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RAISE:A Resilient Anonymous Information Sharing Environment

computer Modeling in engineering & Sciences 2023年第12期137卷 2743-2759页

作者： Ning Hu Ling Liu Xin Liu Kaijun Wu Yue Zhao Cyberspace Institute of Advanced Technology Guangzhou UniversityGuangzhou510006China Science and Technology on Communication Security Laboratory The 30th Research Institute of China Electronics Technology Group CorporationChengdu610041China College of Computer Engineering and Applied Math Changsha UniversityChangsha410022China

With the widespread application of cloud computing and network virtualization technologies,more and more enterprise applications are directly deployed in the ***,the traditional TCP/IP network transmission model does not fully consider the information security issues caused by the uncontrollable internet *** security communication solutions represented by encrypted virtual private networks(VPN)are facing multiple security *** fact,during the communication process,the user application needs to protect not only the content of the communication but also the behavior of the communication,such as the communication relationship,the communication protocol,and so *** by blockchain and software-defined networking technology,this paper proposes a resilient anonymous information sharing environment,*** RAISE system consists of user agents,a core switching network and a control cluster based on a consortium *** agents are responsible for segmenting,encrypting,and encapsulating user *** core switching network forwards user traffic according to the rules issued by the controller,and the controller dynamically calculates the forwarding rules according to the security *** from onion routing technology,RAISE adopts the controller to replace the onion routing model,which effectively overcomes the uncontrollability of *** dispersed computing model is introduced to replace the TCP/IP pipeline transmission models,which overcomes the problems of anti-tracking and traffic hijacking that cannot be solved by *** propose a blockchain control plane framework,design the desired consensus algorithmand deploy a RAISE systemconsisting of 150 nodes in an internet *** experimental results show that the use of blockchain technology can effectively improve the reliability and security of the control *** maintaining high-performance network transmission,it further provides network communicatio

关键词： Software-defined anonymous communication network blockchain network communication security

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Bi-ETC: A Bidirectional Encrypted Traffic Classification Model Based on BERT and BiLSTM 8

Bi-ETC: A Bidirectional Encrypted Traffic Classification Mod...

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8th International Conference on Data Science in Cyberspace, DSC 2023

作者： Ma, XiTing Liu, Tao Hu, Ning Liu, Xin Cyberspace Institute of Advanced Technology GuangZhou University Guangzhou510006 China Peng Cheng Laboratory Shenzhen518000 China College of Computer Engineering and Applied Math ChangSha University Changsha410022 China

ISBN: (纸本)9798350331035

While packet encryption technology provides user privacy protection, it also brings challenges to traffic classification. Traditional traffic classification technologies based on packet fields or payload data content are becoming powerless in the face of encrypted traffic. Although traffic encryption technology can hide data content, it cannot change the inherent characteristics of application traffic. Therefore, how to classify encrypted traffic by mining traffic features has become a topic of widespread concern. An enormous amount of research shows that machine learning algorithms achieve good results in encrypted traffic classification. However, there is still room for improvement. For example, deep learning methods require a large amount of labeled training data, and it is difficult for pre-trained models to capture the associated features of long sequences of packet. This paper propose a Bidirectional encrypted traffic classification model based on BERT and BiLSTM(Bi-ETC), by inserting [CLS]Token at the connection of Token sequence between BERT and BiLSTM, strengthens BiLSTM's focus on packet-level features while maintaining BilSTM's capture of Token sequence context. Experimental results show that the Bi-ETC model is superior to all proposed classification methods on the ISCX VPN dataset, reaching 99.43%in F1 score and 99.7%in accuracy. © 2023 IEEE.

关键词： BERT BiLSTM Encrypted traffic classification Network communication security Privacy Protection

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Detecting lithium plating dynamics in a solid-state battery with operando X-ray computed tomography using machine learning

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npj Computational Materials 2023年第1期9卷 1402-1410页

作者： Ying Huang David Perlmutter Andrea Fei-Huei Su Jerome Quenum Pavel Shevchenko Dilworth Y.Parkinson Iryna V.Zenyuk Daniela Ushizima Department of Materials Science&Engineering University of California IrvineIrvineCaliforniaUSA National Fuel Cell Research Center University of California IrvineIrvineCaliforniaUSA Applied Math and Computational Research Division Lawrence Berkeley National LaboratoryBerkeleyCaliforniaUSA Department of Chemical&Biomolecular Engineering University of California IrvineIrvineCaliforniaUSA Department of Electrical Engineering and Computer Sciences College of EngineeringUniversity of California BerkeleyBerkeleyCaliforniaUSA Advanced Photon Source Argonne National Laboratory9700 South Cass AvenueLemontIllinoisUSA Advanced Light Source Lawrence Berkeley National LaboratoryBerkeleyCaliforniaUSA Berkeley Institute for Data Science University of California BerkeleyBerkeleyCaliforniaUSA

