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Distributed optimization for IoT attack detection using federated learning and Siberian Tiger optimizer

ICT Express 2025年第3期11卷 542-546页

作者： Gupta, Brij B. Gaurav, Akshat Alhalabi, Wadee Arya, Varsha Alharbi, Eman Chui, Kwok Tai Department of Computer Science and Information Engineering Asia University Taichung 413 Taiwan Department of Medical Research China Medical University Hospital China Medical University Taichung Taiwan Symbiosis Centre for Information Technology (SCIT) Symbiosis International University Pune India School of Cybersecurity Korea University Seoul South Korea Kyung Hee University 26 Kyungheedae-ro Dongdaemun-gu Seoul South Korea Ronin Institute Montclair NJ United States International Center for AI and Cyber Security Research and Innovations (CCRI) Asia University Taichung 413 Taiwan Department of Computer Science Immersive Virtual Reality Research Group King Abdulaziz University Jeddah Saudi Arabia Hong Kong Metropolitan University Hong Kong SAR China Center for Interdisciplinary Research University of Petroleum and Energy Studies (UPES) Dehradun India Department of Computer Science Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia

The rapid growth of IoT devices has heightened the risk of botnet attacks, calling for scalable and distributed detection solutions. In this context, this study proposes a distributed optimization system for IoT attack detection using CNN model utilizing federated learning. After optimizing the hyperparameters of the model at the server, the Siberian Tiger Optimization (STO) method distributes these values to clients for dispersed training. Our model achieves accuracy, recall, and precision of 0.89978, 0.94355, and 0.94455, respectively, using the N-BaIoT dataset. These findings show, in spite of latency issues, the efficiency of federated learning in distributed IoT security systems. © 2025

关键词： CNN model Distributed optimization Federated learning IoT attack detection Siberian Tiger Optimization (STO)

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Maximal coin-walker entanglement in a ballistic quantum walk

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Physical Review A 2022年第4期105卷 042216-042216页

作者： Rong Zhang Ran Yang Jian Guo Chang-Wei Sun Jia-Chen Duan Heng Zhou Zhenda Xie Ping Xu Yan-Xiao Gong Shi-Ning Zhu National Laboratory of Solid State Microstructure School of Physics School of Electronic Science and Engineering and Collaborative Innovation Center of Advanced Microstructures Nanjing University Nanjing 210093 China College of Electronic and Optical Engineering Nanjing University of Posts and Telecommunication Nanjing 210023 China School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu 611731 China Institute for Quantum Information and State Key Laboratory of High Performance Computing College of Computing National University of Defense Technology Changsha 410073 China

We report that the position-inhomogeneous quantum walk (IQW) can be utilized to produce the maximal high-dimensional entanglement while maintaining the quadratic speedup spread of the wave function. Our calculations show that the maximal coin-walker entanglement can be generated in any odd steps or asymptotically in even steps, and the nearly maximal entanglement can be obtained in even steps after 2. We implement the IQW by a stable resource-saving time-bin optical network, in which a polarization Sagnac loop is employed to realize the precisely tunable phase shift. Our approach opens up an efficient way for high-dimensional entanglement engineering as well as promotes investigations on the role of coin-walker interactions in QW-based applications.

关键词： Entanglement manipulation Optical quantum information processing Quantum optics Quantum walks

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Enhancing Decision-Making and Operational Efficiency Through Demand Prediction Using Machine Learning

Enhancing Decision-Making and Operational Efficiency Through...

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International Conference on Emerging Trends in Networks and computer Communications (ETNCC)

作者： Nilesh Bhaskarrao Bahadure Neelam Singh Jagdish Chandra Patni Rutuja Durge Shailaja Pipalatkar Ramdas Khomane Tanupriya Choudhury Saumya Deshmukh Symbiosis Institute of Technology Nagpur Campus Symbiosis International (Deemed University) Pune India Dept. of CSE Graphic Era Deemed to be University Dehradun India Deptt. of CSE Alliance School of Advanced Computing Alliance University Bengaluru India School of Computer Science University of Petroleum and Energy Studies (UPES) Dehradun Uttarakhand India

ISBN: (数字)9798350353266

ISBN: (纸本)9798350353273

Launching a new product in the market and then getting reviews about the product so that the feasibility of the product can be planned accordingly mainly depends on the survey information or data from the sources. Here, decision-making plays a significant contribution, and this is where demand prediction comes into the picture. Demand prediction will not only help in planning the business and optimizing the operations but will also help get more information about the product's performance in the market. This study mainly focuses on how machine learning algorithms and other techniques can be used to predict the demand for the product. The model proposed in this study can enhance decision-making and efficiency and lead to high-standard market performance.

