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IEEE Symposium on computers and Communications (ISCC)

作者： Zhicheng Liu Shuhao Li Yongzheng Zhang Xiaochun Yun Zhenyu Cheng Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China Key Laboratory of Network Assessment Technology Chinese Academy of Sciences Beijing China National Computer Network Emergency Response Technical Team/Coordination Center of China Beijing China

ISBN: (数字)9781728180861

ISBN: (纸本)9781728180878

With the booming of malware-based cyber-security incidents and the sophistication of attacks, previous detections based on malware sample analysis appear powerless due to time-consuming and labor-intensive analysis process. The existing detection methods based on traffic analysis rely heavily on the available traffic patterns, which hinder detecting the zero-day attacks caused by malware variants. In this paper, we propose an approach based on deep learning referred to as TrafficGAN, which analyzes (HTTP) traffic sessions to distinguish between malware-related and normal traffic. We first try to explore traffic patterns of malware variants by adding noise and category condition to the Generative Adversarial networks (GAN), thus generating various similar but slightly different traffic. And then, we use discriminative model to seek the deviation between abnormal traffic and normal traffic by extracting the essential difference. Notablely, we increase the diversity of data by generating samples adversarially, which enhances the robustness of the system to detect zero-day attacks and highlights the lack of sensitive data in the security community. We conduct extensive experiments on the public dataset and our data collected for specific targets. The results demonstrate that our method achieves superior performance to other methods and protects specific targets from the susceptibility of malware.

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In-situ data scheduling optimization based on rainbow DQN for IIoT

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Future Generation computer Systems 2026年 174卷

作者： Peiying Zhang Siyi Li Lizhuang Tan Neeraj Kumar Jian Wang Kai Liu Qingdao Institute of Software College of Computer Science and Technology China University of Petroleum (East China) Qingdao 266580 China Shandong Key Laboratory of Intelligent Oil & Gas Industrial Software Qingdao 266580 China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan 250014 China Department of Computer Science and Engineering Thapar Institute of Engineering and Technology Patiala 147004 India Department of Computer Science University of Economics and Human Sciences Warszawa 01-043 Poland Department of Networks and Communications College of Computer Science and Information Technology Imam Abdulrahman Bin Faisal University Dammam 31441 Saudi Arabia College of Science China University of Petroleum (East China) Qingdao 266580 China Beijing National Research Center for Information Science and Technology Tsinghua University Beijing 100084 China State Key Laboratory of Space Network and Communications Tsinghua University Beijing 100084 China

In industrial systems, in-situ computing refers to a mode of local or proximate data processing within the cloud–edge-terminal architecture. In the Industrial Internet of Things (IIoT) environment, servers are typically deployed near terminal devices to coordinate and manage the scheduling of in-situ data, forming a comprehensive in-situ server system. With the rapid growth of IIoT terminal devices, challenges such as high processing delays and severe demand congestion arise in the in-situ data scheduling process. To address this challenge, this paper proposes a scheduling optimization scheme based on Rainbow DQN. The scheme designs a scheduling buffer and classification strategy to distinguish in-situ data based on storage demand characteristics, thereby optimizing the scheduling process. At the same time, a multi-level storage structure is introduced to store in-situ data classified according to the scheduling strategy. Additionally, the Rainbow DQN algorithm is utilized to assist in the scheduling decision-making of in-situ data retrieval, effectively alleviating system congestion while ensuring certain throughput performance. Extensive simulation experiments show that the Rainbow DQN algorithm achieves a reduction of 60.82% in average waiting delay, 62.94% in average unprocessed demand, and a 58.27% reduction in average completion delay.

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Computational strategy of dynamic nanorod networks for dielectric elastomer nanocomposites towards the balance of high permittivity, low modulus, and high breakdown strength

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Polymer 2025年 333卷

作者： Han Qin Yancong Feng Tao Liu Baofeng Gao Ying Guo Jingshan Pan Ming Tian Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan 250000 China Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan 250000 China Guangdong Provincial Key Laboratory of Optical Information Materials and Technology & Institute of Electronic Paper Displays South China Academy of Advanced Optoelectronics South China Normal University Guangzhou 510006 China State Key Laboratory of Organic-Inorganic Composites College of Materials Science and Engineering Beijing University of Chemical Technology Beijing 100029 China

Dielectric elastomers exhibit immense potential in emerging fields such as soft robotics, flexible sensors, wearable electronic devices, and energy harvesting, but their widespread application is limited by the intricate paradoxes among the high permittivity, low modulus, and high breakdown strength. By regulating the aspect ratio, concentration, interaction strength, and crosslinking density, a dynamic nanorod network structure is constructed to harmonize comprehensive properties. The construction of the nanorod network significantly enhances permittivity due to the coupling effect of nanorods. Meanwhile, the low percolation threshold avoids high modulus, enabling large electro-induced strain. Under high strain, nanorod orientation shifts, leading to the destruction of the network, thereby avoiding the electric field concentration and achieving the enhancement of electromechanical stability. After the external field is withdrawn, entropic elasticity drives the recovery of elastomers, while the network reconstructs to restore the high dielectric constant. This strategy offers new insights into balancing high permittivity, low modulus, and high breakdown strength in practical applications.

