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Next-Generation Cybersecurity Solution: A Decentralized Ransomware Recovery Network (DRRN) with Secret Sharing

SN computer science 2025年第5期6卷 1-22页

作者： Ali, Sijjad Wang, Jia Leung, Victor C. M. Ali, Asad College of Computer Science and Software Engineering Shenzhen University Yuehai Campus Guangdong Shenzhen 518060 China College of Electronics and information Engineering Shenzhen University Yuehai Campus Guangdong Shenzhen 518060 China

The increasing frequency of ransomware attacks, with a reported growth rate of 59%, has exposed significant vulnerabilities in existing cybersecurity measures. Traditional defenses, such as routine data backups and endpoint protection systems, often fall short against sophisticated ransomware that encrypts data and demands exorbitant payments for decryption keys. These limitations are exacerbated by attackers leveraging centralized storage systems and static recovery methods, making data recovery costly, unreliable, and time-consuming. To address this pressing issue, this paper proposes a novel Decentralized Ransomware Recovery Network, which leverages decentralized storage and secret sharing theory to enhance data security and recovery. The proposed scheme introduces a decentralized encryption/decryption framework that distributes data and keys across the network, reducing the economic incentive for attackers by enabling secure and reliable data recovery without ransom payments. At its core, the system utilizes Shamir’s Secret Sharing Scheme, where encryption keys are divided into shares and distributed among nodes. In the event of an attack, these shares are reconstructed to restore access to the encrypted files. Additionally, the proposed scheme incorporates a dynamic key reconfiguration mechanism, where keys are updated after each recovery process to thwart repeated attacks. Through extensive simulations and evaluations under various scenarios, the proposed scheme demonstrates superior performance in terms of recovery efficiency, security resilience, and adaptability, achieving a significant reduction in attack success rates. By offering a robust and preventative defense mechanism, this research highlights the novelty and practicality of the proposed scheme as an effective cybersecurity strategy against ransomware. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Cybersecurity defense Decentralized Ransomware Recovery Network (DRRN) Ransomware Resilience Secret sharing schemes

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Camouflaged Variational Graph AutoEncoder against Attribute Inference Attacks for Cross-Domain Recommendation

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IEEE Transactions on Knowledge and Data engineering 2025年第07期37卷 3916-3932页

作者： Xiong, Yudi Guo, Yongxin Pan, Weike Yang, Qiang Ming, Zhong Zhang, Xiaojin Yu, Han Lin, Tao Tang, Xiaoying Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China School of Science and Engineering Shenzhen518172 China Hong Kong University of Science and Technology Department of Computer Science and Engineering Hong Kong WeBank Shenzhen China Huazhong University of Science and Technology School of Computer Science and Technology Wuhan430074 China Nanyang Technological University School of Computer Science and Engineering 639798 Singapore Westlake University School of Engineering Hangzhou China

Cross-domain recommendation (CDR) aims to alleviate the data sparsity problem by leveraging the benefits of modeling two domains. However, existing research often focuses on the recommendation performance while ignores the privacy leakage issue. We find that an attacker can infer user attribute information from the knowledge (e.g., user preferences) transferred between the source and target domains. For example, in our experiments, the average inference accuracies of attack models on gender and age attributes are 0.8323 and 0.3897. The best-performing attack model achieves accuracies of 0.8847 and 0.4634, exceeding a random inference by 25.10% and 64.04%. We can see that the leakage of user attribute information may significantly exceed what would be expected from random inference. In this paper, we propose a novel recommendation framework named CVGAE (short for camouflaged variational graph autoencoder), which effectively models user behaviors and mitigates the risk of user attribute information leakage at the same time. Specifically, our CVGAE combines the strengths of VAEs in capturing latent features and variability with the ability of GCNs in exploiting high-order relational information. Moreover, to ensure against attribute inference attacks without sacrificing the recommendation performance, we design a user attribute protection module that fuses user attribute-camouflaged information with knowledge transfer during cross-domain processes. We then conduct extensive experiments on three real-world datasets, and find our CVGAE is able to achieve strong privacy protection while making little sacrifices in recommendation accuracy. © 1989-2012 IEEE.

