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A Cybersecurity Detection Platform Integrating IOTA DLT and IPFS for Vulnerability Management

Electronics (Switzerland) 2025年第10期14卷 1929-1929页

作者： Lin, Iuon-Chang Ruan, Jyun-Yan Chang, Ching-Chun Chang, Chin-Chen Wang, Chun-Tse Department of Management Information Systems National Chung Hsing University Taichung40227 Taiwan Information and Communication Security Research Center Feng Chia University Taichung40724 Taiwan Department of Information Engineering and Computer Science Feng Chia University Taichung40724 Taiwan Ph.D. Program for Infrastructure Planning and Engineering Feng Chia University Taichung40724 Taiwan

In response to the Cybersecurity Law, organizations face numerous management and technical requirements. Detection techniques such as vulnerability scanning and penetration testing are employed to identify risks. Addressing these vulnerabilities demands substantial manpower, time, and financial resources. security concerns also arise during digital file transmission and remediation efforts. This study proposes a security detection platform with step-by-step implementation guidelines, enabling resource-limited units to replicate the setup and address security gaps. It compares detection results between open-source and commercial tools, highlighting key differences and offering remediation strategies. Numerous digital files (e.g., test reports) are generated during testing. To ensure secure storage and sharing, the system integrates IOTA’s distributed ledger and IPFS, generating HASH values and uploading files on-chain to preserve integrity and authenticity. The objective is to deliver a scalable, cost-effective security detection framework that enhances system resilience while minimizing resource consumption. © 2025 by the authors.

关键词： Cyber attacks

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Efficient Federated Learning with Spike Neural Networks for Traffic Sign Recognition

arXiv

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arXiv 2022年

作者： Xie, Kan Zhang, Zhe Li, Bo Kang, Jiawen Niyato, Dusit Xie, Shengli Wu, Yi The School of Automation Guangdong University of Technology Guangzhou510006 China The Key Laboratory of Intelligent Information Processing and System Integration of IoT Ministry of Education Guangzhou510006 China The Guangdong-HongKong-Macao Joint Laboratory for Smart Discrete Manufacturing Guangzhou510006 China The 111 Center for Intelligent Batch Manufacturing Based on IoT Technology Guangzhou510006 China The Guangdong Key Laboratory of IoT Information Technology Guangzhou510006 China The School of Data Science and Technology Heilongjiang University Harbin150080 China The Institute for Cryptology & Network Security Heilongjiang University Harbin150080 China School of Computer Science and Engineering Nanyang Technological University Singapore

With the gradual popularization of self-driving, it is becoming increasingly important for vehicles to smartly make the right driving decisions and autonomously obey traffic rules by correctly recognizing traffic signs. However, for machine learning-based traffic sign recognition on the Internet of Vehicles (IoV), a large amount of traffic sign data from distributed vehicles is needed to be gathered in a centralized server for model training, which brings serious privacy leakage risk because of traffic sign data containing lots of location privacy information. To address this issue, we first exploit privacy-preserving federated learning to perform collaborative training for accurate recognition models without sharing raw traffic sign data. Nevertheless, due to the limited computing and energy resources of most devices, it is hard for vehicles to continuously undertake complex artificial intelligence tasks. Therefore, we introduce powerful Spike Neural Networks (SNNs) into traffic sign recognition for energy-efficient and fast model training, which is the next generation of neural networks and is practical and well-fitted to IoV scenarios. Furthermore, we design a novel encoding scheme for SNNs based on neuron receptive fields to extract information from the pixel and spatial dimensions of traffic signs to achieve high-accuracy training. Numerical results indicate that the proposed federated SNN outperforms traditional federated convolutional neural networks in terms of accuracy, noise immunity, and energy efficiency as well. Copyright © 2022, The Authors. All rights reserved.

关键词： Traffic signs

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Design of a blind quantization-based audio watermarking scheme using singular value decomposition

Design of a blind quantization-based audio watermarking sche...

