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17th International Conference on Computational Intelligence in Security for Information Systems, CISIS 2024 and the 15th International Conference on EUropean Transnational Education, ICEUTE 2024

作者： AlOtaibi, Nourah Felemban, Muhamad Mahmood, Sajjad Information and Computer Science Department KFUPM Dhahran Saudi Arabia Dammam Saudi Arabia Computer Engineering Department KFUPM Dhahran Saudi Arabia Interdisciplinary Research Center for Intelligent Secure Systems Dhahran Saudi Arabia

ISBN: (纸本)9783031750151

Federated learning (FL) enhances data privacy by enabling decentralized model training without sharing local data. However, FL is vulnerable to Label-Flipping-Attacks (LFA), where malicious clients flip data labels. Traditionally, cloud-centric centralized defenses against LFA pose security and efficiency challenges. We propose a novel edge-based decentralized LFA detection method using edge servers for efficient detection. The proposed method considers the overall and class-wise accuracy in identifying suspicious clients. Initially, we adopt a strict zero-tolerance approach by excluding the entire update from detected malicious clients. We then experiment with four aggregation techniques-subtracting, masking, clipping, and reweighting–to handle the malicious parts of updates by focusing on the final layer neurons corresponding to specific classes. Experiments using three datasets demonstrate the effectiveness, robustness, and efficiency of our method, showing improved model performance and reduced latency under adversarial conditions. This approach improves the security and reliability of FL systems while maintaining data privacy. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Federated learning

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A Provably Secure Lightweight Key Agreement Protocol for Wireless Body Area Networks in Healthcare System

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IEEE Transactions on Industrial Informatics 2023年第2期19卷 1683-1690页

作者： Zia, Maryam Obaidat, Mohammad S. Mahmood, Khalid Shamshad, Salman Saleem, Muhammad Asad Chaudhry, Shehzad Ashraf Comsats University Islamabad Department of Computer Science Sahiwal57000 Pakistan University of Jordan King Abdullah Ii School of Information Technology Amman11942 Jordan University of Science and Technology Beijing School of Computer and Communication Engineering Beijing100083 China Amity University UP Noida201301 India National Yunlin University of Science and Technology Future Technology Research Center Yunlin64002 Taiwan The University of Lahore Department of Software Engineering Lahore54590 Pakistan Abu Dhabi University Department of Computer Science and Information Technology College of Engineering Abu Dhabi United Arab Emirates Nisantasi University Department of Computer Engineering Faculty of Engineering and Architecture Istanbul34398 Turkey

Wireless Body Area Network (WBAN) is a vital application of the Internet of Things (IoT) that plays a significant role in gathering a patient's healthcare information. This collected data helps special professionals like doctors or physicians analyze patients' health status to cure different diseases. However, collecting such information from an insecure channel can be threatening due to the potential security threats. Therefore, it is crucial to secure this sensitive information. This article proposes a secure and lightweight authentication protocol for WBAN. The devised protocol is scalable, secure, and lightweight compared to various relevant competing protocols. The informal security analysis shows that the designed protocol is lightweight, secure, and efficient in resisting various major attacks. The performance analysis demonstrates our protocol's supremacy over various competing protocols in terms of computation and communication costs, inducing efficiency of 20.3% and 12.3%, respectively. Moreover, the practical performance of the designed protocol from the network point of view is measured using the widely recognized NS3 simulation tool. © 2005-2012 IEEE.

关键词： Authentication

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ElectrodeNet - A Deep-Learning-Based Sound Coding Strategy for Cochlear Implants

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IEEE Transactions on Cognitive and Developmental Systems 2024年第1期16卷 346-357页

作者： Huang, Enoch Hsin-Ho Chao, Rong Tsao, Yu Wu, Chao-Min Department of Electrical Engineering National Central University Taoyuan320317 Taiwan Research Center for Information Technology Innovation Academia Sinica Taipei115201 Taiwan Department of Computer Science and Information Engineering National Cheng Kung University Tainan701401 Taiwan Department of Electrical Engineering Chung Yuan Christian University Taoyuan320314 Taiwan

