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International Conference on System Theory, Control, and Computing (ICSTCC)

作者： Flavius Opriţoiu Mircea Vlăduţiu Computer Science and Engineering Department Advanced Computing Systems and Architectures (ACSA) Laboratory Politehnica University Timisoara 2 V.Parvan Blvd Timisoara Romania

This paper presents a systolic array architecture for General Matrix Multiplication. The system was designed and verified using the Verilog description language. The architecture was constructed for educational use aiming to complement the practical activities of computer Architecture classes. The proposed solution can be used as a design space exploration tool for evaluation of matrix multiplication accelerators based on non-stationary systolic arrays. Both the width and format of the processed operands as well as the number of stages of the systolic array’s Processing Elements can be customized. As a case study, the design and performance of a systolic array for accelerating the multiplication of square matrices of signed, 8-bit integers, is presented. The overall architecture was synthesized for the Altera DE2 FPGA board in order to evaluate its performance.

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5G-Fuzz: An Attack Generator for Fuzzing 5GC, using Generative Adversarial Networks

5G-Fuzz: An Attack Generator for Fuzzing 5GC, using Generati...

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2023 IEEE Globecom Workshops, GC Wkshps 2023

作者： Nakas, George Radoglou-Grammatikis, Panagiotis Amponis, George Lagkas, Thomas Argyriou, Vasileios Goudos, Sotirios Sarigiannidis, Panagiotis K3Y Ltd William Gladstone 31 Sofia1000 Bulgaria University of Western Macedonia Department of Electrical and Computer Engineering Campus ZEP Kozani Kozani50100 Greece International Hellenic University Department of Computer Science Kavala Campus Kavala65404 Greece Kingston University London Department of Networks and Digital Media Penrhyn Road Surrey Kingston upon ThamesKT1 2EE United Kingdom School of Physics Aristotle University of Thessaloniki Thessaloniki54124 Greece

ISBN: (纸本)9798350370218

The evolution of fifth-generation (5G) networks represents a significant technological leap towards a seamless and advanced user experience, allowing hyperconnected use cases with faster data transfer, lower latency and better connectivity for a wide range of mobile devices. In particular, a key element of 5G is the 5G Core (5GC) which follows a service-based architecture, enabling network slicing and improved Quality of Service (QoS). However, despite the benefits of 5GC, it also creates important security and privacy concerns. First, 5GC can combine heterogeneous technologies that can increase the growing attack surface. On the other hand, 5G handles a vast amount of sensitive data that may reflect an attractive goal for potential cyberattackers. Based on the previous remarks, in this paper, we introduce 5G-Fuzz. 5G-Fuzz is a smart fuzzer which takes full advantage of historical data in order to fuzz and target the Packet Forwarding Control Protocol (PFCP) communications between the Session Management Function (SMF) and User Plane Function (UPF). For this purpose, two PFCP attacks are used. In contrast to conventional fuzzers, 5G-Fuzz adopts two Generative Adversarial Networks (GANs) in order to identify and generate the appropriate values of Session Endpoint Identifier (SEID) and sequence number (seq) utilised in the PFCP sessions, thus accelerating the PFCP attacks. Finally, 5G-Fuzz composes and replays the malicious PFCP packets against UPF. © 2023 IEEE.

关键词： 5G Fuzzing Generative Adversarial Networks PFCP

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Realizing Intelligent Spectrum Management for Integrated Satellite and Terrestrial Networks

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Journal of Communications and Information Networks 2021年第1期6卷 32-43页

作者： Ying-Chang Liang Junjie Tan Haonan Jia Jintao Zhang Lian Zhao Center for Intelligent Networking and Communications University of Electronic Science and Technology of ChinaChengdu 611731China National Key Laboratory of Science and Technology on Communications University of Electronic Science and Technology of ChinaChengdu 611731China Department of Electrical Computerand Biomedical EngineeringRyerson UniversityToronto M2J4A6ONCanada

Nowadays both satellite and terrestrial networks are expanding rapidly to meet the ever-increasing demands for higher throughput,lower latency,and wider ***,spectrum scarcity places obstacles in the sustainable *** accommodate the expanding network within a limited spectrum,spectrum sharing is deemed as a promising ***,cognitive radio(CR)has been proposed in the literature to allow satellite and terrestrial networks to share their spectrum ***,the existing CR-based schemes are found to be impractical and inefficient because they neglect the difficulty in obtaining the accurate and timely environment perception in satellite communications and only focus on link-level coexistence with limited *** this paper,we propose an intelligent spectrum management framework based on software defined network(SDN)and artificial intelligence(AI).Specifically,SDN transforms the heterogenous satellite and terrestrial networks into an integrated satellite and terrestrial network(ISTN)with reconfigurability and *** is further used to make predictive environment perception and to configure the network for optimal resource ***,the proposed framework provides a new paradigm to integrate and exploit the spectrum of satellite and terrestrial networks.