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作者： Bhat K, Vivekananda Das, Ashok Kumar Lee, Jong-Hyouk Center for Cryptography Manipal Institute of Technology Manipal Academy of Higher Education Manipal India Department of Computer Science and Engineering Manipal Institute of Technology Manipal Academy of Higher Education Manipal India Center for Security Theory and Algorithmic Research International Institute of Information Technology Hyderabad India Protocol Engineering Laboratory Sangmyung University Cheonan Korea Republic of

Watermarking is a mechanism in which owner of the audio file hides the watermark information into a audio for various applications. The identity of the owner of the audio file is hidden in the audio, which is known as watermark. In this article, a quantization-based audio watermarking using singular value decomposition (SVD) is proposed. The original audio signal is converted into non overlapping two dimensional matrix blocks. The SVD is applied to each block. The watermark is embedded into audio signal by quantization of largest singular value of the block. The watermark is extracted blindly without using original audio signal. Experimental results show the watermark's high imperceptibility in the audio signal and good performance against Stirmark as well as traditional signal processing attacks. Compared with other audio watermarking methods, our method has higher embedding capacity and robust against various traditional signal processing attacks. © 2019 John Wiley & Sons, Ltd.

关键词： Singular value decomposition

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The Battery Capacity of Energy-storing Quantum Systems

arXiv

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arXiv 2023年

作者： Yang, Xue Yang, Yan-Han Alimuddin, Mir Salvia, Raffaele Fei, Shao-Ming Zhao, Li-Ming Nimmrichter, Stefan Luo, Ming-Xing The School of Information Science and Technology Southwest Jiaotong University Chengdu610031 China School of Computer and Network Security Chengdu University of Technology Chengdu610059 China Department of Physics of Complex Systems S. N. Bose National Center for Basic Sciences Kolkata700106 India Scuola Normale Superiore PisaI-56127 Italy School of Mathematical Sciences Capital Normal University Beijing100048 China Max-Planck-Institute for Mathematics in the Sciences Leipzig04103 Germany Naturwissenschaftlich-Technische Fakultät Universität Siegen Siegen57068 Germany CAS Center for Excellence in Quantum Information and Quantum Physics Hefei230026 China Shenzhen Institute for Quantum Science and Engineering Southern University of Science and Technology Shenzhen518055 China

The quantum battery capacity is introduced in this letter as a figure of merit that expresses the potential of a quantum system to store and supply energy. It is defined as the difference between the highest and the lowest energy that can be reached by means of the unitary evolution of the system. This function is closely connected to the ergotropy, but it does not depend on the temporary level of energy of the system. The capacity of a quantum battery can be directly linked with the entropy of the battery state, as well as with measures of coherence and entanglement. © 2023, CC BY.

关键词： Quantum optics

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CEPS: A Cross-Blockchain based Electronic Health Records Privacy-Preserving Scheme

CEPS: A Cross-Blockchain based Electronic Health Records Pri...

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IEEE International Conference on Communications (ICC)

作者： Sheng Cao Jing Wang Xiaojiang Du Xiaosong Zhang Xiaolin Qin School of Computer Science and Engineering Center for Cyber Security University of Electronic Science and Technology of China China School of Information and Software Engineering Center for Cyber Security University of Electronic Science and Technology of China China Department of Computer and Information Sciences Temple University Philadelphia USA CAS Chengdu Institute of Computer Applications Chengdu China

ISBN: (数字)9781728150895

ISBN: (纸本)9781728150901

The Electronic Health Record (EHR) has been widely used in cloud-based medical data platforms. Since the owner of the EHR is a patient and the manager is a doctor (or hospital), this separation of ownership of privacy data leads to privacy leakages of the EHR stored in the cloud environment. The tamper-proof and traceable features of the blockchain make it a promising way to solve EHR privacy protection issues. Nevertheless, the latest research findings on blockchain-based schemes for EHR privacy preservation are focused on single blockchain that corresponds to a designated medical institution, which are not compatible with the privacy anti-leakage demands since private data is transferred through multiple blockchains. In this regard, we put forward relay-chain as a service (RaaS), and propose a cross-blockchain based EHR Privacy-preserving scheme (CEPS), which uses relay-chain to achieve secure access to EHR data when patients visit different hospitals. Furthermore, our scheme ensures that patients can delete the link of EHR freely and effectively. security analysis and performance evaluations are performed, which prove that CEPS is highly secure and efficient impressively.