ElectrodeNet, a deep-learning-based sound coding strategy for the cochlear implant (CI), is proposed to emulate the advanced combination encoder (ACE) strategy by replacing the conventional envelope detection using various artificial neural networks. The extended ElectrodeNet-CS strategy further incorporates the channel selection (CS). Network models of deep neural network (DNN), convolutional neural network (CNN), and long short-term memory (LSTM) were trained using the fast Fourier transformed bins and channel envelopes obtained from the processing of clean speech by the ACE strategy. Objective speech understanding using short-time objective intelligibility (STOI) and normalized covariance metric (NCM) was estimated for ElectrodeNet using CI simulations. Sentence recognition tests for vocoded Mandarin speech were conducted with normal-hearing listeners. DNN, CNN, and LSTM-based ElectrodeNets exhibited strong correlations to ACE in objective and subjective scores using mean squared error (MSE), linear correlation coefficient (LCC), and Spearman's rank correlation coefficient (SRCC). The ElectrodeNet-CS strategy was capable of producing N-of-M compatible electrode patterns using a modified DNN network to embed maxima selection, and to perform in similar or even slightly higher average in STOI and sentence recognition compared to ACE. The methods and findings demonstrated the feasibility and potential of using deep learning in the CI coding strategy. © 2023 IEEE.

关键词： Cochlear implants

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Multiphoton Resonance Meets Tunneling Ionization: High-Efficient Photoexcitation in Strong-Field-Dressed Ions

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Physical Review Letters 2024年第11期133卷 113201页

作者： Yewei Chen Hongbin Lei Quanjun Wang Hongqiang Xie He Zhang Xu Lu Ning Zhang Shunlin Huang Yuzhu Wu Jianpeng Liu Qian Zhang Yi Liu Zengxiu Zhao Jing Zhao Jinping Yao State Key Laboratory of High Field Laser Physics School of Optical-Electrical and Computer Engineering Department of Physics School of Science Center of Materials Science and Optoelectronics Engineering School of Microelectronics

Tunneling ionization, a fascinating quantum phenomenon, has played the key role in the development of attosecond physics. Upon absorption of a few tens of photons, tunneling ionization creates ions in different excited states and even enables the formation of population inversion between ionic states. However, the underlying physics is still being debated. Here, we demonstrate a significant enhancement in the efficiency of multiphoton excitation when ionization of neutral molecules and resonant excitation of ions coexist in strong laser fields. It facilitates the dramatic increase in population inversion and lasing radiation in N2+ around 1000 nm pump wavelength. Utilizing the ionization-coupling theory, we discover that the synergistic interplay between tunneling ionization and multiphoton excitation enables the ionic coherence to be maximized by phase locking of the periodically created ionic dipoles and consistently maintain an optimal phase for the follow-up photoexcitation. This Letter provides new insights into the photoexcitation mechanism of ions in strong laser fields and opens up a route for optimizing ionic lasing radiations.

关键词： Multiphoton or tunneling ionization & excitation Optical transient phenomena Stark effect Ultrafast phenomena

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Surface-dominated conductance scaling in Weyl semimetal NbAs

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npj Computational Materials 2024年第1期10卷 2370-2378页

作者： Sushant Kumar Yi-Hsin Tu Sheng Luo Nicholas A.Lanzillo Tay-Rong Chang Gengchiau Liang Ravishankar Sundararaman Hsin Lin Ching-Tzu Chen Department of Materials Science&Engineering Rensselaer Polytechnic Institute1108th StTroyNY 12180USA IBM Thomas J.Watson Research Center 1101 Kitchawan RoadYorktown HeightsNY 10598USA Department of Physics Cheng Kung UniversityTainan CityTaipei Department of Electrical and Computer Engineering College of Design and EngineeringNational University of SingaporeSingaporeSingapore IBM Research 257 Fuller RoadAlbanyNY 12203USA Institute of Physics Academia SinicaTaipeiTaipei