关键词： Recommender systems

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Research on Visual Slam Technology for UAV Positioning and Navigation 21

Research on Visual Slam Technology for UAV Positioning and N...

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21st International computer Conference on Wavelet Active Media Technology and Information Processing, ICCWAMTIP 2024

作者： Wang, Zhou Zhihao, Liu Ying, Tian Yujun, Yang School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China School of Computer and Software Engineering Xihua University Chengdu China Sichuan Police College Intelligent Policing Key Laboratory of Sichuan Province LuZhou China College of Computer and Artificial Intelligence Software College Huaihua University Huaihua China

ISBN: (纸本)9798331519254

UAV (Unmanned Aerial Vehicle) navigation can be considered as the process of robots that determine how to successfully and quickly reach the target location. Specifically, in order to complete the scheduled task successfully, the UAV must accurately understand its status, such as position, navigation speed, heading, as well as the starting point and target position. Moreover, it mainly depends on the current environment and location. This article generally focuses on the research on visual SLAM (Simultaneous localization and mapping) for UAV positioning and navigation. © 2024 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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A Systematic Review of Causal Analysis in Power Systems: Principles, Application, and Prospects 7

A Systematic Review of Causal Analysis in Power Systems: Pri...

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7th IEEE International Electrical and Energy Conference, CIEEC 2024

作者： Sun, Xiaorong Wang, Xuanheng Yan, Bincheng Wang, Baoqun Pan, Xueping Guo, Jinpeng School of Electrical and Power Engineering Hohai Univeristy Nanjing China College of Computer Science and Software Engineering Hohai University Nanjing China

ISBN: (纸本)9798350359558

Causal relationships are a scientific research method used to describe relationships between variable data, allowing for the excavation of the deep logic and operational mechanisms behind phenomena. The integration of causal relationships as a means of data analysis in the field of power systems improves the accuracy and efficiency of power systems analysis and operation. This paper presents the definition and origin of causal relationships and the principles of main causal analysis models. It overviews the application of causal relationships in power systems fault diagnosis, energy prediction, stability analysis, and the electricity market. Finally, it looks forward to the development prospects of causal relationships in combination with power physics models and artificial intelligence, promoting the construction of a reliable and intelligent new power systems under the penetration of renewable sources. © 2024 IEEE.

关键词： Systems analysis

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Student’s Health Exercise Recognition Tool for E-Learning Education

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Intelligent Automation & Soft Computing 2023年第1期35卷 149-161页

作者： Tamara al Shloul Madiha Javeed Munkhjargal Gochoo Suliman AAlsuhibany Yazeed Yasin Ghadi Ahmad Jalal Jeongmin Park Department of Humanities and Social Science Al Ain UniversityAl Ain15551UAE Department of Computer Science Air UniversityIslamabad44000Pakistan Department of Computer Science and Software Engineering United Arab Emirates UniversityAl Ain15551UAE Department of Computer Science College of ComputerQassim UniversityBuraydah51452Saudi Arabia Department of Computer Science and Software Engineering Al Ain UniversityAl Ain15551UAE Department of Computer Engineering Korea Polytechnic UniversitySiheung-siGyeonggi-do237Korea

Due to the recently increased requirements of e-learning systems,multiple educational institutes such as kindergarten have transformed their learning towards virtual *** student health exercise is a difficult task but an important one due to the physical education needs especially in young *** proposed system focuses on the necessary implementation of student health exercise recognition(SHER)using a modified Quaternion-basedfilter for inertial data refining and data fusion as the pre-processing ***,cleansed data has been segmented using an overlapping windowing approach followed by patterns identification in the form of static and kinematic signal ***,these patterns have been utilized to extract cues for both patterned signals,which are further optimized using Fisher’s linear discriminant analysis(FLDA)***,the physical exercise activities have been categorized using extended Kalmanfilter(EKF)-based neural *** system can be implemented in multiple educational establishments including intelligent training systems,virtual mentors,smart simulations,and interactive learning management methods.