关键词： Surveys Machine learning algorithms Reviews Decision making Production Pricing Predictive models Market research Scheduling Business

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3D Image Reconstruction and Processing for Augmented and Virtual Reality Applications: A computer Generated Environment

3D Image Reconstruction and Processing for Augmented and Vir...

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Communication, Security and Artificial Intelligence (ICCSAI), International Conference on

作者： Radhika Baskar P. Muthu Krishnammal Anurag Aeron Syed Sumera Ali T. Thomas Leonid M R Arun Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India School of Electronics Engineering VIT-AP University Amaravati Andhra Pradesh India Computer Science and Engineering School of Computing DIT University Dehradun Electronics and Computer Engineering Department CSMSS Chh Shahu College of Engineering Aurangabad Maharashtra India ECE dept KCG college of Technology Chennai Department of ECE Vel Tech Rangarajan Dr. Sagunthala R&D Science and Technology Chennai India

New technologies like AR and VR are changing our online and offline experiences. Augmented and virtual reality applications need 3D picture reconstruction and processing. In augmented and virtual reality, this abstract explains 3D image reconstruction and processing concepts, techniques, and effects. Creating engaging AR and VR experiences starts with 3D data. Depth sensors, stereoscopic images, and photogrammetry are essential for accurate 3D reconstructions of real-world objects and settings. These data streams support spatial cognition and interaction in AR/VR. Reconstruction technologies like point cloud and mesh reconstruction are essential for turning 3D data into models. Virtual features that complement or replace the actual environment in AR experiences or build fully fabricated VR settings are based on these models. By applying 2D textures to 3D geometry, “texture mapping” enhances scene realism. Realtime augmented and virtual reality processing requires effective rendering, tracking, and physics simulation algorithms. Rendering engines provide beautiful, responsive experiences, while SLAM lets users explore and interact with virtual information in real life. Occlusion culling and level-of-detail rendering are two optimisation methods needed for smooth performance on high-end VR systems and mobile AR devices. Virtual reality is defined by 3D object interaction and control. Natural and engaging interfaces are created using gesture detection and real-time physics simulations. AR and VR software developers encounter new challenges with each iteration. AR requires accurate spatial mapping, dependable tracking, and simple user interfaces to blend the virtual and real worlds. In contrast, virtual reality (VR) immerses users in realistic computer settings. Portability across operating systems is crucial for greater adoption. Developers must optimise programmes for several hardware platforms to reach the most people.

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The Future of Smart Grids: Revolutionizing Energy Distribution with advanced IoT, and Renewable Integration

The Future of Smart Grids: Revolutionizing Energy Distributi...

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Global Conference for Advancement in technology (GCAT)

作者： S. Avinash J.C. Vinitha S. Sree Dharinya V. Samuthira Pandi Lakshmi Priya J Jaishika S Department of MBA St. Joseph's College of Engineering Chennai Department of Electrical and Electronics Engineering Rajalakshmi Engineering College Chennai Tamil Nadu India School of Computer Science Engineering and Information Systems VIT University Vellore Centre for Advanced Wireless Integrated Technology Chennai Institute of Technology Chennai Tamil Nadu India Department of ECE Saveetha Engineering College Chennai Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai

ISBN: (数字)9798350376685

ISBN: (纸本)9798350376692

Smart grids, which integrate modern Internet of Things (IoT) technology and renewable energy sources to boost efficiency, sustainability, and dependability, are poised to bring about a dramatic shift in the future of energy distribution. Intelligent grids are expected to bring about this shift. Focusing on the role of the Internet of Things (IoT) in enabling real-time monitoring and control, as well as the incorporation of renewable energy to support a low-carbon future, this research investigates the potential for smart grids to change the energy sector. This study gives insights into how smart grids can be efficiently deployed to satisfy the growing energy needs of society by studying the technological breakthroughs, obstacles, and benefits of smart grids. Specifically, the study's focus is on smart grids. The research highlights the essential role that smart grids play in aiding the shift towards a more sustainable and resilient energy infrastructure by conducting an in-depth analysis of case studies, pilot projects, and industry trends all around the world. Providing recommendations for policymakers, industry stakeholders, and utilities to exploit the full potential of smart grids in determining the future of energy distribution, the findings contribute to the continuing discourse on the strategic development and implementation of smart grid technologies. Both of these topics are currently being discussed.