关键词： Hospitals Data privacy Privacy DH-HEMTs

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Preface

Lecture Notes in Networks and Systems

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Lecture Notes in Networks and Systems 2024年 966 LNNS卷 xi-xii页

作者： Thampi, Sabu M. Hu, Jiankun Das, Ashok Kumar Mathew, Jimson Tripathi, Shikha School of Computer Science and Engineering Digital University Kerala Kerala Trivandrum India School of Engineering and Information Technology Australian Defence Force Academy The University of New South Wales CanberraACT Australia Center for Security Theory and Algorithmic Research International Institute of Information Technology Telangana Hyderabad India Department of Computer Science and Engineering Indian Institute of Technology Patna Bihar Patna India Department of Electronics and Communication Engineering PES University Karnataka Bengaluru India

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Deep Reinforcement Learning Guided Graph Neural Networks for Brain Network Analysis

arXiv

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arXiv 2022年

作者： Zhao, Xusheng Wu, Jia Peng, Hao Beheshti, Amin Monaghan, Jessica J.M. McAlpine, David Hernandez-Perez, Heivet Dras, Mark Dai, Qiong Li, Yangyang Yu, Philip S. He, Lifang Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China School of Computing Macquarie University Sydney Australia Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing China National Acoustic Laboratories Sydney Australia Department of Linguistics The Australian Hearing Hub Macquarie University Sydney Australia CAEIT Beijing China Department of Computer Science University of Illinois ChicagoIL United States Computer Science & Engineering Lehigh University PA United States

Modern neuroimaging techniques enable us to construct human brains as brain networks or connectomes. Capturing brain networks' structural information and hierarchical patterns is essential for understanding brain functions and disease states. Recently, the promising network representation learning capability of graph neural networks (GNNs) has prompted related methods for brain network analysis to be proposed. Specifically, these methods apply feature aggregation and global pooling to convert brain network instances into vector representations encoding brain structure induction for downstream brain network analysis tasks. However, existing GNN-based methods often neglect that brain networks of different subjects may require various aggregation iterations and use GNN with a fixed number of layers to learn all brain networks. Therefore, how to fully release the potential of GNNs to promote brain network analysis is still non-trivial. In our work, a novel brain network representation framework, BN-GNN, is proposed to solve this difficulty, which searches for the optimal GNN architecture for each brain network. Concretely, BN-GNN employs deep reinforcement learning (DRL) to automatically predict the optimal number of feature propagations (reflected in the number of GNN layers) required for a given brain network. Furthermore, BN-GNN improves the upper bound of traditional GNNs' performance in eight brain network disease analysis tasks. © 2022, CC BY.

关键词： Reinforcement learning

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Heavy metals prediction in coastal marine sediments using hybridized machine learning models with metaheuristic optimization algorithm

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Chemosphere 2024年 352卷 141329页

作者： Yaseen, Zaher Mundher Melini Wan Mohtar, Wan Hanna Homod, Raad Z. Alawi, Omer A. Abba, Sani I. Oudah, Atheer Y. Togun, Hussein Goliatt, Leonardo Ul Hassan Kazmi, Syed Shabi Tao, Hai Civil and Environmental Engineering Department King Fahd University of Petroleum and Minerals Dhahran 31261 Saudi Arabia Interdisciplinary Research Center for Membranes and Water Security King Fahd University of Petroleum & Minerals (KFUPM) Dhahran Saudi Arabia Department of Civil Engineering Faculty of Engineering and Built Environment Universiti Kebangsaan Malaysia UKM Selangor Bangi 43600 Malaysia Environmental Management Centre Institute of Climate Change Universiti Kebangsaan Malaysia Selangor UKM Bangi 43600 Malaysia Department of Oil and Gas Engineering Basrah University for Oil and Gas Basra Iraq Department of Thermofluids School of Mechanical Engineering Universiti Teknologi Malaysia UTM Skudai Johor Bahru 81310 Malaysia Department of Computer Sciences College of Education for Pure Science University of Thi-Qar Nasiriyah 64001 Iraq Information and Communication Technology Research Group Scientific Research Center Al-Ayen University Nasiriyah 64001 Iraq Department of Mechanical Engineering College of Engineering University of Baghdad Baghdad Iraq Computational and Applied Mechanics Department Federal University of Juiz de Fora 36036-900 Brazil Guangdong Provincial Key Laboratory of Marine Disaster Prediction and Prevention and Guangdong Provincial Key Laboratory of Marine Biotechnology Shantou University Shantou 515063 China School of Computer and Information Qiannan Normal University for Nationalities Guizhou Duyun 558000 China Institute of Big Data Application and Artificial Intelligence Qiannan Normal University for Nationalities Guizhou Duyun 558000 China Faculty of Data Science and Information Technology INTI International University 71800 Malaysia

