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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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FFFN: Fashion Feature Fusion Network by Co-Attention Model for Fashion Recommendation 48

FFFN: Fashion Feature Fusion Network by Co-Attention Model f...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Lin, Zhantu Zhang, Xiaoyan Shenzhen University College of Computer Science and Software Engineering Shenzhen China

ISBN: (纸本)9781728163277

Fashion complementary recommendation has always been crucial in the field of recommendation. In previous work, researchers did not pay attention to the connection and combinability between multi-dimensional image features of fashion items. To effectively utilize the advantages of high-level and low-level features in images, we propose a Fashion Feature Fusion Network (FFFN) to solve the fashion complementary recommended tasks, which extracts and combines the features of different dimensions in the neural network into a fusion feature. Then, we input the fused image features and the category text features of fashion items into the co-attention module to realize the guidance of the text attention information to the image attention information. Experimental results on different datasets show that our model has advantages compared to the state-of-the-art methods. © 2023 IEEE.

关键词： Co-attention Fashion complementary recommendation Feature fusion

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An interval RSP-based ensemble model for big data analysis 29

An interval RSP-based ensemble model for big data analysis

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29th International DMS Conference on Visualization and Visual Languages, DMSVIVA 2023

作者： Cai, Wenzhu Ao, GengYuan Lin, YiGang Li, Mark Junjie College of Computer Science and Software Engineering Shenzhen University Shenzhen China

ISBN: (纸本)1891706578

Ensemble learning for big data has been successful in machine learning and has great advantages over other learning methods. The ensemble model based on Random Sample Partition (RSP) is a prominent method of it. Although the RSP data blocks have the consistent probability distribution function as the whole data, there is some uncertainty in prediction results due to the non-overlapping data between blocks. In this paper, we propose a novel interval ensemble model based on RSP named Inr-RSP, which maps prediction results to interval-valued data by interval modeling and then uses the IAA aggregation method to convert the interval-valued data into fuzzy sets to get a more accurate and stable final result. The experimental classification results from four real datasets also show that the performance of this model is better than that of the traditional RSP ensemble model. And the IAA method usage has a stronger ability to capture uncertainty in prediction than the common majority voting method. Copyright © 2023 by KSI Research Inc. and Knowledge Systems Institute, USA.

关键词： Learning systems

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Legal Judgment Prediction Incorporating Guiding Cases Matching 12th

Legal Judgment Prediction Incorporating Guiding Cases Matchi...

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12th National CCF Conference on Natural Language Processing and Chinese Computing, NLPCC 2023

作者： Li, Hengzhi Cai, Shubin Ming, Zhong College of Computer Science and Software Engineering Shenzhen University Shenzhen China

ISBN: (纸本)9783031446924

Legal judgment prediction aims to predict the judgment result based on the case fact description. It is an important application of natural language processing within the legal field. To enhance the impartiality and consistency of the judiciary, the Supreme People’s Court selects representative cases and categorizes them as guiding or typical cases. However, the current research is based on the unified training of large-scale datasets, and does not distinguish the value of different existing cases. To address these issues, this paper collects relevant guiding cases and typical cases, designs a legal judgment prediction method that incorporates matching guiding cases, and proposes a legal judgment prediction model called MAGIC (MAtching GuIding Cases), based on multi-feature fusion. The goal of this research is to improve the prediction performance of legal judgments by utilizing the value of high-quality cases. The experimental results on the CAIL2018 evaluation datasets demonstrate that the proposed method significantly improves the performance of legal judgment prediction models. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Forecasting

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Multichannel Seismic Deconvolution via 2D K-SVD and Convolutional Sparse Coding 3

Multichannel Seismic Deconvolution via 2D K-SVD and Convolut...