Operando X-ray micro-computed tomography(µCT)provides an opportunity to observe the evolution of Li structures inside pouch *** is an essential step to quantitatively analyzingµCT datasets but is challenging to achieve on operando Li-metal battery datasets due to the low X-ray attenuation of the Li metal and the sheer size of the ***,we report a computational approach,batteryNET,to train an Iterative Residual U-Net-based network to detect Li *** resulting semantic segmentation shows singular Li-related component changes,addressing diverse morphologies in the *** addition,visualizations of the dead Li are provided,including calculations about the volume and effective thickness of electrodes,deposited Li,and redeposited *** also report discoveries about the spatial relationships between these *** approach focuses on a method for analyzing battery performance,which brings insight that significantly benefits future Li-metal battery design and a semantic segmentation transferrable to other datasets.

关键词： battery lithium state

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Bi-ETC: A Bidirectional Encrypted Traffic Classification Model Based on BERT and BiLSTM

Bi-ETC: A Bidirectional Encrypted Traffic Classification Mod...

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IEEE International Conference on Data Science in Cyberspace (DSC)

作者： XiTing Ma Tao Liu Ning Hu Xin Liu Cyberspace Institute of Advanced Technology GuangZhou University Guangzhou China Peng Cheng Laboratory Shenzhen China College of computer Engineering and Applied Math ChangSha University Changsha China

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TLD-WF: Surfing Freely Under Website Fingerprinting Attacks 7

TLD-WF: Surfing Freely Under Website Fingerprinting Attacks

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7th IEEE International Conference on Data Science in Cyberspace, DSC 2022

作者： Liu, Ling Liu, Xin Zhao, Yue Hu, Ning Cyberspace Institute of Advanced Technology GuangZhou University Guangzhou510006 China ChangSha University College of Computer Engineering and Applied Math Changsha410022 China Science and Technology on Communication Security Laboratory Chengdu610041 China Peng Cheng Laboratory Shenzhen518000 China

ISBN: (数字)9781665474801

ISBN: (纸本)9781665474801

Anonymous networks hide communication content and communication relationships by encrypting the content and transmission path of traffic. But the website fingerprinting attack can determine the websites a user visits by analyzing the user's traffic patterns and website fingerprints without decrypting the traffic. To defend against website fingerprinting attacks, most of the existing methods reduce the accuracy of analysis by increasing the access delay and background traffic. Unfortunately, such methods not only degrade the user experience, but also affect the performance of anonymity networks. By adopting the Tor pluggable transmission technology, this paper proposes a new targeted, lightweight defense to against website fingerprinting attack (TLD-WF). TLD-WF uses the TLD-flow module to obtain the analyzed flow information with an accuracy rate of over 90%, and conducts targeted dynamic scrambling. TLD-WF not only effectively guarantees the anonymity and security of Tor users, but also has the advantages of zero latency overhead and low bandwidth overhead. The experimental results show that TLD-flow can reduce the analysis accuracy of the CNN classifier from 93.2% to around 8.3% under normal conditions. With guaranteed zero latency and 45% bandwidth overhead, the analytical accuracy of the CNN classifier will be reduced by about 50%. © 2022 IEEE.

关键词： Websites

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Retraction Note: An efficient quantum based D2D computation and communication approach for the internet of things

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Optical and Quantum Electronics 2024年第11期56卷 1-2页

作者： Mekala, M. S. Srivastava, Gautam Chun-Wei Lin, Jerry Dhiman, Gaurav Park, Ju H. Jung, Ho-Youl Department of Information and Communication Engineering Yeungnam University Gyeongsan Korea RLRC for Autonomous Vehicle Parts and Materials Innovation Yeungnam University Gyeongsan Korea Department of Math and Computer Science Brandon University Brandon Canada Western Norway University of Applied Sciences Gaurav Dhiman Bergen Norway Department of Computer Science Government Bikram College of Commerce Patiala India Department of Electrical Engineering Yeungnam University Gyeongsan Korea Research Centre for Interneural Computing China Medical University Taichung Taiwan University Centre for Research and Development Department of Computer Science and Engineering Chandigarh University Gharuan India Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India

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A graph-based convolutional neural network stock price prediction with leading indicators

A graph-based convolutional neural network stock price predi...