关键词： cognitive radio(CR) spectrum management satellite communications integrated satellite and terrestrial networks(ISTN)

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A Large-scale Expensive Optimization Algorithm with a Multi-view Synthetic Sampling

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IEEE Transactions on Evolutionary Computation 2025年

作者： Zhao, Kaili Wang, Xilu Sun, Chaoli Jin, Yaochu Taiyuan University of Science and Technology School of Electronic Information Engineering Taiyuan 030024 China Shanxi Engineering Vocational College department of Mechanical Engineering Taiyuan 030009 China University of Surrey School of Computer Science and Electronic Engineering Surrey GU2 7XH United Kingdom Taiyuan University of Science and Technology School of Computer Science and Technology Taiyuan 030024 China Westlake University School of Engineering Hangzhou 310030 China

Many real-world problems involve optimizing numerous decision variables and are expensive to evaluate, known as large-scale expensive optimization problems (LSEOPs). While surrogate-assisted evolutionary algorithms have proven effective for expensive problems, training proper models for LSEOPs remains challenging due to insufficient training data. In this paper, we adopt the divide-and-conquer approach, decomposing LSEOPs into lower-dimensional sub-problems and constructing models for sub-problems, and introduce a multi-view synthetic sampling technique for new sample selection. Specifically, we propose sorting all evaluated solutions in an ascending order and dividing them into intervals, from which data are sampled to obtain informative training data for models. The population for the LSEOP is updated by employing cooperative environmental selections on the population, formed by recombining all renewed populations for sub-problems to balance exploration and exploitation. Finally, a solution is selected among the current population for the true evaluation based on its multi-view performance predicted across all sub-problems. Results on CEC’2013 benchmark problems show the effectiveness and efficiency of our proposed method compared to three prevalent large-scale expensive optimization algorithms. Additionally, results on 2000-dimensional CEC’2010 benchmark problems and a 1200-dimensional real-world problem demonstrate encouraging scalability and robustness of the proposed method for addressing higher-dimensional problems. © 1997-2012 IEEE.

关键词： Divide-and-conquer strategy infill sampling large-scale expensive optimization RBF surrogate models

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HoneyDOC: An Efficient Honeypot Architecture Enabling All-Round Design

arXiv

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arXiv 2024年

作者： Fan, Wenjun Du, Zhihui Smith-Creasey, Max Fernández, David School of Computing University of Kent Canterbury CT2 7NF United Kingdom Department of Computer Science and Technology Tsinghua University Beijing100084 China School of Mathematics Computer Science and Engineering City University of London LondonEC1V 0HB United Kingdom Department of Telematics Engineering Technical University of Madrid Madrid28040 Spain

Honeypots are designed to trap the attacker with the purpose of investigating its malicious behavior. Owing to the increasing variety and sophistication of cyber attacks, how to capture high-quality attack data has become a challenge in the context of honeypot area. All-round honeypots, which mean significant improvement in sensibility, countermeasure and stealth, are necessary to tackle the problem. In this paper, we propose a novel honeypot architecture termed HoneyDOC to support all-round honeypot design and implementation. Our HoneyDOC architecture clearly identifies three essential independent and collaborative modules, Decoy, Captor and Orchestrator. Based on the efficient architecture, a Software-Defined Networking (SDN) enabled honeypot system is designed, which supplies high programmability for technically sustaining the features for capturing high-quality data. A proof-of-concept system is implemented to validate its feasibility and effectiveness. The experimental results show the benefits by using the proposed architecture comparing to the previous honeypot solutions. Copyright © 2024, The Authors. All rights reserved.