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3rd International Meeting for Applied Geoscience and Energy, IMAGE 2023

作者： Zhang, Guiqian Gao, Xiayu Zou, Bangli Wang, Yaojun Chen, Yingzhu China College of Computer Science and Software Engineering Shenzhen University China

Seismic deconvolution encounters problems of multiplicity and instability. The regularization technique based on various prior knowledge is used to constrain the range of solutions and improve the stability of deconvolution results. We use dictionary learning as a technical means to consider both vertical resolution and lateral continuity issues in reflectivity inversion problem. Considering the drawbacks of patch-based operations in 1D dictionary learning, we adopt 2D K-SVD and convolutional sparse coding (CSC) based on global feature extraction as the dictionary learning methods. We divide the seismic profile into low-frequency component and high-frequency component, and reconstruct their corresponding reflectivity with 2D K-SVD and CSC, respectively. Finally, the seismic deconvolution with high resolution and strong continuity is obtained by summing the results of each component deconvolution. The effectiveness of the proposed method is verified by Marmousi model data. © 2023 Society of Exploration Geophysicists and the American Association of Petroleum Geologists.

关键词： Reflection

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SOID: Towards an Efficient Incremental Deployment Scheme for Source Address Validation 29

SOID: Towards an Efficient Incremental Deployment Scheme for...

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29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Yang, Shu Song, Bingqian Cui, Laizhong Shenzhen University College of Computer Science and Software Engineering Shenzhen China

ISBN: (纸本)9798350330717

The current Internet makes forwarding decisions based on only destination addresses, leading to a prevalence of IP source address spoofing. To mitigate the risks posed by IP spoofing, Source Address Validation in Intra-domain and Interdomain Networks (SAVNET) has been recently proposed and become a hot topic in both academia and industry. While all network equipment can not be upgraded to support SAVNET during one night, incremental deployment is needed for network operators. However, SAVNET can not defend against all spoofing attacks under partial deployment. Thus, we need to carefully choose nodes to be deployed, to improve incentive benefits during incremental deployment. In this paper, we formulate the incremental problem and prove that the problem is NP-Complete. To efficiently solve the problem, we propose a heuristic deployment scheme named SOID (SAV protocol optimized incremental deployment). The intuitive idea of SOID is using the sink-tree to get the detectable flow sets of each router. Then, it selects the routers which have the maximum total weight during each iteration. To evaluate the performance of the proposed algorithm, we conduct comprehensive simulations with generated and real topologies. The simulation results show that SOID performs much better compared with traditional schemes, such as random deployment and minimum vertex cover algorithms, with a manageable overhead. © 2023 IEEE.

关键词： Incremental Deployment Algorithm Intra-domain Source Address Validation

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Knowledge Distillation with Active Exploration and Self-Attention Based Inter-Class Variation Transfer for Image Segmentation 48

Knowledge Distillation with Active Exploration and Self-Atte...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Zhang, Yifan Li, Shaojie Yang, Xuan Shenzhen University College of Computer Science and Software Engineering Shenzhen China

ISBN: (纸本)9781728163277

Knowledge distillation (KD) aims to distill the knowledge from a more extensive deep neural network into a small net-work without losing validity. This paper proposes a novel approach with active exploration and passive transfer (AEPT) and self-attention-based inter-class feature variation (AIFV) distillation for the cardiac image segmentation task. The AEPT encourages the student model to learn undiscovered knowledge/features of the teacher model, aiming to explore new features outperforming the teacher. More specifically, we focus on where the teacher performs poorly and encourage the student model to explore complementary features different from the teacher model. To improve the distinguishability of the student for different classes, the student learns the self-attention-based feature variation (AIFV) between classes. Extensive experiments on medical and public image datasets demonstrate that our approach makes the student model learn better representations and outperforms state-of-art methods by combining two knowledge distillation strategies. © 2023 IEEE.

关键词： Distillation

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LLMaaS: Serving Large-Language Models on Trusted Serverless Computing Platforms

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第2期6卷 405-415页

作者： Cai, Zinuo Ma, Rongbo Fu, Yicheng Zhang, Weishan Ma, Ruhui Guan, Haibing Shanghai Jiao Tong University School of Electronic Information and Electrical Engineering Shanghai200240 China Qingdao Institute of Software College of Computer Science and Technology Qingdao266555 China

