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SSRN 2023年

作者： Zhao, Zhen Susilo, Willy Guo, Fuchun Lai, Jianchang Wang, Baocang State Key Laboratory of Integrated Service Networks Xidian University Xi’an710071 China Institute of Cybersecurity and Cryptology School of Computing and Information Technology University of Wollongong Wollongong2522 Australia School of Cyber Science and Engineering Southeast University Nanjing211189 China

Accountable identity-based encryption (A-IBE) was proposed to relieve the key escrow problem caused by the fully trustworthy private-key generator (PKG) in the IBE system, where the true generator of private keys or decoder boxes can be traced back to the PKG or related users. Retrievable A-IBE (RA-IBE) enhances the security of A-IBE by providing retrievability to the master secret key of the PKG when more than one private key of the same user are released. RA-IBE strengthens the deterrent effect of A-IBE against the PKG since disclosure of the master secret key could lead to the breakdown of the entire IBE system. However, current RA-IBE schemes only provide retrievability in a white-box model, which limits the ability to support traceability and retrievability on well-formed private keys only. This overlooks the fact that a malicious PKG can easily conceal a private key within a decoder box, making the inserted private key inaccessible. To overcome this limitation, we propose a full black-box RA-IBE scheme, where traceability and retrievability of decoder boxes are provided while the malicious PKG is allowed to access the decryption oracle in the security model simultaneously. We first give the formal definition and security models of full black-box RA-IBE and then present a concrete construction. In our construction, a user interacts with the PKG to obtain its private key and an additional commitment tuple with which the user can retrieve the master secret key of the PKG using a related decoder box generated by the PKG. Finally, we show that the proposed full black-box RA-IBE scheme is secure in the random oracle model. © 2023, The Authors. All rights reserved.

关键词： Decoding

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Finding the proper node ranking method for complex networks

arXiv

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arXiv 2018年

作者： Yu, Senbin Gao, Liang Wang, Yi-Fan Institute of Transportation Systems Science and Engineering MOE Key Laboratory of Urban Transportation System Theory and Technology State Key Laboratory of Rail Traffic Control and Safety Center of Cooperative Innovation for Beijing Metropolitan Transportation Beijing Jiaotong University Beijing100044 China Yinchuan Municipal Bureau of Big Data Management and Service Yinchuan750011 China

Ranking node importance is crucial in understanding network structure and function on complex networks. Degree, h-index and coreness are widely used, but which one is more proper to a network associated with a dynamical process, e.g. SIR spreading process, is still unclear. To fill this gap, a, which is extracted from the fitting 1 function (f(x) = 1 − e2a(x−b)+1) of the average number of nodes in each radius of the neighborhood of a node, is proposed. Experiment results which are carried out on twenty real-world networks show that a can classify which of the three measures (degree, h-index and coreness) is more proper to a network in ranking node importance. We also find that [b/3] is a good indicator for forecasting the optimal radius of the neighborhood of a node in ranking node importance for a given network. To the best of our knowledge, it is the first solution of this interesting and open issue. Furthermore, by extending the range of neighborhood where we construct an operator ℋ on of a node, we propose a new method to quantify the importance of a node. The ranking accuracies of most networks can be improved when the radius is increased from 0 to its forecasting optimal radius and the improvement, for the best case, reaches up to 111%. The performances will reduce on half of the networks studied in this paper if we roughly extend the radius of the neighborhood. Our work deepens the understanding of how to find out the proper node ranking method for complex networks. The proposed methods bridge the gaps among network structure and node importance, and may have potential applications in controlling the outbreak of disease, designing of optimal information spreading *** Codes Game theory, economics, social and behavioral sciences Copyright © 2018, The Authors. All rights reserved.

关键词： Complex networks

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Local articulation points in complex networks

arXiv

引用

arXiv 2018年

作者： Yu, Senbin Gao, Liang Song, Rongqiu Institute of Transportation Systems Science and Engineering MOE Key Laboratory of Urban Transportation System Theory and Technology State Key Laboratory of Rail Traffic Control and Safety Center of Cooperative Innovation for Beijing Metropolitan Transportation Beijing Jiaotong University Beijing100044 China Yinchuan Municipal Bureau of Big Data Management and Service Yinchuan750011 China