关键词： Cybersecurity

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Spatial single mode laser source interaction with measured pulse based parabolic index multimode fiber

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Journal of Optical Communications 2024年第0期45卷 s465-s473页

作者： Rashed, Ahmed Nabih Zaki Ahammad, S.K. Hasane Daher, Malek G. Sorathiya, Vishal Siddique, Abrar Asaduzzaman, Sayed Rehana, Hasin Dutta, Nitul Patel, Shobhit K. Nyangaresi, Vincent Omollo Jibon, Rayhan Habib Abdelhamid, Huda Said Electronics and Electrical Communications Engineering Department Faculty of Electronic Engineering Menoufia University Menouf32951 Egypt Department of ECE Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram522302 India Physics Department Islamic University of Gaza P.O. Box 108 Gaza Palestine Department of Information and Communication Technology Marwadi University Rajkot India Department of Smart Robot Convergence and Application Engineering Pukyong National University Busan48513 Korea Republic of Department of Electrical and Computer Engineering University of Waterloo WaterlooONN2L 3G1 Canada Department of Computer Science and Engineering Rangamati Science and Technology University Rangamati Bangladesh Department of Computer Science and Engineering Daffodil International University Dhaka Bangladesh Computer Sc and Engg. Dept. SRM University Andhra Pradesh Guntur India Electronics and Communication Engg. Department Marwadi University Gujarat Rajkot India Tom Mboya University College P.O. Box 199-40300 Homabay Kenya Electronics and Communication Engineering Discipline Khulna University Khulna9208 Bangladesh

The study clarified spatial single mode laser interaction with measured pulse based parabolic index multimode fiber. Peak power level margin, maximum/minimum signal amplitude margin after parabolic index multimode fiber are measured with core radius of 25 μm, cladding thickness of 10 μm, refractive index peak of 1.4142, length of 300 m, and refractive index step of 1%. Maximum signal power margin against spectral frequency after PIN light detector based parabolic fiber properties is tested under the same operating parameters. The signal power amplitude/power within parabolic index multimode fiber is also measured based on variations of fiber lengths and relative refractive index step. The study implies the multimode graded index fibers with parabolic or near parabolic index profile cores have transmission bandwidths than other multimode fibers. © 2022 Walter de Gruyter GmbH, Berlin/Boston.

关键词： Multimode fibers

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Secure and Privacy-Preserving Decision Tree Classification with Lower Complexity

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Journal of Communications and Information Networks 2020年第1期5卷 16-25页

作者： Liang Xue Dongxiao Liu Cheng Huang Xiaodong Lin Xuemin(Sherman)Shen Department of Electrical and Computer Engineering University of WaterlooWaterloo N2L 3G1Canada School of Computer Science University of GuelphGuelph N1G 2W1Canada Department of Electrical and Computer EngineeringUniversity of WaterlooWaterloo N2L 3G1Canada

As a widely-used machine-learning classifier,a decision tree model can be trained and deployed at a service provider to provide classification services for clients,e.g.,remote *** address privacy concerns regarding the sensitive information in these services(i.e.,the clients’inputs,model parameters,and classification results),we propose a privacy-preserving decision tree classification scheme(PDTC)in this ***,we first tailor an additively homomorphic encryption primitive and a secret sharing technique to design a new secure two-party comparison protocol,where the numeric inputs of each party can be privately compared as a whole instead of doing that in a bit-by-bit ***,based on the comparison protocol,we exploit the structure of the decision tree to construct PDTC,where the input of a client and the model parameters of a service provider are concealed from the counterparty and the classification result is only revealed to the client.A formal simulation-based security model and the security proof demonstrate that PDTC achieves desirable security *** addition,performance evaluation shows that PDTC achieves a lower communication and computation overhead compared with existing schemes.

关键词： decision trees data privacy model privacy secure comparison machine-learning classification

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Reconfigurable SIW Liquid Metal Phase Shifter for mm- Wave Phased Arrays

Reconfigurable SIW Liquid Metal Phase Shifter for mm- Wave P...

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Antennas and Propagation & USNC/URSI National Radio science Meeting

作者： Shaker Alkaraki Zhishu Qu Syeda Fizzah Jilani James R. Kelly Electrical and Electronic Engineering Department George Green Institute for Electromagnetic Research University of Nottingham Nottingham NG7 2RD UK School of Computer Science Fudan University Shanghai China Department of Physics Physical Sciences Building Aberystwyth University Aberystwyth UK The School of Electronic Engineering and Computer Science Queen Mary University of London London U.K.