关键词： E-learning exercise recognition online physical education student’s healthcare

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Density estimation-based method to determine sample size for random sample partition of big data

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Frontiers of computer science 2024年第5期18卷 57-70页

作者： Yulin HE Jiaqi CHEN Jiaxing SHEN Philippe FOURNIER-VIGER Joshua Zhexue HUANG Guangdong Laboratory of Artificial Intelligence and Digital Economy(SZ) Shenzhen 518107China College of Computer Science and Software Engineering Shenzhen UniversityShenzhen 518060China Department of Computing and Decision Sciences Lingnan UniversityHong Kong 999077China

Random sample partition(RSP)is a newly developed big data representation and management model to deal with big data approximate computation *** research and practical applications have confirmed that RSP is an efficient solution for big data processing and ***,a challenge for implementing RSP is determining an appropriate sample size for RSP data *** a large sample size increases the burden of big data computation,a small size will lead to insufficient distribution information for RSP data *** address this problem,this paper presents a novel density estimation-based method(DEM)to determine the optimal sample size for RSP data ***,a theoretical sample size is calculated based on the multivariate Dvoretzky-Kiefer-Wolfowitz(DKW)inequality by using the fixed-point iteration(FPI)***,a practical sample size is determined by minimizing the validation error of a kernel density estimator(KDE)constructed on RSP data blocks for an increasing sample ***,a series of persuasive experiments are conducted to validate the feasibility,rationality,and effectiveness of *** results show that(1)the iteration function of the FPI method is convergent for calculating the theoretical sample size from the multivariate DKW inequality;(2)the KDE constructed on RSP data blocks with sample size determined by DEM can yield a good approximation of the probability density function(p.d.f);and(3)DEM provides more accurate sample sizes than the existing sample size determination methods from the perspective of *** demonstrates that DEM is a viable approach to deal with the sample size determination problem for big data RSP implementation.

关键词： random sample partition big data sample size Dvoretzky-Kiefer-Wolfowitz inequality kerneldensity estimator probability density function

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Lunar crater identification based on SA-BRU-Net 8

Lunar crater identification based on SA-BRU-Net

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8th International Conference on Electrical, Mechanical and computer engineering, ICEMCE 2024

作者： Lu, Youbei Wei, Ersa Chen, Jiaqi Liu, Xiangmei Hohai University College of Information Science and Engineering Changzhou China Hohai University College of Computer Science and Software Engineering Changzhou China Hohai University College of Artificial Intelligence and Automation Changzhou China

ISBN: (纸本)9798331506230

Identification of crater impacts is crucial during lunar exploration. Previous methods primarily rely on visual interpretation, which is time-consuming and lacks objective standards. Moreover, these methods often fail to accurately identify small craters. To address these issues, we propose a novel convolutional neural network, SA-BRU-Net (Spatial Attentive Bottleneck Residual U-Net). Building upon U-Net, SA-BRU-Net introduces spatial attention mechanisms and employs bottleneck residual blocks instead of traditional residual blocks. These improvements reduce network parameters while enhancing the identification accuracy of lunar craters. Experimental results demonstrate that our model achieves a recall rate of 81.38% and a precision of 79.03% on DEM images, outperforming other network models. © 2024 IEEE.

关键词： Convolutional neural networks

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Tomato Leaf Diseases Prediction Using Deep Learning Algorithms

Tomato Leaf Diseases Prediction Using Deep Learning Algorith...

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2024 International Conference on Advances in Data engineering and Intelligent Computing Systems, ADICS 2024

作者： Ananthi, P. Devi, K. Nirmala Gopinath, D. Shanmugapriya, P. Gowtham, S. Kongu Engineering College Department Of Computer Technology Tamilnadu Perundurai India Bangaluru India Kristu Jayanti College Department Of Computer Science Bangaluru India Software Systems Kongu Engineering College Erode India

ISBN: (纸本)9798350364828

In the realm of agriculture, infections on tomato leaves pose a worldwide danger to established tomato production, impacting a large number of farmers worldwide. To ensure healthy tomato plant growth and food security for the world's expanding population, early disease detection, treatment, and solution identification are essential. The use of computer-assisted technologies for disease detection in plant leaf is prevalent in modern agriculture. The goal was to develop an effective deep learning model for the detection of diseases in tomato plant leaf. To address memory and computational resource constraints, the proposed model incorporates a lightweight 'Region Convolutional Neural Network (R-CNN)' head and conducted experiments with various neural network architectures, including Densenet-169, optimized Mobilenet-V2 and Resnet-50, to enhance detection accuracy and computational efficiency. The research extends the capabilities of these neural network architectures by optimizing anchor proportions in the Conventional Neural Network (CNN) and modifying feature extraction topologies. These optimizations resulted in improved detection accuracy and computational efficiency and compared proposed technique against cutting-edge models to evaluate its viability and robustness. The outcomes of the research were promising. Impressive outcomes were obtained by the proposed approach regarding accuracy, F1-score, recall, and precision. © 2024 IEEE.

