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arXiv 2023年

作者： Sayed, Md Abu Anwar, Ahmed H. Kiekintveld, Christopher Bosansky, Branislav Kamhoua, Charles University of Texas El PasoTX79968 United States US Army Research Laboratory MD20783 United States Department of Computer Science Faculty of Electrical Engineering Czech Technical University in Prague Czech Republic

Reconnaissance activities precedent other attack steps in the cyber kill chain. Zero-day attacks exploit unknown vulnerabilities and give attackers the upper hand against conventional defenses. Honeypots have been used to deceive attackers by misrepresenting the true state of the network. Existing work on cyber deception does not model zero-day attacks. In this paper, we address the question of"How to allocate honeypots over the network?" to protect its most valuable assets. To this end, we develop a two-player zero-sum game theoretic approach to study the potential reconnaissance tracks and attack paths that attackers may use. However, zero-day attacks allow attackers to avoid placed honeypots by creating new attack paths. Therefore, we introduce a sensitivity analysis to investigate the impact of different zero-day vulnerabilities on the performance of the proposed deception technique. Next, we propose several mitigating strategies to defend the network against zero-day attacks based on this analysis. Finally, our numerical results validate our findings and illustrate the effectiveness of the proposed defense approach. Copyright © 2023, The Authors. All rights reserved.

关键词： Game theory

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Computing Safest $st$-Paths in Backbone Networks: Efficiently Solvable Cases and Fast Heuristics

Computing Safest $st$-Paths in Backbone Networks: Efficientl...

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International Workshop on Reliable Networks Design and Modeling (RNDM)

作者： Balázs Vass Péter Revisnyei Alija Pašíć Department of Telecommunications and Artificial Intelligence (TMIT) Faculty of Electrical Engineering and Informatics (VIK) High Speed Networks Laboratory (HSNLab) Budapest University of Technology and Economics (BME) Budapest Hungary Faculty of Mathematics and Computer Science Babeș-Bolyai University Cluj Napoca Romania HUN-REN-BME Information Systems Research Group Budapest Hungary

ISBN: (数字)9798331518189

ISBN: (纸本)9798331518196

For proper evaluation and optimization of the expected availability of a backbone network service, two related very fundamental modeling and algorithmic questions are the following. 1) Realistically estimate the availability of a given $st$ -path for some source and target pair of communicating nodes $s$ and $t$ , and 2) Find a safest $st$ -path. For these, traditional approaches suppose network element failures are independent. In this paper, we show that, by not considering joint failure probabilities, the traditional approaches may misguess the $st$ -path availabilities and, consequently, the total connection availability, which can lead to more frequent Service Level Agreement (SLA) violations and a financial burden on the Communication Service Provider (CSP). Due to the inconsistent estimations, the supposedly safest paths yielded by these approaches may turn out to be suboptimal. On the positive side, we propose a fast algorithmic approach, that, under some assumptions, returns with a (truly) safest $st$ -path accompanied by its exact expected availability. When the aforementioned assumptions do not hold, in our simulations, the proposed algorithmic scheme proved to be a good heuristic, performing at least as well as the traditional method.

关键词： Computational modeling Heuristic algorithms Conferences Estimation Hazards Computational efficiency Optimization Service level agreements

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Solid-state non-volatile memories based on vdW heterostructure-based vertical-transport ferroelectric field-effect transistors

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Science China(Information Sciences) 2024年第6期67卷 198-206页

作者： Qiyu YANG Zheng-Dong LUO Fei XIAO Junpeng ZHANG Dawei ZHANG Dongxin TAN Xuetao GAN Yan LIU Zhufei CHU Yinshui XIA Genquan HAN Hangzhou Institute of Technology Xidian University State Key Discipline Laboratory of Wide Band Gap Semiconductor Technology School of MicroelectronicsXidian University School of Artificial Intelligence Xidian University School of Materials Science and Engineering University of New South Wales (UNSW) Sydney ARC Centre of Excellence in Future Low-Energy Electronics Technologies (FLEET) University of New South Wales (UNSW) Sydney Key Laboratory of Light Field Manipulation and Information Acquisition Ministry of Industry and Information Technologyand Shaanxi Key Laboratory of Optical Information TechnologySchool of Physical Science and TechnologyNorthwestern Polytechnical University Faculty of Electrical Engineering and Computer Science Ningbo University