ISBN: (数字)9798350369908

ISBN: (纸本)9798350369915

This paper introduces a millimeter-wave (mm-wave) phase shifters based on Gallium based liquid metal (LM), presenting an exceptional low insertion loss (IL) performance. The phase shifter can provide a measured phase shift of up to 180° with a precision of 22.5°. Operating within the 26GHz 5G band, this proposed phase shifter exhibits low IL, measuring below 1.6 dB at 26GHz and it achieves an outstanding figure of merit (FoM) of 117.1°/dB. Advantages of the proposed phase shifter include seamless integration with SIW-based feeding networks for mm-wave phased array antennas. Phase control is facilitated by liquid metal-filled vias, allowing for dynamic reconfiguration. Liquid metal is introduced into the via holes when needed and emptied when the via is no longer required. This reconfiguration approach holds promise for various reconfigurable mm-wave devices across a wide range of applications.

关键词： Phased arrays Performance evaluation Liquids Phase measurement Power transmission lines Phase shifters Metals

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Heuristic Modularity Maximization Algorithms for Community Detection Rarely Return an Optimal Partition or Anything Similar 23rd

Heuristic Modularity Maximization Algorithms for Community ...

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23rd International Conference on Computational science, ICCS 2023

作者： Aref, Samin Mostajabdaveh, Mahdi Chheda, Hriday Department of Mechanical and Industrial Engineering University of Toronto TorontoM5S 3G8 Canada Huawei Technologies Canada BurnabyV5C 6S7 Canada Department of Computer Science University of Toronto TorontoM5S 2E4 Canada

ISBN: (纸本)9783031360268

Community detection is a fundamental problem in computational sciences with extensive applications in various fields. The most commonly used methods are the algorithms designed to maximize modularity over different partitions of the network nodes. Using 80 real and random networks from a wide range of contexts, we investigate the extent to which current heuristic modularity maximization algorithms succeed in returning maximum-modularity (optimal) partitions. We evaluate (1) the ratio of the algorithms’ output modularity to the maximum modularity for each input graph, and (2) the maximum similarity between their output partition and any optimal partition of that graph. We compare eight existing heuristic algorithms against an exact integer programming method that globally maximizes modularity. The average modularity-based heuristic algorithm returns optimal partitions for only 19.4% of the 80 graphs considered. Additionally, results on adjusted mutual information reveal substantial dissimilarity between the sub-optimal partitions and any optimal partition of the networks in our experiments. More importantly, our results show that near-optimal partitions are often disproportionately dissimilar to any optimal partition. Taken together, our analysis points to a crucial limitation of commonly used modularity-based heuristics for discovering communities: they rarely produce an optimal partition or a partition resembling an optimal partition. If modularity is to be used for detecting communities, exact or approximate optimization algorithms are recommendable for a more methodologically sound usage of modularity within its applicability limits. © The Author(s) 2023.

关键词： Heuristic algorithms

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ENHANCING NEURAL SPOKEN LANGUAGE RECOGNITION: AN EXPLORATION WITH MULTILINGUAL DATASETS

arXiv

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arXiv 2025年

作者： Anidjar, Or Haim Yozevitch, Roi Department of Computer Software & Engineering Ariel University Golan Heights 1 Ariel4077625 Israel Department of Electrical Engineering Ariel University Golan Heights 1 Ariel4077625 Israel Faculty of Computer Science College of Management Elie Wiesel 2 Rishon LeTsiyon7579806 Israel Ariel Cyber Innovation Center Ariel University Golan Heights 1 Ariel4077625 Israel

In this research, we advanced a spoken language recognition system, moving beyond traditional feature vector-based models. Our improvements focused on effectively capturing language characteristics over extended periods using a specialized pooling layer. We utilized a broad dataset range from Common-Voice, targeting ten languages across Indo-European, Semitic, and East Asian families. The major innovation involved optimizing the architecture of Time Delay Neural Networks. We introduced additional layers and restructured these networks into a funnel shape, enhancing their ability to process complex linguistic patterns. A rigorous grid search determined the optimal settings for these networks, significantly boosting their efficiency in language pattern recognition from audio samples. The model underwent extensive training, including a phase with augmented data, to refine its capabilities. The culmination of these efforts is a highly accurate system, achieving a 97% accuracy rate in language recognition. This advancement represents a notable contribution to artificial intelligence, specifically in improving the accuracy and efficiency of language processing systems, a critical aspect in the engineering of advanced speech recognition technologies. Copyright © 2025, The Authors. All rights reserved.

关键词： Speech recognition

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