the maximum temperature was successfully reduced to 2.0750 °C. Moreover, significant enhancements in the average availability of IoT devices were achieved, with improvements of 27.03%, 15.76%, and 10.85% respectively compared to the aforementioned methods. The instantaneous failure rates were 100%, 87.89%, and 84.4% respectively for the three algorithms. This optimization algorithm proves highly efficient in eliminating fault signals and optimizing the prediction of time-limited satisfaction. Furthermore, it exhibits strategic foresight in the decision-making process.
为了提高物联网设备和数据的可用性,人们进行了大量研究,重点是快速预测瞬时故障率和温度。温度在设备可用性方面起着至关重要的作用,因为它对设备的性能和使用寿命有重大影响。它是预测设备故障的重要指标,并能通过有效的温度管理提高可用性和效率。在针对物联网设备和数据可用性提出的优化方案中,利用了人工神经网络(ANN)算法和 K-Nearest Neighbours (KNN) 算法来驱动神经网络。初步选择了可用性优化算法,并将目标分为两个部分:数据优化和设备优化。为每个部分构建了合适的模型,并采用 KNN 驱动的神经网络算法来求解所提出的优化模型。验证结果清楚地表明了所提方案的有效性。与基准方法、可用性前向容错方法和启发式优化算法相比,最高温度成功降低到 2.0750 °C。此外,物联网设备的平均可用性也得到了显著提高,与上述方法相比,分别提高了 27.03%、15.76% 和 10.85%。三种算法的瞬时故障率分别为 100%、87.89% 和 84.4%。事实证明,该优化算法在消除故障信号和优化限时满意度预测方面非常有效。此外,它在决策过程中还表现出了战略性的前瞻性。
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引用次数: 0
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EURASIP Journal on Information Security
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