Protected surface states arising fromnon-trivial bandstructure topology in semimetals can potentially enable advanced device functionalities in compute,memory,interconnect,sensing,and *** necessitates a fundamental understanding of surface-state transport in nanoscale topological ***,we investigate quantum transport in a prototypical topological semimetal NbAs to evaluate the potential of this class of materials for beyond-Cu interconnects in highly-scaled integrated *** density functional theory(DFT)coupled with non-equilibrium Green’s function(NEGF)calculations,we show that the resistance-areaRAproduct in NbAs films decreases with decreasing thickness at the nanometer scale,in contrast to a nearly constant RA product in ideal Cu *** anomalous scaling originates from the disproportionately large number of surface conduction states which dominate the ballistic conductance by up to 70%in NbAs thin *** also show that this favorable RA scaling persists even in the presence of surface defects,in contrast to RA sharply increasing with reducing thickness for films of conventional metals,such as Cu,in the presence of surface *** results underscore the potential of topological semimetals as future back-end-of-line(BEOL)interconnect metals.

关键词： scaling topological conductance

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Joint Optimization of Compression, Transmission and Computation for Cooperative Perception Aided Intelligent Vehicular Networks

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IEEE Transactions on Vehicular Technology 2025年第5期74卷 8201-8214页

作者： Lu, Binbin Huang, Xumin Wu, Yuan Qian, Liping Zhou, Sheng Niyato, Dusit University of Macau State Key Laboratory of Internet of Things for Smart City Department of Computer and Information Science 999078 China Guangdong University of Technology School of Automation Guangzhou510006 China University of Macau State Key Lab of Internet of Things for Smart City 999078 China University of Macau State Key Laboratory of Internet of Things for Smart City Department of Computer Information Science 999078 China Zhuhai UM Science and Technology AQ2 Research Institute Zhuhai519301 China Zhejiang University of Technology Institute of Cyberspace Security Hangzhou310023 China Tsinghua University Beijing National Research Center for Information Science and Technology Department of Electronic Engineering Beijing100084 China Nanyang Technological University School of Computer Science and Engineering 639815 Singapore

Cooperative perception is a promising paradigm to tackle the perception limitations of a single intelligent vehicle (IV) to enhance the driving safety and efficiency in intelligent vehicular networks. However, the real-time transmission and computation-intensive fusion of raw sensing data raise new challenges for satisfying the stringent delay requirement of delay-sensitive applications. In this paper, we formulate a joint optimization problem of the cooperative IV selection, compression ratio selection, transmit power control, task offloading decision and computation allocation to minimize the end-to-end delay consisting of compression, transmission and computation. In particular, to meet the perception probability requirement, the quality and quantity-aware matching algorithm is proposed to optimize the cooperative IV selection. To guarantee the queue stability of offloading tasks, we develop the Lyapunov optimization algorithm to determine the upper bound of the offloading tasks and the corresponding optimal computation allocation. The Lyapunov aided deep reinforcement learning algorithm is further proposed to dynamically adjust the task offloading decision and compression ratio selection to minimize the end-to-end delay with the transmit power control being optimized by the successive convex approximation algorithm. Simulation results demonstrate that, compared to several benchmark algorithms, our proposed algorithm achieves the lowest end-to-end delay while guaranteeing the requirements on perception probability and queue stability effectively. © 1967-2012 IEEE.

关键词： Deep reinforcement learning

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A nano-scale design of a multiply-accumulate unit for digital signal processing based on quantum computing

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Optical and Quantum Electronics 2024年第1期56卷 1-25页

作者： Ahmadpour, Seyed-Sajad Navimipour, Nima Jafari Yalcin, Senay Bakhshayeshi Avval, Danial Ain, Noor Ul Department of Computer Engineering Faculty of Engineering and Natural Sciences Kadir Has University Istanbul Turkey Future Technology Research Center National Yunlin University of Science and Technology Douliou 64002 Taiwan Department of Computer Engineering Nisantasi University Istanbul Turkey Department of Information Systems Engineering Sakarya University Sakarya Turkey Department of Business Administration Kadir Has University Istanbul Turkey