This study proposes different standalone models viz: Elman neural network (ENN), Boosted Tree algorithm (BTA), and f relevance vector machine (RVM) for modeling arsenic (As (mg/kg)) and zinc (Zn (mg/kg)) in marine sediments owing to anthropogenic activities. A heuristic algorithm based on the potential of RVM and a flower pollination algorithm (RVM-FPA) was developed to improve the prediction performance. Several evaluation indicators and graphical methods coupled with visualized cumulative probability function (CDF) were used to evaluate the accuracy of the models. Akaike (AIC) and Schwarz (SCI) information criteria based on Dickey-Fuller (ADF) and Philip Perron (PP) tests were introduced to check the reliability and stationarity of the data. The prediction performance in the verification phase indicated that RVM-M2 (PBAIS = -o.0465, MAE = 0.0335) and ENN-M2 (PBAIS = 0.0043, MAE = 0.0322) emerged as the best model for As (mg/kg) and Zn (mg/kg), respectively. In contrast with the standalone approaches, the simulated hybrid RVM-FPA proved merit and the most reliable, with a 5 % and 18 % predictive increase for As (mg/kg) and Zn (mg/kg), respectively. The study's findings validated the potential for estimating complex HMs through intelligent data-driven models and heuristic optimization. The study also generated valuable insights that can inform the decision-makers and stockholders for environmental management strategies. © 2024 Elsevier Ltd

关键词： Artificial intelligence Heavy metals Sensitivity analysis Soil contamination

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Multi-Layer and Heterogeneous Resource Management in SDN-Based Space-Terrestrial Integrated Networks 22

Multi-Layer and Heterogeneous Resource Management in SDN-Bas...

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22nd IEEE International Conference on High Performance Computing and Communications, 18th IEEE International Conference on Smart City and 6th IEEE International Conference on Data science and Systems, HPCC-SmartCity-DSS 2020

作者： Dong, Peng Gao, Min Tang, Feilong Cao, Lijun Zhang, Xuhui Han, Ping Yang, Yanqin Xu, Wenchao Zhang, Xingjun Shanghai Jiao Tong University Department of Computer Science and Engineering China Material Supply Branch of Shenhua Baoshen Railway Group Co. Ltd China KEDACOM Co. Ltd Innovation Resource Center China Sci-Tech Information Office of Xuzhou Public Security Bureau China East China Normal University School of Information Science and Technology China Xi'An Jiaotong University School of Computer Science and Technology China

ISBN: (纸本)9781728176499

The hierarchical SDN network can effectively integrate the heterogeneous resources of the space-Terrestrial integrated networks through its reconfigurable network management capabilities. In a hierarchical SDN network, how to achieve hierarchical management of heterogeneous controllers is a very important issue. In this paper, we first construct a SDN-based STINs hierarchical architecture to achieve hierarchical management of resources. Then, we extend the SDN standard southbound interface OpenFlow protocol to manage hierarchical resources. Finally, we design a Multipath transmission algorithm (MTA) based on the information provided by the extended OpenFlow protocol to achieve link load balancing. We evaluate our proposed approach with related algorithm. The results demonstrate that our algorithm outperforms related approach in terms of throughput, average end-To-end delay, and load balancing. © 2020 IEEE.

关键词： Software defined networking

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An Efficient Pairing-Free Certificateless Signcryption Without Secure Channel Communication during Secret Key Issuance ∗ 3

An Efficient Pairing-Free Certificateless Signcryption Witho...

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3rd International Conference on Computing and Network Communications, CoCoNet 2019

作者： Karati, Arijit Fan, Chun-I Huang, Jheng-Jia Department of Computer Science and Engineering National Sun Yat-sen University Kaohsiung80424 Taiwan Intelligent Electronic Commerce Research Center National Sun Yat-sen University Kaohsiung80424 Taiwan Information Security Research Center National Sun Yat-sen University Kaohsiung80424 Taiwan Telecom Technology Center Kaohsiung82151 Taiwan

Signcryption is one of the recent public key paradigms that satisfies both the requirement of authenticity and confidentiality of messages between parties. However, most of all the existing schemes use secure channel communication while distributing the partial-private-keys to the users in the network. However, it is not always efficient to establish a secure channel during the interaction between entities. To address this issue, this paper aims to present a new certificateless signcryption scheme that does not consider secure channel communication. The proposed scheme is designed without considering high computation bilinear pairing and map-to-point (MTP) hash function. The scheme can be shown secured in the random oracle model based on the infeasibility elliptic curve discrete logarithm problem (ECDLP). Performance analysis demands better acceptability than other existing ones. © 2020 The Authors. Published by Elsevier B.V.

关键词： Hash functions

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