DQ-NN 和幽灵路由增强多源和多目的地物联网的源位置隐私性

IF 2.5 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS EURASIP Journal on Information Security Pub Date : 2024-09-05 DOI:10.1186/s13635-024-00176-1
Arpitha T., Dharamendra Chouhan, Shreyas J.
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引用次数: 0

摘要

物联网(IoT)现已成为我们日常生活的重要组成部分。无论如何,各种设备的关联给物联网带来了众多安全挑战。在某些情况下,无处不在的数据或流量可能会被某些智能设备收集,从而威胁到源节点位置的隐私。为了解决这个问题,我们提出了一种名为深度 Q 学习神经网络(DQ-NN)的混合 DL 技术,用于基于幻象路由的物联网网络中的源位置隐私(SLP)。在这里,首先考虑一个具有多个来源和目的地的物联网网络,然后通过分析邻居列表、能量、距离和信任异质性参数来选择幽灵节点。然后,创建多条从源节点经由幽灵节点到达汇节点的路径。最后,通过提议的 DQ-NN 进行路径选择。此外,DQ-NN 是通过合并深度 Q 学习网络(DQN)和深度神经网络(DNN)获得的。我们创建了一个由 150 个节点组成的仿真环境,以研究其性能和可扩展性的有效性。所提出的新型 DQ-NN 优于其他现有算法,其网络寿命高达 111.912,安全周期为 664970.7 m,能量为 0.034 J,距离为 56.594 m。
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DQ-NN and phantom routing for enhanced source location privacy for IoT under multiple source and destination
The Internet of Things (IoT) is now an essential component of our day-to-day lives. In any case, the association of various devices presents numerous security challenges in IoT. In some cases, ubiquitous data or traffic may be collected by certain smart devices which threatens the privacy of a source node location. To address this issue, a hybrid DL technique named Deep Q Learning Neural network (DQ-NN) is proposed for the Source Location Privacy (SLP) in IoT networks based on phantom routing. Here, an IoT network with multiple sources and destinations is considered first, and then the phantom node is chosen by analyzing neighbor list, energy, distance, and trust heterogeneity parameters. After that, multiple routes are created from the source node to the sink node via the phantom node. Finally, path selection is performed by the proposed DQ-NN. Moreover, DQ-NN is obtained by merging the Deep Q Learning Network (DQN) and Deep Neural Network (DNN). A simulation environment consisting of 150 nodes is created to study the effectiveness of performance and scalability. The proposed novel DQ-NN outperforms other existing algorithms, by recording a high network lifetime is 111.912, a safety period of 664970.7 m, an energy is 0.034 J, and a distance is 56.594 m.
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来源期刊
EURASIP Journal on Information Security
EURASIP Journal on Information Security COMPUTER SCIENCE, INFORMATION SYSTEMS-
CiteScore
8.80
自引率
0.00%
发文量
6
审稿时长
13 weeks
期刊介绍: The overall goal of the EURASIP Journal on Information Security, sponsored by the European Association for Signal Processing (EURASIP), is to bring together researchers and practitioners dealing with the general field of information security, with a particular emphasis on the use of signal processing tools in adversarial environments. As such, it addresses all works whereby security is achieved through a combination of techniques from cryptography, computer security, machine learning and multimedia signal processing. Application domains lie, for example, in secure storage, retrieval and tracking of multimedia data, secure outsourcing of computations, forgery detection of multimedia data, or secure use of biometrics. The journal also welcomes survey papers that give the reader a gentle introduction to one of the topics covered as well as papers that report large-scale experimental evaluations of existing techniques. Pure cryptographic papers are outside the scope of the journal. Topics relevant to the journal include, but are not limited to: • Multimedia security primitives (such digital watermarking, perceptual hashing, multimedia authentictaion) • Steganography and Steganalysis • Fingerprinting and traitor tracing • Joint signal processing and encryption, signal processing in the encrypted domain, applied cryptography • Biometrics (fusion, multimodal biometrics, protocols, security issues) • Digital forensics • Multimedia signal processing approaches tailored towards adversarial environments • Machine learning in adversarial environments • Digital Rights Management • Network security (such as physical layer security, intrusion detection) • Hardware security, Physical Unclonable Functions • Privacy-Enhancing Technologies for multimedia data • Private data analysis, security in outsourced computations, cloud privacy
期刊最新文献
Access control for trusted data sharing DQ-NN and phantom routing for enhanced source location privacy for IoT under multiple source and destination Trajectory-aware privacy-preserving method with local differential privacy in crowdsourcing Enhancing internet of things security using entropy-informed RF-DNA fingerprint learning from Gabor-based images Cover-source mismatch in steganalysis: systematic review
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