Digital signal processing (DSP) is used in computer processing to conduct different signal-processing tasks. The DSPs are used in the series numbers representing a continuous variable in a domain such as time, area, or frequency. The multiply-accumulate (MAC) unit is crucial in various DSP applications, including convolution, discrete cosine transform (DCT), Fourier Transform, etc. Thus, all DSPs contain a critical MAC unit in signal processing. The MAC unit conducts multiplication and accumulation operations for continuous and complicated DSP application processes. On the other hand, in the MAC structure, the stability of the circuit and the occupied area pose some significant challenges. However, high-performance quantum technology can easily overcome all the previous shortcomings. Hence, this paper suggests an efficient MAC for DSP applications using a Vedic multiplier, half adder, and accumulator based on quantum technology. All the proposed structures have used a single-layer layout without rotated cells. The suggested architecture is designed and validated based on the QCADesigner 2.0.3 tool. The findings revealed that all the developed circuits have a simple architecture with fewer quantum cells, optimal area, and low latency. © 2023, The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature.

关键词： Multiply-accumulate (MAC) Nano-design Quantum dot cellular automata (QCA) Signal processing

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A Low Complexity ML-Based Methods for Malware Classification

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computers, Materials & Continua 2024年第9期80卷 4833-4857页

作者： Mahmoud E.Farfoura Ahmad Alkhatib Deema Mohammed Alsekait Mohammad Alshinwan Sahar A.El-Rahman Didi Rosiyadi Diaa Salama Abd Elminaam Cybersecurity Department Al-Zaytoonah University of JordanAmman11733Jordan Department of Computer Science and Information Technology Applied CollegePrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Faculty of Information Technology Applied Science Private UniversityAmman11931Jordan Computer Systems Program-Electrical Engineering Department Faculty of Engineering-ShoubraBenha UniversityCairo11629Egypt Research Center for Artificial Intelligence and Cybersecurity Electronics and Informatics OrganizationNational Research and Innovation Agency(BRIN)KST Samaun SamadikunBandung40135Republic of Indonesia Jadara Research Center Jadara UniversityIrbid21110Jordan MEU Research Unit Middle East UniversityAmman11831Jordan

The article describes a new method for malware classification,based on a Machine Learning(ML)model architecture specifically designed for malware detection,enabling real-time and accurate malware *** an innovative feature dimensionality reduction technique called the Interpolation-based Feature Dimensionality Reduction Technique(IFDRT),the authors have significantly reduced the feature space while retaining critical information necessary for malware *** technique optimizes the model’s performance and reduces computational *** proposed method is demonstrated by applying it to the BODMAS malware dataset,which contains 57,293 malware samples and 77,142 benign samples,each with a 2381-feature *** the IFDRT method,the dataset is transformed,reducing the number of features while maintaining essential data for accurate *** evaluation results show outstanding performance,with an F1 score of 0.984 and a high accuracy of 98.5%using only two reduced *** demonstrates the method’s ability to classify malware samples accurately while minimizing processing *** method allows for improving computational efficiency by reducing the feature space,which decreases the memory and time requirements for training and *** new method’s effectiveness is confirmed by the calculations,which indicate significant improvements in malware classification accuracy and *** research results enhance existing malware detection techniques and can be applied in various cybersecurity applications,including real-timemalware detection on resource-constrained *** and scientific contribution lie in the development of the IFDRT method,which provides a robust and efficient solution for feature reduction in ML-based malware classification,paving the way for more effective and scalable cybersecurity measures.

关键词： Malware detection ML-based models dimensionality reduction feature engineering

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Analysis of Knee Osteoarthritis Severity: A Deep Learning Paradigm for Automated Assessment from Plain Radiographs 8th

Analysis of Knee Osteoarthritis Severity: A Deep Learning Pa...