In recent years, the emergence of large-language models (LLMs) has profoundly transformed our production and lifestyle. These models have shown tremendous potential in fields, such as natural language processing, speech recognition, and recommendation systems, and are increasingly playing crucial roles in applications such as human-computer interaction and intelligent customer service. Efficient inference solutions for LLMs in data centers have been extensively researched, with a focus on meeting users' quality of service requirements. In this article, we focus on two additional requirements that responsible LLM inference should meet under QoS conditions: security throughout the model execution process and low maintenance requirements for the inference system. Therefore, we propose LLMaaS, a trusted model inference platform based on a serverless computing platform aimed at providing inference as a service for LLMs. First, we design a trusted serverless computing platform based on software guard extension (SGX), which includes distributed identity verification and SGX device plugins to ensure the security and trustworthiness of the inference process. Additionally, to reduce the maintenance requirements of the system, we enhance the SGX-based deep learning computing framework, including replacing PyTorch and using a greedy algorithm for graph partitioning. We conduct tests on four typical large models, and the experimental results demonstrate that, with minimal overhead and user code modifications, we can ensure the security of model execution. © 2020 IEEE.

关键词： Quality of service

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Role of Machine Learning in Power Analysis Based Side Channel Attacks on FPGA

Role of Machine Learning in Power Analysis Based Side Channe...

引用

2023 International Conference on Robotics and Automation in Industry, ICRAI 2023

作者： Hasnain, Ali Asfia, Yame Khawaja, Sajid Gul College of EME Department of Computer and Software Engineering NUST Islamabad Pakistan

ISBN: (纸本)9781665464727

The cloud-based devices face many threats these days due to shared resources like power. The attacker measures the power by using some remote sensors which are present in attacker tenants. These sensors get partial or full access to a power distribution network (PDN) and work as a backdoor for the attacker. In our research, we explored potential security issues which involved power analysis-based side-channel attacks (SCAs) on Field Programmable Gate Arrays (FPGAs). We have made three major contributions to our research paper. First, we have discussed the power analysis or power profiling of FPGA, which is dependent upon voltage fluctuations' leakage while performing some encryption tasks. The voltage fluctuations of the cryptographic module are measured by some physical source like an oscilloscope or remote source like delay line sensors. Second, we have discussed potential power analysis-based SCAs that used these measurements of voltage fluctuations to extract the secret key. Third, we have designed a framework based on machine learning (ML) and deep learning (DL) models to perform secret key predictions and successful attacks. Firstly, our custom convolutional neural networks (CNN) model has revealed all 16 bytes of the secret key and performed a successful attack with only 570 attack power traces. Secondly, the multi-layer perceptron (MLP) model has successfully attacked only using 3200 traces using the same framework. Overall we have achieved a better performance in terms of the required number of power traces for a successful attack, training time, prediction time, and attack time. © 2023 IEEE.

关键词： Neural network models

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TD-SAS: A Trust-Aware and Decentralized Speed Advisory System for Energy-Efficient Autonomous Vehicle Platoons

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2023年第9期9卷 1-16页

作者： Li, Shike Li, Jianbin Liang, Yin Zhang, Hengyang Wu, Sixing Wang, Shen Cheng, Long School of Control and Computer Engineering North China Electric Power University Beijing China School of Computer Science University College Dublin Ireland

Autonomous vehicle platooning benefits significantly from the Consensus Speed Advisory System (CSAS), an emerging technology that recommends a consensus speed to reduce energy consumption. However, managing trust to ensure system security and identify malicious nodes poses a considerable challenge in these autonomous environments. Furthermore, while CSAS optimizes the total energy consumption of the platoon, it may inadvertently result in increased energy use for specific vehicles, discouraging their continued participation and hindering an efficient formation of a platoon. This paper proposes a trust-aware and decentralized speed advisory system (TD-SAS) to address these challenges. TD-SAS employs a consortium blockchain for managing trust nodes, providing non-repudiation and tamper resistance for reputation data. Capitalizing on this platform, we use a multi-weight subjective logic model for precise reputation value calculation. Additionally, we present a trust-aware consensus speed recommendation scheme capable of adapting its recommendations to vehicular reputation variations. To mitigate the potential disincentives for vehicles experiencing increased energy consumption, we incorporate an incentive mechanism to encourage their re-engagement with TD-SAS. Comprehensive security analysis and extensive simulation experiments confirm the robustness and effectiveness of TD-SAS, emphasizing its potential for enhancing the energy efficiency and security of autonomous vehicle platoons. IEEE

关键词： Blockchain

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