Driven by the explosive development of data-centric computation applications, it is becoming urgent to develop in-memory computing devices that are beyond the von Neumann architecture with an arrangement of separated logic and memory components. The transistor-type solid-state non-volatile memories,such as ferroelectric field-effect transistors(Fe FETs), have long been regarded as a competitive candidate for future in-memory computing architectures. However, the density scaling towards high-density arrays would require advanced Fe FETs with reduced footprints, which remains a great challenge so far. Here, a vertical-transport(VT) Fe FET that flips the charge transport channel perpendicular to the substrate plane is proposed, in which a ferroelectric gate and a van der Waals(vd W) heterojunction channel are vertically integrated, effectively reducing the device footprints. The proposed VT-Fe FET shows not only the robust binary non-volatile memory states but also several key synaptic functionalities at the device level. An artificial neural network with supervised learning was simulated based on the device conductance switching properties,showing excellent classification accuracy for the MNIST handwritten digits. These findings suggest that the proposed VT-Fe FET could offer a new solution for future non-volatile memories as well as more advanced neuromorphic systems.

关键词： vdW heterostructure non-volatile memory vertical-transport transistor ferroelectric field-effect transistor memristive devices

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Intelligent Reflecting Surface OFDM Communication with Deep Neural Prior

Intelligent Reflecting Surface OFDM Communication with Deep ...

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IEEE International Conference on Communications (ICC)

作者： Tomer Fireaizen Gal Metzer Dan Ben-David Yair Moshe Israel Cohen Emil Bjö rnson Signal and Image Processing Laboratory (SIPL) Faculty of Electrical &amp Computer Engineering Technion &#x2013 Israel Institute of Technology Haifa Israel Faculty of Engineering Tel Aviv University Tel Aviv Israel Dept. of Electrical Engineering (ISY) Link&#x00F6 ping University and KTH Royal Insitute of Technology Sweden

ISBN: (数字)9781538683477

ISBN: (纸本)9781538683484

An Intelligent Reflecting Surface (IRS) is an emerging technology for improving the data rate over wireless channels by controlling the underlying channel. In this paper, we describe a novel solution for IRS configuration to maximize the data rate over wideband channels. The optimization is obtained by online training of a deep generative neural network. Inspired by related works in image processing, this network is randomly initialized and acts as a regularization term for the optimization process since the structure of the generator is sufficient to capture a great deal of IRS statistics prior to any learning. In contrast to recent deep learning techniques for IRS configuration, the proposed technique does not require an offline training stage and can adapt quickly to any environment. Compared to the previous state-of-the-art algorithm, the proposed method is significantly faster and obtains IRS configurations that achieve higher data transmission rates.

关键词： Wireless communication Training Deep learning OFDM Image processing Neural networks Generators

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Drying-Mediated Self-Assembly of Graphene for Inkjet Printing of High-Rate Micro-supercapacitors

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Nano-Micro Letters 2020年第3期12卷 155-168页

作者： Szymon Sollami Delekta Mika‑Matti Laurila Matti Mantysalo Jiantong Li School of Electrical Engineering and Computer Science KTH Royal Institute of TechnologyElectrum 22916440 KistaSweden Laboratory for Future Electronics Faculty of Information Technology and Communication SciencesTampere University33720 TampereFinland

Scalable fabrication of high-rate micro-supercapacitors(MSCs)is highly desired for on-chip integration of energy storage *** virtue of the special self-assembly behavior of 2D materials during drying thin films of their liquid dispersion,a new inkjet printing technique of passivated graphene micro-flakes is developed to directly print MSCs with 3D networked porous *** presence of macroscale through-thickness pores provides fast ion transport pathways and improves the rate capability of the devices even with solid-state *** multiple-pass printing,the porous microstructure effectively absorbs the successively printed inks,allowing full printing of 3D structured MSCs comprising multiple vertically stacked cycles of current collectors,electrodes,and sold-state *** all-solid-state heterogeneous 3D MSCs exhibit excellent vertical scalability and high areal energy density and power density,evidently outperforming the MSCs fabricated through general printing techniques.