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8th International Conference on Information and Communication Technology for Intelligent Systems, ICTIS 2024

作者： Panwar, Punita Chaurasia, Sandeep Gangrade, Jayesh Bilandi, Ashwani Department of Computer Science and Engineering Manipal University Jaipur Rajasthan Jaipur India Department of Artificial Intelligence and Data Science Jaipur Engineering College and Research Center Rajasthan Jaipur India Department of Orthopedic Mahatma Gandhi Medical College Rajasthan Jaipur India

ISBN: (纸本)9789819766741

Knee osteoarthritis (OA) is a chronic disease, also known as progressive joint disease which occurs due to continuous loss of cartilage. It generally affects the older person with age above 45 years. It is also known as "wear and tear" joint disease because it mostly affects the joints of humans like knee, hand, hips and spine. Osteoarthritis symptoms frequently grow over the time and result in worsening of articular cartilage. Pain, stiffness, tenderness, swelling, and bone spur are most common signs of osteoarthritis. Doctors normally use radiographic X-ray images for identification of OA. In medical field this is a highly time-consuming process;hence, to automate this process and rapidly diagnose, deep learning algorithms such as convolutional neural networks (CNN), are employed for the identification of knee osteoarthritis (OA) severity in X-ray scans, using the Kellgren-Lawrence (KL) grading scheme. The dataset utilized for this research was sourced from Kaggle, consisting of knee X-ray images. The dataset was partitioned into a training set including 5778 images, a testing set including 1661 images, and a validation dataset containing 826 images. The proposed study encompasses the quantification of grade-specific severity in knee osteoarthritis. The measured rates for each severity grade are G0 = 91%, G1 = 1%, G2 = 49.77%, G3 = 67.85%, and G4 = 88.46%. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Cartilage

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A Bioinformatics Analysis and Homology 3D Modeling of Dihydroxy-orotate-dehydrogenase of Plasmodium ovale

A Bioinformatics Analysis and Homology 3D Modeling of Dihydr...

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2023 International Conference on Informatics, Multimedia, Cyber and Information Systems, ICIMCIS 2023

作者： Nirwantono, Rudi Assidqi, Khoirunnisa Hidayat, Alam Ahmad Pebrianto Trinugroho, Joko Pardamean, Bens Bina Nusantara University Faculty of Engineering Bioinformatics and Data Science Research Center Biotechnology Department Jakarta11480 Indonesia Bina Nusantara University Faculty of Engineering Food Biotechnology Research Center Biotechnology Department Jakarta11480 Indonesia Bina Nusantara University School of Computer Science Bioinformatics and Data Science Research Center Mathematics Departement Jakarta11480 Indonesia Bina Nusantara University Bioinformatics and Data Science Research Center Jakarta11480 Indonesia Bina Nusantara University Binus Graduate Program - Computer Science Program Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350307306

The infection of Plasmodium vivax is relatively less virulent than the deathliest Plasmodium falciparum. However, it still can lead to a fatal case and often induces recurring malaria due to dormant parasites in the liver. Thus, the research to study the drug to treat Plasmodium vivax is essential, where the enzyme dihydroorotate dehydrogenase (DHODH) has recently become a new drug target. However, the drug-enzyme interaction study has only recently been conducted in Plasmodium falciparum (pfDHODH) due to the lack of the 3D structure of enzyme DHODH from Plasmodium vivax that is crucial for the study. Therefore, this study aimed to perform the modelling study of Plasmodium vivax DHODH (PvDHODH) to create a 3D structure as a basis for drug-protein interaction study in upcoming studies. Sequence pvDHODH (Accession: SCO68359.1) was used in homology modelling using Modeller 10.2 with the crystal structure of DHODH from DHODH (Accession: 7KZY) as a template. Overall, the model generated from the homology modelling was considered a good model, and the 3D structure was close to the native state according to several parameters, including DOPE score, GA341 score, QMEAN4 value, 3D-1D score, ERRAT2 score, and ProSA score. Ramachandran plot also revealed that almost all amino acids were distributed in the desirable and allowed area (99.1%), and only 0.9% were in generously allowed regions. Superimposition of the model with the template also indicated that the model has an almost similar structure and amino acids positioning, including in the binding pocket and active site. Therefore, the model can be used for downstream analysis in drug-protein interaction studies. Nevertheless, some improvements can still be performed to upgrade the quality of the model by deleting unaligned residues on the C-terminal and realigning the residues at the doubting site. © 2023 IEEE.

关键词： Amino acids

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