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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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ALERT: Atlas-Based Low Estimation Rank Tensor Approach to Detect Autism Spectrum Disorder*

ALERT: Atlas-Based Low Estimation Rank Tensor Approach to De...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Ananya Samanta Monalisa Sarma Debasis Samanta Sponsored Research and Institute Consultancy Indian Institute of Technology Kharagpur India Subir Chowdhury School of Quality and Reliability Indian Institute of Technology Kharagpur India Department of Computer Science & Engineering Indian Institute of Technology Kharagpur India

In response to a stimulus, distinct areas of the human brain are activated. Also, it is known that the regions interact with one another. This functional connectivity is helpful to diagnose any neurological abnormality, such as autism spectrum disorder (ASD). This work proposes an approach to construct a functional connectivity network from fMRI image data. For obtaining a functional connectivity network, the time series component of fMRI data is used and from it correlation matrix is calculated showing the degree of interaction among the brain regions. To map the different regions of a brain, the brain atlas is considered. This essentially yields a low-rank tensor approximation of the functional connectivity matrix. A 2D convolutional deep neural network model is built to categorize topological similarity in the functional connectivity matrices related to ASD and typically developing control. The proposed approach has been tested with ABIDE dataset of fMRI data for autism spectrum disorder. Several brain atlases have been considered in the experiment. With a majority voting concept on the results from the atlases, the proposed technique reveals an ASD detection accuracy of 84.79%, which is significantly comparable to the state of the art *** Relevance— ASD is one of the least understood neurological disorders that has been recently recognized to have major sociological consequences on an affected individual’s life. A symptom-based diagnosis is in practice. However, this requires prolonged behavioural examinations under the supervision of a highly skilled multidisciplinary team. An early and cost-effective detection using an fMRI image is considered an appropriate, comprehensive, and advanced treatment plan.

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TieFake: Title-Text Similarity and Emotion-Aware Fake News Detection

arXiv

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arXiv 2023年

作者： Guo, Quanjiang Kang, Zhao Tian, Ling Chen, Zhouguo School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China 30th Research Institute of China Electronics Technology Group Corporation Chengdu China

Fake news detection aims to detect fake news widely spreading on social media platforms, which can negatively influence the public and the government. Many approaches have been developed to exploit relevant information from news images, text, or videos. However, these methods may suffer from the following limitations: (1) ignore the inherent emotional information of the news, which could be beneficial since it contains the subjective intentions of the authors;(2) pay little attention to the relation (similarity) between the title and textual information in news articles, which often use irrelevant title to attract reader’ attention. To this end, we propose a novel Title-Text similarity and emotion-aware Fake news detection (TieFake) method by jointly modeling the multi-modal context information and the author sentiment in a unified framework. Specifically, we respectively employ BERT and ResNeSt to learn the representations for text and images, and utilize publisher emotion extractor to capture the author’s subjective emotion in the news content. We also propose a scale-dot product attention mechanism to capture the similarity between title features and textual features. Experiments are conducted on two publicly available multi-modal datasets, and the results demonstrate that our proposed method can significantly improve the performance of fake news detection. Our code is available at https://***/UESTC-GQJ/TieFake. Copyright © 2023, The Authors. All rights reserved.

关键词： Sentiment analysis

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A Novel 3D Object Classification Paradigm through Reconfigurable Intelligent Surfaces

A Novel 3D Object Classification Paradigm through Reconfigur...

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IEEE Conference on Standards for Communications and Networking (CSCN)

作者： Stavros Tsimpoukis Dimitrios Tyrovolas George Karagiannidis Sotirios Ioannidis Christos Liaskos Institute of Computer Science – Foundation for Research and Technology Hellas Greece Dept. of Electrical and Computer Engineering Aristotle University of Thessaloniki Greece Dept. of Electrical Engineering Technical University of Crete Greece Dept. of Computer Science and Engineering University of Ioannina Greece

Programmable Wireless Environments (PWEs) leverage Reconfigurable Intelligent Surfaces (RIS) to convert the wireless propagation into a deterministic process. Recently, PWEs have shown promising results in boosting the efficiency of Radio-Frequency (RF) imaging, creating a novel, lightweight object detection and visualization approach for Extended Reality (XR). As a first step towards optimizing the PWE-XR synergy, this work proposes and compares a set of four PWE configuration policies. The goal is to deduce which policy yields the optimal classification of a set of arbitrary 3D objects present within the PWE. The rationale is that the PWE configuration policy that yields optimal object classification, will also yield the best XR quality in subsequent studies. Evaluation results regarding the performance of the proposed policies, based on ray-tracing, are demonstrated and discussed.

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A Comprehensive Survey on Learning Based Methods for Link Prediction Problem

A Comprehensive Survey on Learning Based Methods for Link Pr...