关键词： Industries Renewable energy sources Protocols Real-time systems Smart grids Internet of Things Stakeholders Standards Resilience Investment

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Physical Information Neural Networks for Solving High-index Differential-algebraic Equation Systems Based on Radau Methods

arXiv

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arXiv 2023年

作者： Chen, Jiasheng Tang, Juan Yan, Ming Lai, Shuai Liang, Kun Lu, Jianguang Yang, Wenqiang Institute of Computing Science and Technology Guangzhou University Guangzhou China School of Computer Science and Cyber Engineering Guangzhou University Guangzhou China Singapore Singapore State Key Laboratory of Public Big Data Guizhou University Guiyang China Chongqing Institute of Green and Intelligent Technology Chinese Academy of Sciences Chongqing China

As is well known, differential algebraic equations (DAEs), which are able to describe dynamic changes and underlying constraints, have been widely applied in engineering fields such as fluid dynamics, multi-body dynamics, mechanical systems and control theory. In practical physical modeling within these domains, the systems often generate high-index DAEs. Classical implicit numerical methods typically result in varying order reduction of numerical accuracy when solving high-index systems. Recently, the physics-informed neural network (PINN) has gained attention for solving DAE systems. However, it faces challenges like the inability to directly solve high-index systems, lower predictive accuracy, and weaker generalization capabilities. In this paper, we propose a PINN computational framework, combined Radau IIA numerical method with a neural network structure via the attention mechanisms, to directly solve high-index DAEs. Furthermore, we employ a domain decomposition strategy to enhance solution accuracy. We conduct numerical experiments with two classical high-index systems as illustrative examples, investigating how different orders of the Radau IIA method affect the accuracy of neural network solutions. The experimental results demonstrate that the PINN based on a 5th-order Radau IIA method achieves the highest level of system accuracy. Specifically, the absolute errors for all differential variables remains as low as 10−6, and the absolute errors for algebraic variables is maintained at 10−5, surpassing the results found in existing literature. Therefore, our method exhibits excellent computational accuracy and strong generalization capabilities, providing a feasible approach for the high-precision solution of larger-scale DAEs with higher indices or challenging high-dimensional partial differential algebraic equation systems. Copyright © 2023, The Authors. All rights reserved.

关键词： Numerical methods

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The Space Cooling Capability of Janus Emitter With Different Enclosure Temperature

The Space Cooling Capability of Janus Emitter With Different...

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Pacific Rim Conference on Lasers and Electro-Optics (CLEO/Pacific Rim)

作者： Do Hyeon Kim Gil Ju Lee Se Yeon Heo Il-Suk Kang Young Min Song School of Electrical Engineering and Computer Science Gwangju Institute of Science and Technology Gwangju Republic of Korea Department of Electronics Engineering Pusan National University Busan Republic of Korea National Nanofab Center Korea Advanced Institute of Science and Technology Daejeon Republic of Korea

A Janus emitter (JET) allows releasing heat from enclosures efficiently. However, space-cooling is not demanded at night or winter. This study analyzes space-cooling performance of JET with different temperature gaps ...

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Simulation of the Electrical Control Unit (ECU) in Automated Electric Vehicles for Reliability and Safety Using On-Board Sensors and Internet of Things

Simulation of the Electrical Control Unit (ECU) in Automated...

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International Conference on Electronics, Communication and computing Technologies (ICECCT)

作者： M Sunil Kumar Anuradha T Yerram Srinivas Santhi Sri T Catherine T J Sagaya Aurelia Department of computer science and Engineering School of Computing Mohan Babu University (erstwhile Sree Vidyanikethan Engineering College) Tirupathi AP India Department of Electrical and Electronics Engineering KCG College of Technology Chennai India Department of ECE Vignana Bharathi Institute of Technology Hyderabad India Department of Computer Science and Engineering Koneru lakshmaiah Education Foundation Guntur India Department of EEE R.M.K. College of Engineering and Technology RSM Nagar Puduvoyal India Department of Computer Science CHRIST University Bangalore India

The adaptation of the energy storage system (ESS) with high power and energy density remains a difficulty for electric vehicles (EVs), despite the increasing demand they are experiencing around the world. A lightweight, compact ESS is necessary to deliver the responsive performance and driving range that modern vehicles need. When planning for widespread use of EVs, it's important to give careful attention to the factors of ESS selection, sizing, and administration. One of the most promising future mobility alternatives is the hybrid electric vehicle (HEV), which offers improved fuel economy and lower pollution levels. As a result, one of the most pressing needs is for automakers to develop new technologies for vehicle design that might help lessen emissions and boost economy. The environmental impact of emissions from light-duty cars is growing in tandem with the annual increase in the number of such vehicles on the road. The usage of other modes of transportation, such as ships and planes, is on the rise, but road transportation will always be the most common. Electronic Control Units, or ECUs, have been increasingly commonplace in cars during the past few decades. Vehicle network multicore CPU scheduling is notoriously difficult. This study's findings consist of a straightforward power-sharing control approach for the HESS based on battery and UC, with the goal of extending the battery's useful life in a city environment.