An articulation point (AP) is any node whose removal increases the number of connected components of a graph. There is no doubt that this kind of node which occupies a non-ignorable fraction of real-world networks plays a key role in ensuring the connectivity. However, we should not thus neglect the impacts of non-APs nodes. In this paper, we define a local AP (LAP) whose removal will increase the number of connected components within its r-step neighborhood. Through investigating the fraction of LAPs in forty-five real networks, we find a critical proportion Scr, which is equal to 0.5 (S = r/D, D is the diameter of a network), and this result can also be turned out in ER networks. In addition, we present a unique advantage of LAPs in dismantling networks under the process of targeted attack, compared with APs, which provide another way of thinking to improve the calculation efficiency of APs and design better targeted attack strategy of network *** Codes Game theory, economics, social and behavioral sciences Copyright © 2018, The Authors. All rights reserved.

关键词： Complex networks

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Enhancing Security and Privacy in Federated Learning using Low-Dimensional Update Representation and Proximity-Based Defense

arXiv

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arXiv 2024年

作者： Li, Wenjie Fan, Kai Zhang, Jingyuan Li, Hui Lim, Wei Yang Bryan Yang, Qiang The State Key Laboratory of Integrated Service Networks Xidian University Xi’an710071 China The College of Computing and Data Science Nanyang Technological University 639798 Singapore The Department of Computer Science and Engineering Hong Kong University of Science and Technology 999077 Hong Kong

Federated Learning (FL) is a promising privacy-preserving machine learning paradigm that allows data owners to collaboratively train models while keeping their data localized. Despite its potential, FL faces challenges related to the trustworthiness of both clients and servers, particularly against curious or malicious adversaries. In this paper, we introduce a novel framework named Federated Learning with Low-Dimensional Update Representation and Proximity-Based defense (FLURP), designed to address privacy preservation and resistance to Byzantine attacks in distributed learning environments. FLURP employs LinfSample method, enabling clients to compute the l∞ norm across sliding windows of updates, resulting in a Low-Dimensional Update Representation (LUR). Calculating the shared distance matrix among LURs, rather than updates, significantly reduces the overhead of Secure MultiParty Computation (SMPC) by three orders of magnitude while effectively distinguishing between benign and poisoned updates. Additionally, FLURP integrates a privacy-preserving proximity-based defense mechanism utilizing optimized SMPC protocols to minimize communication rounds. Our experiments demonstrate FLURP’s effectiveness in countering Byzantine adversaries with low communication and runtime overhead. FLURP offers a scalable framework for secure and reliable FL in distributed environments, facilitating its application in scenarios requiring robust data management and security. Copyright © 2024, The Authors. All rights reserved.

关键词： Federated learning

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TS-ENAS:Two-Stage Evolution for Cell-based Network Architecture Search

arXiv

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arXiv 2023年

作者： Zou, Juan Wu, Shenghong Xia, Yizhang Jiang, Weiwei Wu, Zeping Zheng, Jinhua Engineering Research Center of Hunan Province for Optimization and Security of Intelligent System Key Laboratory of Intelligent Computing and Information Processing Ministry of Education of China Key Laboratory of Hunan Province for Internet of Things and Information Security Xiangtan University Hunan Province Xiangtan411105 China The College of Aerospace Science and Engineering National University of Defense Technology Hunan Changsha410073 China

Neural network architecture search provides a solution to the automatic design of network structures. However, it is difficult to search the whole network architecture directly. Although using stacked cells to search neural network architectures is an effective way to reduce the complexity of searching, these methods do not able find the global optimal neural network structure since the number of layers, cells and connection methods is fixed. In this paper, we propose a Two-Stage Evolution for cell-based Network Architecture Search(TS-ENAS), including one-stage searching based on stacked cells and second-stage adjusting these cells. In our algorithm, a new cell-based search space and an effective two-stage encoding method are designed to represent cells and neural network structures. In addition, a cell-based weight inheritance strategy is designed to initialize the weight of the network, which significantly reduces the running time of the algorithm. The proposed methods are extensively tested and compared on four image classification dataset, Fashion-MNIST, CIFAR10, CIFAR100 and ImageNet and compared with 22 state-of-the-art algorithms including hand-designed networks and NAS networks. The experimental results show that TS-ENAS can more effectively find the neural network architecture with comparative performance. Copyright © 2023, The Authors. All rights reserved.