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International Conference on Information Systems and computer Networks (ISCON)

作者： Sachin U. Balvir Mukesh M. Raghuwanshi Kavita R. Singh Dept. of CT YCCE Nagpur India Datta Meghe Institute of Engineering Technology & Research Wardha India Department of Computer Science and Engineering Symboisis Intsitute of Technology Nagpur India Department of Computer Technology Yeshwantrao Chavan College of Engineering Nagpur India

The development of internet technology is continuously increasing day by day. Social network is also the internet based technology, running online to deliver more services and strengthen communities. The availability of large amount of data encouraged the various field of study like data science, data mining and analysis, neural network etc. Currently, in real scenario the two giants Facebook and Google have the tremendous amount of data. The link prediction problem is a key issue with social network analysis. The Link prediction issue is to predicting new link or future link between two nodes in a network. This paper talked about the several method used to address the issue of link prediction based on learning approach mainly it focus on supervised, unsupervised and semi supervised learning based methods.

关键词： Proteins Social networking (online) Supervised learning Neural networks Semisupervised learning Search problems Security

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Emergency Localization for Mobile Ground Users: An Adaptive UAV Trajectory Planning Method

arXiv

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arXiv 2024年

作者： Zhu, Zhihao He, Jiafan Hou, Luyang Xu, Lianming Zhu, Wendi Wang, Li School of Computer Science Beijing University of Posts and Telecommunications Beijing China Science and Technology on Information Systems Engineering Laboratory Nanjing Research Institute of Electronics Engineering Nanjing China School of Electronic Engineering Beijing University of Posts and Telecommunications Beijing China

In emergency search and rescue scenarios, the quick location of trapped people is essential. However, disasters can render the Global Positioning System (GPS) unusable. Unmanned aerial vehicles (UAVs) with localization devices can serve as mobile anchors due to their agility and high line-of-sight (LoS) probability. Nonetheless, the number of available UAVs during the initial stages of disaster relief is limited, and innovative methods are needed to quickly plan UAV trajectories to locate non-uniformly distributed dynamic targets while ensuring localization accuracy. To address this challenge, we design a single UAV localization method without hovering, use the maximum likelihood estimation (MLE) method to estimate the location of mobile users and define the upper bound of the localization error by considering users' movement. Combining this localization method and localization error-index, we utilize the enhanced particle swarm optimization (EPSO) algorithm and edge access strategy to develop a low complexity localization-oriented adaptive trajectory planning algorithm. Simulation results demonstrate that our method outperforms other baseline algorithms, enabling faster localization without compromising localization accuracy. Copyright © 2024, The Authors. All rights reserved.

关键词： Disasters

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Cognitive Congestion Alleviation Framework in IoT-Enabled WSN for Next-Gen Intelligent Transport Systems via Optimized Capsule Attention Network

Cognitive Congestion Alleviation Framework in IoT-Enabled WS...

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Intelligent Data Communication Technologies and Internet of Things (IDCIoT), International Conference on

作者： R. Mohandas Sudha Y Narmatha P P.Venkata Prasad N. Manikandan Department of Electronics & Communication Engineering Chennai Institute of Technology Chennai Tamil Nadu India Department of Computer Science and Engineering Presidency University Bangalore Karnataka India Department of Computer Science and Engineering Sri Sairam College of Engineering Bangalore Karnataka India Department of Electrical and Electronics Engineering Chaitanya Bharathi Institute of Technology Hyderabad Telangana India Department of Business Administration Kalasalingam Academy of Research and Education Kalasalingam Business School Krishnankoil Viruthunagar India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

The rapid advancement of Intelligent Transportation Systems (ITS) has heightened the importance of reliable, real-time data transmission in Wireless Sensor Networks (WSNs). However, congestion in data-heavy Internet of Things (IoT)-enabled networks remains a critical challenge, impacting communication efficiency and decision-making in ITS. In high-density IoT-ITS applications, traditional congestion control methods are insufficient to address the complexity and dynamics of data flows. Existing solutions often fail to adapt in real time to varying traffic loads, leading to delays and data loss. There is a strong need for a congestion alleviation framework capable of intelligently prioritizing data while dynamically managing network resources to ensure seamless information exchange in large-scale deployments. This study presents a Cognitive Congestion Alleviation Framework in IoT-Enabled WSN for Next-Gen Intelligent Transport Systems via Optimized Capsule Attention Network (V-CapMiAN-Parr), which combines the Vectorized Adaptive Capsule Neural Network (V-AdCapNet) and Multi-instance Attention Network (MAN), fine-tuned by the Parrot Optimizer (ParrOpt). This integration aims to effectively detect, mitigate, and prevent congestion through an advanced capsule-based attention mechanism with adaptive optimization. The suggested V-CapMiAN-Parr framework demonstrated significant improvements in congestion control, achieving data throughput efficiencies above 99.7%, packet delivery rates above 99.5%, and network reliability reaching 99.2% under high traffic loads. The model's adaptive weighting mechanism ensures real-time responsiveness and reliability, crucial for next-gen ITS applications. The V-CapMiAN-Parr framework effectively addresses congestion issues in IoT-enabled WSNs, providing a robust, scalable solution for ITS applications. This cognitive framework's advanced data prioritization and adaptive optimization capabilities are well-suited for enhancing the performanc

关键词： Wireless communication Wireless sensor networks Adaptive systems Transportation Telecommunication traffic Real-time systems Internet of Things Data communication Optimization Next generation networking

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A Smart Innovation of Business Intelligence Based Analytical Model by Using POS Based Deep Learning Model

A Smart Innovation of Business Intelligence Based Analytical...