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作者： Wu, Jimmy Ming-Tai Li, Zhongcui Srivastava, Gautam Tasi, Meng-Hsiun Lin, Jerry Chun-Wei College of Computer Science and Engineering Shandong University of Science and Technology Shandong China Department of Math and Computer Science Brandon University Brandon Canada Research Centre for Interneural Computing China Medical University Taichung Taiwan Department of Management Information Systems National Chung Hsing University Taichung Taiwan Department of Computer Science Electrical Engineering and Mathematical Sciences Western Norway University of Applied Sciences Bergen Norway

The stock market is a capitalistic haven where the issued shares are transferred, traded, and circulated. It bases stock prices on the issue market, however, the structure and trading activities of the stock market are much more complicated than the issue market itself. Therefore, making an accurate prediction becomes an intricate as well as highly difficult task. On the other hand, because of the potential benefits of stock prediction, it attracts generation after generation of scholars as well as investors to continuously develop various prediction methods from different perspectives, a myriad of theories, a multitude of investment strategies, and different practical experiences. In this article, aiming at the task of time series (financial) feature extraction and prediction of price movements, a new convolutional novel neural network that can be called a framework to improve the prediction accuracy of stock trading is proposed. The method that is proposed is called SSACNN, a short form of stock sequence array convolutional neural network. SSACNN collects data including historical data of prices and its leading indicators (options/futures) for a stock to take an array as the input graph of the convolutional neural network framework. In our experimental results, five Taiwanese and American stocks were used as a benchmark to compare with the previous algorithms and proposed algorithm, the motion prediction performance of SSACNN has been improved significantly and proved that it has the potential to be applied in the real financial market. © 2020 John Wiley & Sons Ltd

关键词： Forecasting

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A DNA Based Colour Image Encryption Scheme Using A Convolutional Autoencoder

arXiv

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arXiv 2022年

作者： Ahmed, Fawad Rehman, Muneeb Ur Ahmad, Jawad Khan, Muhammad Shahbaz Boulila, Wadii Srivastava, Gautam Lin, Jerry Chun-Wei Buchanan, William J. Department of Cyber Security Pakistan Navy Engineering College NUST Karachi75350 Pakistan Department of Electrical Engineering HITEC University Taxila Pakistan School of Computing Edinburgh Napier University Edinburgh United Kingdom Department of Electrical Engineering HITEC University Taxila47080 Pakistan Robotics and Internet-of-Things Laboratory Prince Sultan University Riyadh Saudi Arabia RIADI Laboratory University of Manouba Manouba Tunisia Math and Computer Science Brandon University Brandon Canada Research Centre for Interneural Computing China Medical University Taichung Taiwan Dept of Computer Science and Math Lebanese American University Beirut Lebanon Western Norway University of Applied Sciences Bergen Norway

With the advancement in technology, digital images can easily be transmitted and stored over the Internet. Encryption is used to avoid illegal interception of digital images. Encrypting large-sized colour images in their original dimension generally results in low encryption/decryption speed along with exerting a burden on the limited bandwidth of the transmission channel. To address the aforementioned issues, a new encryption scheme for colour images employing convolutional autoencoder, DNA and chaos is presented in this paper. The proposed scheme has two main modules, the dimensionality conversion module using the proposed convolutional autoencoder, and the encryption/decryption module using DNA and chaos. The dimension of the input colour image is first reduced from N × M × 3 toP × Q gray-scale image using the encoder. Encryption and decryption are then performed in the reduced dimension space. The decrypted gray-scale image is upsampled to obtain the original colour image having dimension N × M × 3. The training and validation accuracy of the proposed autoencoder is 97% and 95%, respectively. Once the autoencoder is trained, it can be used to reduce and subsequently increase the dimension of any arbitrary input colour image. The efficacy of the designed autoencoder has been demonstrated by the successful reconstruction of the compressed image into the original colour image with negligible perceptual distortion. The second major contribution presented in this paper is an image encryption scheme using DNA along with multiple chaotic sequences and substitution boxes. The security of the proposed image encryption algorithm has been gauged using several evaluation parameters, such as histogram of the cipher image, entropy, NPCR, UACI, key sensitivity, contrast, etc. The experimental results of the proposed scheme demonstrate its effectiveness to perform colour image encryption. © 2022, CC BY.