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Modeling of Layered Supply Chain network Considering Similarity

Modeling of Layered Supply Chain Network Considering Similar...

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第32届中国控制与决策会议

作者： Yi Zhang Yurong Song Guoping Jiang Bin Yu School of Computer Science Software and Network Space SecurityNanjing University of Posts and Telecommunications School of Automation Nanjing University of Posts and Telecommunications Jiangsu Engineering Lab for IOT Intelligent Robots(IOTRobot) Hengyang Key Laboratory of Information Secure and its application research

The supply chain network is a complex network with the risk of cascading *** study the cascading failure in it,an accurate supply chain network model needs to be *** this paper,we construct a layered supply chain network model according to the types of companies in real supply chain *** first define the similarity between companies in the same layer by studying real-world scenarios in supply chain ***,considering both the node degree and the similarity between nodes in the same layer,we propose preferential attachment probability formulas for the new nodes to join the exist ***,the evolution steps of the model are *** analyze the structural characteristics of the new *** results show that the new model has scale-free property and small-world property,which conform to the structural characteristics of the known supply chain *** with the other network models,it is found that the new model can better describe the actual supply chain network.

关键词： Supply Chain network Cascading Failure Similarity BA Model Structural Characteristics

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LR-RRA-CCA secure functional encryption for randomized functionalities from trapdoor HPS and LAF

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Science China(Information Sciences) 2018年第5期61卷 291-305页

作者： Huige WANG Kefei CHEN Baodong QIN Ziyuan HU Department of Network Engineering Anhui Science and Technology University Department of Computer Science and Engineering Shanghai Jiao Tong University Department of Mathematics Hangzhou Normal University Westone Cryptologic Research Center National Engineering Laboratory for Wireless Security Xi'an University of Posts and Telecommunications State Key Laboratory of Cryptology

Original public-key encryption is viewed as a tool to encrypt point-to-point *** data is aimed at a particular user,where a sender encrypts a message x under a specified public key P *** this case,only the owner of the(unique)secret key corresponding to P K can decrypt the resulting ciphertext to recover the message ***,on some occasions,encryption is necessary to implement more fine-grained control of the encrypted *** example,the sender

关键词： LAF CCA RRA

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An improved covariance spectrum sensing algorithm establish on AD test 4th

An improved covariance spectrum sensing algorithm establish ...

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4th International Conference on Signal and Information Processing, networking and computers, ICSINC 2018

作者： Chen, Yaqin Jing, Xiaojun Mu, Junsheng Li, Jia National Engineering Laboratory for Mobile Network Security Beijing University of Posts and Telecommunications Beijing China Ministry of Education Beijing University of Posts and Telecommunications Beijing China School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China School of Engineering and Computer Science Oakland University Rochester United States

ISBN: (纸本)9789811317323

Owing to no need for prior knowledge of signal, blind spectrum sensing has received wide attention. Covariance Absolute Value (CAV) detection algorithm, one of the most popular blind sensing algorithms, considers the correlation of signal samples. However, its detection performance is restricted by the uncertain threshold calculation. To optimize the performance of CAV, we propose a new method based on a new statistic and goodness of fit test. The statistic is constructed from the off-diagonal of covariance matrix firstly, then Anderson-Darling (AD) test is used to estimate the existence or absence of primary user. The proposed method not only achieves blind detection but also improves the sensing performance of CAV. Experimental results manifest the effectiveness of the proposed scheme. © Springer Nature Singapore Pte Ltd. 2019.

关键词： Covariance matrix

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Golden grain: Building a secure and decentralized model marketplace for MLaaS

arXiv

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arXiv 2020年

作者： Weng, Jiasi Weng, Jian Cai, Chengjun Huang, Hongwei Wang, Cong The College of Information Science and Technology College of Cyber security National Joint Engineering Research Center of Network Security Detection and Protection Technology Guangdong Key Laboratory of Data Security and Privacy Preserving Jinan University Guangzhou510632 China The Department of Computer Science City University of Hong Kong Hong Kong Hong Kong The City University of Hong Kong Shenzhen Research Institute Shenzhen518057 China

ML-as-a-service (MLaaS) becomes increasingly popular and revolutionizes the lives of people. A natural requirement for MLaaS is, however, to provide highly accurate prediction services. To achieve this, current MLaaS systems integrate and combine multiple well-trained models in their services. Yet, in reality, there is no easy way for MLaaS providers, especially for startups, to collect sufficiently well-trained models from individual developers, due to the lack of incentives. In this paper, we aim to fill this gap by building up a model marketplace, called as Golden Grain, to facilitate model sharing, which enforces the fair model-money swapping process between individual developers and MLaaS providers. Specifically, we deploy the swapping process on the blockchain, and further introduce a blockchain-empowered model benchmarking process for transparently determining the model prices according to their authentic performances, so as to motivate the faithful contributions of well-trained models. Especially, to ease the blockchain overhead for model benchmarking, our marketplace carefully offloads the heavy computation and designs a secure off-chain on-chain interaction protocol based on a trusted execution environment (TEE), for ensuring both the integrity and authenticity of benchmarking. We implement a prototype of our Golden Grain on the Ethereum blockchain, and conduct extensive experiments using standard benchmark datasets to demonstrate the practically affordable performance of our design. © 2020, CC BY.