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GenFace: A Large-Scale Fine-Grained Face Forgery Benchmark and Cross Appearance-Edge Learning

arXiv

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arXiv 2024年

作者： Zhang, Yaning Yu, Zitong Wang, Tianyi Huang, Xiaobin Shen, Linlin Gao, Zan Ren, Jianfeng Computer Vision Institute College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China School of Computing and Information Technology Great Bay University Dongguan523000 China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China Nanyang Technological University 50 Nanyang Ave Block N 4 639798 Singapore Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen518129 China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University China Jinan250014 China Key Laboratory of Computer Vision and System Ministry of Education Tianjin University of Technology Tianjin300384 China School of Computer Science University of Nottingham Ningbo China

The rapid advancement of photorealistic generators has reached a critical juncture where the discrepancy between authentic and manipulated images is increasingly indistinguishable. Thus, benchmarking and advancing techniques detecting digital manipulation become an urgent issue. Although there have been a number of publicly available face forgery datasets, the forgery faces are mostly generated using GAN-based synthesis technology, which does not involve the most recent technologies like diffusion. The diversity and quality of images generated by diffusion models have been significantly improved and thus a much more challenging face forgery dataset shall be used to evaluate SOTA forgery detection literature. In this paper, we propose a large-scale, diverse, and fine-grained high-fidelity dataset, namely GenFace, to facilitate the advancement of deepfake detection, which contains a large number of forgery faces generated by advanced generators such as the diffusion-based model and more detailed labels about the manipulation approaches and adopted generators. In addition to evaluating SOTA approaches on our benchmark, we design an innovative Cross Appearance-Edge Learning (CAEL) detector to capture multi-grained appearance and edge global representations, and detect discriminative and general forgery traces. Moreover, we devise an Appearance-Edge Cross-Attention (AECA) module to explore the various integrations across two domains. Extensive experiment results and visualizations show that our detection model outperforms the state of the arts on different settings like cross-generator, cross-forgery, and cross-dataset evaluations. Code and datasets will be available at https://***/Jenine-321/GenFace. Copyright © 2024, The Authors. All rights reserved.

关键词： Image enhancement

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Technical Support for Detection and Prediction of Rainfall 2

Technical Support for Detection and Prediction of Rainfall

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2nd International Conference on Smart Electronics and Communication, ICOSEC 2021

作者： Hemavathi, D. Kumar, V. Ramesh Regin, R. Rajest, S. Suman Phasinam, Khongdet Singh, Saurabh SRM Institute of Science and Technology School of Computing Department of Information Technology Tamil Nadu Physical Education and Sports University Department of Sports Management Chennai India Adhiyamaan College of Engineering Department of Information Technology Tamil Nadu India Technology Advanced Studies Vels Institute of Science Tamil Nadu Chennai India Pibulsongkram Rajabhat University School of Agricultural and Food Engineering Faculty of Food and Agricultural Technology Phitsanulok Thailand Prestige Institute of Management and Research India

ISBN: (纸本)9781665433686

This project evaluates the effectiveness of some DM methods for predicting rainfall using India's historical weather data gathered from the IDM database. Including 10 attributes from the collected meteorological data, only 5 features are generally believed to be significant for precipitation forecasting. The following phases of research are used in the collected data: data cleaning, data selection, Data Transformation and classification. Many pre-processing responsibilities are concerned with determining missing values and eliminating clutter. Hence, two responsibilities are carrying out for finding missing value purpose, which is cleaning and normalization. In this work, six data mining classification methods, including MLR, K-NN, SVM, ANN, Random Forest (RF) and decision tree (DT), were investigated. The classification process divides objects into different classes. A classifier is often trained using a training set, where single or multiple experts assign labels to a set of objects. The proposed RF is a novel classification technique used in rainfall data to classify the region differently from normal rainfall or heavy rainfall. The completed accuracy of the planned RF classifier is 96.1%, which is advanced than other classifiers used in the Rainfall database. © 2021 IEEE.

关键词： Rain

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