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2023 International Conference on Disruptive Technologies, ICDT 2023

作者： Jagannathan, Sharath Kumar Bizel, Gulhan Thomas Abraham, J.V. Kannan, G. Sankaradass, Veeramalai Navulla, Durgaprasad Saint Peter's University Frank J. Guarini School of Business Jersey CityNJ07306 United States Vellore Institute of Technology School of Computing Science and Engineering Tamil Nadu Chennai India St. Peter's Institute of Higher Education and Research Department of Management Studies Tamil Nadu Chennai India Chennai Institute of Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Koneru Lakshmaiah Education Foundation Kl Business School Andhra Pradesh Guntur India

ISBN: (纸本)9798350323887

In analytics and business intelligence, there are a lot of things that can go wrong. Be it a report or a plan in the data based results must be consistent, verifiable, accurate to emerge and most importantly, acceptable to the end user. Contrary to the popular belief that the coexistence of multiple BI tools poses some major impediment to the advancement of high-quality analytics output, allowing multiple tools at the same time has several serious advantages in many ways. These are all examples of BI software and are more intelligent than relatively sophisticated tools. In this paper innovative business intelligence based analytics model was proposed to enhance the point of sale in commercial retail market. The practice of continuous integration, invented by software developers and borrowed by the analytics and business intelligence community, is an attempt to detect mistakes or errors early. The Point of sale systems occupies every retail space. Any company that has employees has some software that manages payroll, Sales reports are almost universal. © 2023 IEEE.

关键词： Wages

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Lookup Parameter Optimization for Kademlia DHT Alternative in IPFS

Lookup Parameter Optimization for Kademlia DHT Alternative i...

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IEEE International Symposium on Parallel and Distributed Processing Workshops and Phd Forum (IPDPSW)

作者： Hidehiro Kanemitsu Kenji Kanai Hidenori Nakazato School of Computer Science Tokyo University of Technology Tokyo Japan Waseda Research Institute for Science and Engineering Waseda University Tokyo Japan School of Fundamental Science and Engineering Waseda University Tokyo Japan

Peer-to-peer networks, such as IPFS, adopt the distributed hash table (DHT) to efficiently find contents. In particular, Kademlia, which is used in IPFS, requires parameters regarding the lookup concurrency, the number of next hops, and the k-bucket size. However, such values are manually set and then the configuration is not optimal for minimizing the network latency in any network dynamics. In this paper, we present a method for automatically deriving the optimal lookup parameters for KadRTT, which is a modified version of Kademlia to improve the lookup latency. We derive the optimal values for the k-bucket size, lookup concurrency, and the number of next hops using the lookup message arrival rate, initial ID distance, and lookup iteration count. From the experimental comparisons by both a simulation and an emulation, we show that our proposal contributes to the lookup latency, and overlay hop count.

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Structural, electronic and nonlinear optical properties of fluorophenol and nitrophenol molecules in effect of ortho, para and meta position

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UPB Scientific Bulletin, Series B: Chemistry and Materials science 2021年第2期83卷 167-176页

作者： Jayalakshmi, D.S. Raj, Jeeva Vergin Raj, M. Victor Antony Mohankumar, V. Hemanand, D. Department of Physics Sathyabama Institute of Science and Technology Tamil Nadu India Department of Physics Loyola College Tamil Nadu India SSN Research Centre SSN College of Engineering Tamil Nadu India Department of Computer Science Sriram Engineering college Thiruvallur India

In this present work, the theoretical studies on molecular structure of ortho (o), para (p) and meta (m) fluorophenol and nitrophenol, vibration spectra and non linear optical properties has been analysed by using B3LYP/6-31G basic set. The obtained minimum energy shows the stability of the molecule by comparative study. Based on the energy optimisation of the molecule the position of the nitrogen in the phenol ring is optimised at ortho state and fluorophenol is optimised at Meta state. Bond length, bond angle, energy gap, electron affinity, ionization potential, global hardness, softness, electronegativity, chemical potential, global electrophilicity index, vibrational spectra analysis based on FT-IR and FT-Raman frequency spectrum are investigated. © 2021, Politechnica University of Bucharest. All rights reserved.

关键词： Molecules

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