关键词： Blockchain

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A Symmetric Uniform Formula and Sole Index Method for Sieving (Twin) Primes

A Symmetric Uniform Formula and Sole Index Method for Sievin...

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IEEE International Conference on Trust, security and Privacy in Computing and Communications (TrustCom)

作者： Wei Ren School of Computer Science China University of Geosciences Wuhan P.R. China Guangxi Key Laboratory of Cryptography and Information Security Guilin P.R. China Key Laboratory of Network Assessment Technology CAS Institute of Information Engineering Chinese Academy of Sciences Beijing P.R. China

ISBN: (数字)9781665403924

ISBN: (纸本)9781665403931

All primes can be indexed by k, as primes must be in the form of 6k+1 or 6k-1. In this paper, we explore for the set of k such that either 6k+1 or 6k-1 is not a prime. Our method provides a uniform formula for k that can sieve primes and twin primes as well. The uniform presents symmetry in terms of k that works as single index in sieving. We also propose a new conjecture that is equivalent to Twin Prime Conjecture but possibly be easier to approach by merely exploring of sole index in term of k. The uniform formula for prime sieving are as follows: k ∈ S l ⇒ 6k-1 ∉ P, where s l = [-I]6I+1 = [I] 6I-1 \min([I] 6I-1 ), I ∈ N. ∈ s r ⇒ 6k+1 ∉ P, where s r = [-I] 6I-1 ∪ ([I] 6I+1 \min([I] 6I+1 )), I ∈ N.

关键词： Data privacy Conferences Indexes computer security

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Research on System Dynamics and Agent Hybrid Modeling Method for Multi-Energy Collaborative Control under Ubiquitous Power Internet 3

Research on System Dynamics and Agent Hybrid Modeling Method...

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3rd IEEE Conference on Energy Internet and Energy System Integration, EI2 2019

作者： Zhang, Fuxing Zhang, Yingjie Zhan, Xiong Luo, Juan Miao, Honglei Gui, Yonghua Hunan University College of Computer Science and Electronic Engineering Changsha China Global Energy Interconnection Research Institute State Grid Key Laboratory of Information Network Security Beijing China HNAC Technology Co. Ltd. Hardware RD Department Changsha China

ISBN: (纸本)9781728131375

In order to promote the study of system dynamics (SD) and multi-agent theory and its wide application in the field of renewable energy, a hybrid modeling method of SD and Agent is proposed in this paper, which is suitable for the multi-energy collaborative management and control of regional cooling, heating and power under the background of the ubiquitous Internet of Things. Based on the detailed analysis of the lack of single modeling paradigm of system dynamics and the necessity and feasibility of mixed modeling of SD and Agent, the basic types of hybrid modeling of SD and Agent for regional multi-energy cooperative management and control are defined, and the application forms, simulation platforms, problems and ideas of hybrid modeling of SD and Agent in regional multi-energy cooperative management are discussed and analyzed, which can deepen the innovative application of SD and Agent hybrid modeling in multiple fields and assist relevant managers in scientific decision-making. © 2019 IEEE.

关键词： Internet of things

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SCScan: A SVM-Based Scanning System for Vulnerabilities in Blockchain Smart Contracts

SCScan: A SVM-Based Scanning System for Vulnerabilities in B...

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IEEE International Conference on Trust, security and Privacy in Computing and Communications (TrustCom)

作者： Xiaohan Hao Wei Ren Wenwen Zheng Tianqing Zhu School of Computer Science China University of Geosciences Wuhan P.R. China Guangxi Key Laboratory of Cryptography and Information Security Guilin P.R. China Key Laboratory of Network Assessment Technology CAS Institute of Information Engineering Chinese Academy of Sciences Beijing P.R. China

ISBN: (数字)9781665403924

ISBN: (纸本)9781665403931

The application of blockchain has moved beyond cryptocurrencies, to applications such as credentialing and smart contracts. The smart contract allows ones to achieve fair exchange for values without relying on a centralized entity. However, as the smart contract can be automatically executed with token transfers, an attacker can seek to exploit vulnerabilities in smart contracts for illicit profits. Thus, this paper proposes a support vector machine (SVM)-based scanning system for vulnerabilities on smart contracts. Our evaluation on Ethereum demonstrate that we achieve a identification rate of over 90% based on several popular attacks.

关键词： Support vector machines Deep learning Privacy Smart contracts Supervised learning Blockchain Pattern matching

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