关键词： High-rate micro-supercapacitor 3D micro-supercapacitor Drying-mediated self-assembly Graphene Inkjet printing

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Short-term solar eruptive activity prediction models based on machine learning approaches:A review

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Science China Earth Sciences 2024年第12期67卷 3727-3764页

作者： Xin HUANG Zhongrui ZHAO Yufeng ZHONG Long XU Marianna B.KORSÓS R.ERDÉLYI Faculty of Electrical Engineering and Computer Science Ningbo UniversityNingbo 315211China State Key Laboratory of Space Weather National Space Science CenterChinese Academy of SciencesBeijing 100190China School of Astronomy and Space Science University of Chinese Academy of SciencesBeijing 100049China Beijing Cellular Exploration Science and Technology Center Beijing 100190China Dipartimento di Fisica e Astronomia“Ettore Majorana” Universitàdi CataniaCatania I 95123Italy Department of Astronomy Eötvös Loránd UniversityBudapest H-1112Hungary Hungarian Solar Physics Foundation Gyula H-5700Hungary Solar Physics&Space Plasma Research Center(SP2RC) School of Mathematics and StatisticsUniversity of SheffieldSheffield S37RHUK

Solar eruptive activities,mainly including solar flares,coronal mass ejections(CME),and solar proton events(SPE),have an important impact on space weather and our *** short-term solar eruptive activity prediction is an active field of research in the space weather ***,statistical,and machine learning methods are proposed to build prediction models of the solar eruptive *** the development of space-based and ground-based facilities,a large amount of observational data of the Sun is accumulated,and data-driven prediction models of solar eruptive activities have made a significant *** this review,we briefly introduce the machine learning algorithms applied in solar eruptive activity prediction,summarize the prediction modeling process,overview the progress made in the field of solar eruptive activity prediction model,and look forward to the possible directions in the future.

关键词： Solar flare Coronal mass ejection Solar proton event Machine learning Prediction model

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Reconstruction Error-Based Efficient Deep Neural Network for Diabetes Prediction in the Korean Population 1

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16th International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IIH-MSP 2020 in conjunction with the 13th International Conference on Frontiers of Information Technology, Applications and Tools, FITAT 2020

作者： Amarbayasgalan, Tsatsral Li, Meijing Namsrai, Oyun-Erdene Ryu, Keun Ho Database Laboratory School of Electrical and Computer Engineering Chungbuk National University Cheongju28644 Korea Republic of College of Information Engineering Shanghai Maritime University Shanghai China School of Engineering and Applied Sciences National University of Mongolia Ulaanbaatar Mongolia Faculty of Information Technology Ton Duc Thang University Ho Chi Minh City700000 Viet Nam Department of Computer Science College of Electrical and Computer Engineering Chungbuk National University Cheongju28644 Korea Republic of

ISBN: (数字)9789813367579

ISBN: (纸本)9789813367562

The World Health Organization (WHO) reported that diabetes is now one of the top ten causes of global mortality, and it is also highly ranked in Korea. The poor lifestyle, such as lack of physical activity, unhealthy diet, overweight, and tobacco usage, directly increases the risk of diabetes. It is asymptomatic at the early stage, and most patients may be diagnosed several years after onset. Over time, it can lead to cardiovascular diseases, kidney failure, blindness, and limb amputation. Nowadays, machine learning-based approaches can play an essential role in the healthcare domain for disease prediction. In this study, we have proposed a deep neural network (DNN) with a reconstruction error (RE)-based feature to predict diabetes. First, a deep autoencoder (DAE) model is used to calculate the RE-based new feature. Then, the DNN classifier learns from a combination of the new feature and other risk factors to predict diabetes. We have performed an experiment on the Korea National Health and Nutrition Examination Survey (KNHANES) dataset and compared with typical machine learning algorithms. As a result of our experiment, proposed RE-based DNN outperformed regular machine learning classifiers. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021.

关键词： Deep neural networks

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CryptoPyt: Unraveling Python Cryptographic APIs Misuse with Precise Static Taint Analysis

CryptoPyt: Unraveling Python Cryptographic APIs Misuse with ...

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Annual computer Security Applications Conference

作者： Xiangxin Guo Shijie Jia Jingqiang Lin Yuan Ma Fangyu Zheng Guangzheng Li Bowen Xu Yueqiang Cheng Kailiang Ji School of Cyber Security University of Science and Technology of China Hefei China Key Laboratory of Cyberspace Security Defense Institute of Information Engineering CAS Beijing China School of Cryptology University of Chinese Academy of Sciences Beijing China Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo China NIO China