关键词： DNA

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Continuous and Discrete Data Assimilation with Noisy Observations for the Rayleigh-Benard Convection: A Computational Study

arXiv

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arXiv 2022年

作者： Hammoud, Mohamad Abed El Rahman Le Maître, Olivier Titi, Edriss S. Hoteit, Ibrahim Knio, Omar Division of Physical Sciences and Engineering King Abdullah University of Science and Technology Thuwal23955 Saudi Arabia Centre de Mathématiques Appliquées CNRS and Inria Ecole Polytechnique Palaiseau91120 France Department of Applied Mathematics and Theoretical Physics University of Cambridge CambridgeCB3 0WA United Kingdom Department of Mathematics Texas A & M University College StationTX77843 United States Division of Computer Electrical and Mathematical Sciences and Engineering King Abdullah University of Science and Technology Thuwal23955 Saudi Arabia

Obtaining accurate high-resolution representations of model outputs is essential to describe the system dynamics. In general, however, only spatially- and temporally-coarse observations of the system states are available. These observations can also be corrupted by noise. Downscaling is a process/scheme in which one uses coarse scale observations to reconstruct the high-resolution solution of the system states. Continuous Data Assimilation (CDA) is a recently introduced downscaling algorithm that constructs an increasingly accurate representation of the system states by continuously nudging the large scales using the coarse observations. We introduce a Discrete Data Assimilation (DDA) algorithm as a downscaling algorithm based on CDA with discrete-in-time nudging. We then investigate the performance of the CDA and DDA algorithms for downscaling noisy observations of the Rayleigh-Bénard convection system in the chaotic regime. In this computational study, a set of noisy observations was generated by perturbing a reference solution with Gaussian noise before downscaling them. The downscaled fields are then assessed using various error- and ensemble-based skill scores. The CDA solution was shown to converge towards the reference solution faster than that of DDA but at the cost of a higher asymptotic error. The numerical results also suggest a quadratic relationship between the 2 error and the noise level for both CDA and DDA. Cubic and quadratic dependences of the DDA and CDA expected errors on the spatial resolution of the observations were obtained, respectively. © 2022, CC BY.

关键词： Errors

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From thermodynamics to protein design: Diffusion models for biomolecule generation towards autonomous protein engineering

arXiv

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arXiv 2025年

作者： Li, Wenran Cadet, Xavier F. Medina-Ortiz, David Davari, Mehdi D. Sowdhamini, Ramanathan Damour, Cedric Li, Yu Miranville, Alain Cadet, Frederic University of Paris City University of Reunion INSERM BIGR DSIMB ParisF-75015 France Department of Computing Imperial College London United Kingdom Department of Computer Engineering University of Magallanes Punta Arenas Chile Centre for Biotechnology and Bioengineering Universidad de Chile Santiago Chile Department of Bioorganic Chemistry Leibniz Institute of Plant Biochemistry Halle06120 Germany National Centre for Biological Science TIFR Bangalore India Molecular Biophysics Unit Indian Institute of Science Bangalore India Institute of Bioinformatics and Applied Biotechnology Bangalore India EnergyLab EA 4079 Faculty of Sciences and Technology University of Reunion France School of Information Science and Technology Beijing Institute of Artificial Intelligence China Laboratoire de Mathématiques Appliquées University Le Havre Normandie le Havre France PEACCEL Artificial Intelligence Department AI for Bi-ologics Paris75013 France

Protein design with desirable properties has been a significant challenge for many decades. Generative artificial intelligence is a promising approach and has achieved great success in various protein generation tasks. Notably, diffusion models stand out for their robust mathematical foundations and impressive generative capabilities, offering unique advantages in certain applications such as protein design. In this review, we first give the definition and characteristics of diffusion models and then focus on two strategies: Denoising Diffusion Probabilistic Models (DDPM) and Score-based Generative Models (SGM), where DDPM is the discrete form of SGM. Furthermore, we discuss their applications in protein design, peptide generation, drug discovery, and protein-ligand interaction. Finally, we outline the future perspectives of diffusion models to advance autonomous protein design and engineering. The E(3) group consists of all rotations, reflections, and translations in three-dimensions. The equivariance in the E(3) group can maintain the physical stability of the N − Cα − C frame of each amino acid as much as possible, and we reflect on how to keep the diffusion model E(3) equivariant for protein generation. © 2025, CC BY.

关键词： Biomolecules

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