关键词： Computational efficiency

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Joint-Wise Distributed Perception Graph Convolutional Network for Skeleton-Based Action Recognition

Joint-Wise Distributed Perception Graph Convolutional Networ...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Huang, Qian Geng, Qiang Chen, Zhaoyu Li, Xin Li, Yangyang Li, Xing College of Computer Science and Software Engineering Hohai University China College of Information Science and Technology & Artificial Intelligence Nanjing Forestry University China

ISBN: (纸本)9798350368741

Recent studies have achieved remarkable results for action recognition with human skeletal data by utilizing graph convolutional models. Traditional approaches typically aggregate local spatio-temporal information bottom-up to form a single spatio-temporal global understanding. However, this method may fail to achieve multi-location perception for fine-grained action capture and may struggle to model subactions due to their varying durations. To solve these challenges, we design a Distributed Spatio-temporal Perception (DSP) module that innovatively treats each joint as an independent perception unit, performing joint-wise distributed multi-location perception in both spatial and temporal dimensions. In addition, we introduce an Anchor Pose-driven Subaction Encoding (APSE) module, which enhances informative clues for subaction reasoning through identifying the correlations between anchor pose and subactions formed by integrating distributed spatio-temporal perception features. Based on the above work, we propose a Joint-wise Distributed Perception Graph Convolutional Network (JDP-GCN). The experiments on three widely used datasets: NTU RGB+D 60, NTU RGB+D 120, and NW-UCLA, demonstrate that our method achieves state-of-the-art performance. © 2025 IEEE.

关键词： Distributed Perception Graph Convolutional Network Human Action Recognition Skeleton Modality

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Optimization of Heliostat Field Arrangement Model Based on Geometric Relationship and Particle Swarm Algorithm 4

Optimization of Heliostat Field Arrangement Model Based on G...

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4th IEEE International Conference on Power, Electronics and computer Applications, ICPECA 2024

作者： Liu, Zhishuai Wei, Zhengyang Li, Jiangnan School of Mathematics Hohai University Nanjing China College of Computer Science and Software Engineering Hohai University Nanjing China

ISBN: (纸本)9798350359589

With the continuous progress of photovoltaic (PV) technology and the steady reduction of related costs, solar energy, as an important renewable energy source, shows an increasingly strong competitiveness in energy market competition. In the tower solar thermal power station, the arrangement of the heliostat field directly affects the power generation efficiency of the tower power generation system as well as the working cost. Therefore, this paper presents a model based on geometric relationship and particle swarm algorithm for the optimization of the heliostat field arrangement, mathematical modeling and calculating cosine efficiency, truncation efficiency, etc., and effectively improves the output thermal power as well as the optical efficiency of the heliostat field. Geometric planning is utilized to determine the location of the absorption tower, the coordinates of the heliostat arrangement and other layout parameters to optimize the layout of the tower solar system. Drawing on the idea of clustering, the model analyzes the characteristics of the heliostat mirrors in the same region, uses same or similar parameters to minimize the cost of computation, improving the overall optical efficiency of the mirror field. The particle swarm algorithm is utilized to solve the parameters such as mirror length and mirror width of the heliostat to get the suitable size of the heliostat for the heliostat mirror field. After completing all the calculation and optimization steps, the final solution of the model is carried out in this paper. The layout scheme of the heliostat field optimized by the implementation of the model gets a significant performance improvement. Specifically, the average annual thermal power output of the heliostat field is improved by 33.2309 MW, while the average annual optical efficiency is also improved by 43.2%. These improvements effectively enhance the power generation efficiency of the whole system, confirming the effectiveness of the optimization

关键词： Campo arrangement method Geometric relationship Mirror field optimization arrangement Particle swarm algorithm Spatial transformation

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