ISBN: (数字)9798331520885

ISBN: (纸本)9798331520892

Cryptographic APIs are essential for ensuring the security of software systems. However, many research studies have revealed that the misuse of cryptographic APIs is commonly widespread. Detecting such misuse in Python poses challenges due to its intricate features, including dynamic features and pass-by-object-reference. Existing tools lack the precision and accuracy to tackle these challenges, leading to both high false positives and false negatives. In this work, we propose a specific Python Cryptographic Abstract Syntax Tree (PCAST) to represent the structure of source code, which rewrites AST nodes to handle complex Python features. Based on PCAST, we design and implement CryptoPyt, a static code analysis tool that leverages precise taint analysis and 17 cryptographic misuse rules to automatically identify potential cryptographic APIs misuse in Python projects. We conduct an in-depth analysis of all the APIs within the popular 21 Python cryptographic libraries and design five kinds of taint detectors to perform intra-procedural and inter-function analysis on the APIs and arguments. To demonstrate the effectiveness of CryptoPyt, we conduct experiments with six state-of-the-art tools (i.e., Cryptolation, LICMA, Bandit, Dlint, Semgrep and CodeQL) on both the labeled benchmark PyCryptoBench and the real-world Python cryptographic projects datasets PCAMD. Our evaluations show that CryptoPyt achieves an F1 score of 0.80 on PyCryptoBench and a recall rate of 99.08% on PCAMD. Furthermore, we disclose the discovered critical issues to the developers and seven high-level CVE IDs have been assigned to these findings. Our tool contributes to enhancing the security of Python cryptographic software.

关键词： Source coding Static analysis Detectors Syntactics Feature extraction Software systems Libraries Cryptography computer security Python

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Neural network based model for image detection in seismic electromagnetic wave

Journal of Network Intelligence

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Journal of Network Intelligence 2021年第4期6卷 715-727页

作者： Ding, Wei Han, Ji Song, Lan Wang, Dijin Key Laboratory of Earthquake Geodesy CEA Institute of Seismology CEA Hubei Earthquake Administration Wuhan China Jackie Chan movie and media college Wuhan Institute of Design and Sciences Wuhan China Electrical and Computer Engineering Faculty of Engineering and Applied Science Memorial University St. JohnsNLA1B 3X5 Canada

The composition of natural seismic electromagnet signals recorded by seismic instruments is complex, which cannot be directly used to analyze ground motion events information with common text data analysis methods. The fractal dimension of seismic trace time series are used to study the similarity of seismic waves in the characteristics of complex geological structures and breakpoint locations. The basis of extract-ing seismic electromagnet waveform image texture feature is to realize image recognition. Two-dimensional waveform texture features can be used to identify two-dimensional time-amplitude seismic waveform signals. This paper proposes the application of STFT algorithm to curve fitting of seismic electromagnetic waveform signal, which can be used to generate the filled and binarized seismic waveform image. An improved VGG neural network is used to realize the identification of related images, which transform electromagnetic signal recognition into an object detection problem. 1437 real-time earthquake cases collected from 165 stations in Sichuan-Yunnan region are applied to verify the performance of VGG model. The result shows that the training and detection accuracy rate of the data set can reach 90%. The seismic electromagnetic signals processed by the STFT algorithm are more conducive to seismic identification, and suitable image features can be extracted, which has certain reference value for the identification of small sets of natural seismic events. © 2021, Taiwan Ubiquitous Information CO LTD. All rights reserved.

关键词： Convolutional neural networks

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Transparent Blockchain-Based Voting System: Guide to Massive Deployments 6th

Transparent Blockchain-Based Voting System: Guide to Massive...

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6th International Conference on Advanced Intelligent Systems and Informatics, AISI 2020

作者： Fatrah, Aicha El Kafhali, Said Salah, Khaled Haqiq, Abdelkrim Computer Networks Mobility and Modeling Laboratory: IR2M Faculty of Sciences and Techniques Hassan First University of Settat Settat26000 Morocco Electrical and Computer Engineering Department Khalifa University of Science and Technology Abu Dhabi United Arab Emirates

ISBN: (纸本)9783030586683

With the development of society and its people democratic consciousness, voting as a crucial canal of democracy has to satisfy high expectations of modern society, new technologies and techniques must be deployed for better voting experience, and to replace traditional cost inefficient and laborious traditional paper voting. Blockchain technology is currently disrupting every industry where security and data integrity are prioritized. In this paper, we leveraged this technology to propose a design and implementation of a blockchain-based electronic voting system for large scale election. The novelty in this paper when compared to other state of the art blockchain-based voting systems is that it respects voters privacy with a full transparency for auditing and user-friendly terminals, which will boost the confidence of people in the voting system and therefor increase the number of participants in the election. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Smart contract

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