关键词： Image classification

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Coupling Process between Droplet and Iron Investigated by Reactive Molecular Dynamics Simulations

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ACS omega 2024年第18期9卷 20410-20424页

作者： Hang Dong Can Zhao Na Kong Yu Zhou Jianping Zhou School of Mechanical Engineering Xinjiang University Urumqi 830046 China. Xinjiang Uygur Autonomous Region Science and Technology Project Service Center Urumqi 830011 China. State Key Laboratory of Mechanical System and Vibration School of Mechanical Engineering Shanghai Jiao Tong University Shanghai 200240 China.

The droplet-to-iron electrochemical reaction is common in nature and industrial production, and it causes damage to the economy, safety, and the environment. The electrochemical reaction of droplet-to-iron is a coupling process of wetting and corrosion. Presently, investigations into electrochemical reactions mainly focus on the corrosions caused by a solution, and wetting is rarely considered. However, for the droplet-to-iron electrochemical reaction, the mechanism of charge transfer in the process is still unclear. In this paper, a reactive molecular dynamics simulation model for the droplet-to-iron electrochemical reaction is developed for the first time. The electrochemical reaction of droplet-to-iron is studied, and the interaction between droplet wetting and corrosion on iron is investigated. The effects of temperature, electric field strength, and salt concentration on the electrochemical reaction are explored. Results show that droplet wetting on the iron surface and the formation of a single-molecular-layer ordered structure are prerequisites for corrosion. The hydroxyl radicals that penetrate the ordered structure acquire electrons from iron atoms on the substrate surface under the action of Coulomb forces and form iron-containing oxides with these iron atoms. The corrosion products and craters lead to a reduced droplet height, which promotes droplet wetting on iron and further intensifies the droplet-to-iron electrochemical reaction.

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Bag2image: A Multi-Instance Network Traffic Representation for Network Security Event Prediction

SSRN

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SSRN 2024年

作者： Zhang, Jiachen Han, Daoqi Lv, Zhaoxuan Liu, Yang Lu, Yueming Duan, Junke Zhang, Xinyu School of Cyberspace Security Beijing University of Posts and Telecommunications Beijing China Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education China School of Automation Beijing Institute of Technology Beijing China Beijing Institute of Astronautical Systems Engineering Beijing China

In practical scenarios, security events triggered by abnormal network traffic often result from the collective behavior of multiple data streams, embodying group security events with collective characteristics. Existing research methods, focusing on individual data streams, lack a macroscopic analysis and struggle with challenges of analyzing massive, imbalanced data sets. To address these challenges, this paper adopts a multi-instance learning approach, mapping multiple data streams into a bag with a coarse-grained approach, where each bag corresponds to a security event label and each data stream represents an instance. We propose a multi-instance network traffic conversion method, Bag2Image, which transforms temporal multi-instance network traffic data into image representations, preserving the spatio-temporal characteristics of instances within the bag through image channels and pixels. This strategy allows the network security event prediction task to be approached as an image classification problem, leveraging advanced image classification techniques for prediction. Our cross-experiments with six advanced multi-instance learning (MIL) algorithms and six different classification models demonstrate the superior performance of our method on both the UNSW-NB15 dataset and a private dataset. Specifically, our method achieved the highest F1 scores of 77.9% and 74.4% on these datasets, respectively, representing improvements of 4.1% and 13.5% over the second-best MIL algorithm. The recall rates also saw increases of 4.1% and 13.2%, respectively. © 2024, The Authors. All rights reserved.

关键词： Network security

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Self-Supervised Commonsense Knowledge Learning for Document-Level Relation Extraction

SSRN

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SSRN 2022年

作者： Li, Rongzhen Zhong, Jiang Xue, Zhongxuan Dai, Qizhu Li, Xue College of Computer Science Chongqing University Chongqing400044 China Key Laboratory of Dependable Service Computing in Cyber-Physical-Society Ministry of Education Chongqing University Chongqing400044 China School of Information Technology and Electrical Engineering University of Queensland Brisbane Australia

Compared to sentence-level relation extraction, practical document-level relation extraction (DocRE) is a more challenging task for which multi-entity problems need to be resolved. It aims at extracting relationships between two entities over multiple sentences at once while taking into account significant cross-sentence features. Learning long-distance semantic relation representation across sentences in a document, however, is a widespread and difficult task. To address this problem, this paper proposes a self-supervised commonsense-enhanced DocRE method, called SCDRE, without external knowledge. First, we introduce self-supervised learning to represent commonsense knowledge of each entity in an entity pair based on the commonsense entailed text. Second, we convert the cross-sentence entity pairs into anonymous entity pairs with coreference commonsense replacement. Finally, we perform semantic relation representation learning on the anonymous entity pairs and automatically convert them into target entity pairs. We examined our model on three publicly accessible datasets, DocRED, DialogRE and MPDD, and the results show that it performs significantly better than strong baselines by 2.03% F1, and commonsense knowledge has an important contribution to the DocRE by the ablation experimental analysis. © 2022, The Authors. All rights reserved.

关键词： Supervised learning

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Joint power allocation and beamforming for non-orthogonal multiple access (NOMA) in 5G millimeter-wave communications

arXiv

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arXiv 2017年

作者： Xiao, Zhenyu Zhu, Lipeng Choi, Jinho Xia, Pengfei Xia, Xiang-Gen School of Electronic and Information Engineering Beihang University Beijing100191 China Beijing Key Laboratory for Network-Based Cooperative Air Traffic Management Beijing Laboratory for General Aviation Technology Beijing100191 China Collaborative Innovation Center of Geospatial Technology Wuhan430079 China Korea Republic of School of Electronics and Information Engineering Key Laboratory of Embedded System and Service Computing Tongji University Shanghai200092 China College of Information Engineering Shenzhen University Shenzhen China Department of Electrical and Computer Engineering University of Delaware NewarkDE19716 United States

In this paper we explore non-orthogonal multiple access (NOMA) in millimeter-wave (mmWave) communications (mmWave-NOMA). In particular, we consider a typical problem, i.e., maximization of the sum rate of a 2-user mmWave-NOMA system. In this problem, we need to find the beamforming vector to steer towards the two users simultaneously subject to an analog beamforming structure, while allocating appropriate power to them. As the problem is non-convex and may not be converted to a convex problem with simple manipulations, we propose a suboptimal solution to this problem. The basic idea is to decompose the original joint beamforming and power allocation problem into two sub-problems which are relatively easy to solve: one is a power and beam gain allocation problem, and the other is a beamforming problem under a constant-modulus constraint. Extension of the proposed solution from 2-user mmWave-NOMA to more-user mmWave-NOMA is also discussed. Extensive performance evaluations are conducted to verify the rational of the proposed solution, and the results also show that the proposed sub-optimal solution achieve close-to-bound sum-rate performance, which is significantly better than that of time-division multiple access (TDMA). Copyright © 2017, The Authors. All rights reserved.

关键词： Millimeter waves

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Integration of a novel attribute and classical topology metrics of hyper-networks for automatic diagnosis of Major depressive disorder 22

Integration of a novel attribute and classical topology metr...

引用

22nd IEEE International Conference on E-Health Networking, Application and services, HEALTHCOM 2020

作者： Li, Yongchao Chen, Nan Wang, Yin Yang, Lin Zheng, Weihao Yao, Zhijun Hu, Bin Gansu Province Key Laboratory of Wearable Computing School of Information Science and Engineering Lanzhou University Lanzhou China College of Biomedical Engineering and Instrument Science Zhejiang University Zhejiang China CAS Center for Excellence in Brain Science and Intelligence Technology Shanghai Institutes for Biological Sciences Chinese Academy of Sciences Shanghai China Joint Research Center for Cognitive Neurosensor Technology of Lanzhou University Institute of Semiconductors Chinese Academy of Sciences Lanzhou China Engineering Research Center of Open Source Software and Real-Time System Lanzhou University Ministry of Education Lanzhou China

ISBN: (纸本)9781728162676

Conventional hyper-network coefficients ignore the weighted hyper-edge information which could be vital in researching the specificity of brain disease. Functional hyper-networks for 64 healthy controls (HC) and 56 patients with major depressive disorder (MDD) were constructed using the least absolute shrinkage and selection operator (Lasso). Not only the classical topology metrics but also a novel hyper-edge weight (HEW) attribute were extracted as features to promote the functional-based auto-diagnosis accuracy of MDD. We compared the categorization performance of each hyper-network coefficient. A multi-feature ensemble model was applied to fuse different kinds of features. We obtained 82.15 % accuracy with the classical hyper-network clustering coefficient (HCC) and 84.08 % accuracy with the HEW attribute on the MDD dataset. The performance was further improved to 89.24% by combining all the properties of the hyper-networks. The multi-feature ensemble model combining different hyper-network coefficients provides new insights into the automatic diagnosis with diverse information of MDD. © 2021 IEEE.

关键词： Topology

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