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A Web 3.0 Integrated Blockchain Enabled Access System Augmented by Meta-Heuristic Cognitive Learning Framework for Mitigating Threats in IoT Enabled Consumer Electronic Devices

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IEEE Transactions on Consumer Electronics 2025年

作者： Aruna, Etikala Sahayadhas, Arun Kalpana, Ponugoti Khan, Surbhi B. Quasim, Tabrez Almusharrf, Ahlam VELS Institute of Science Technology and Advanced Studies Department of Computer Science and Engineering Pallavaram Chennai India AVN Institute of Engineering and Technology Department of Computer Science and Engineering Hyderabad501510 India University of Salford School of Science Engineering and Environment SalfordM5 4WT United Kingdom Chandigarh University University Centre for Research and Development Mohali India Chitkara University Centre for Research Impact and Outcome India University of Bisha College of Computing and Information Technology Department of Computer Science and Artificial Intelligence Bisha Saudi Arabia King Khalid Univeersity Saudi Arabia

Consumer Electronic Devices have become an open network model because of the infusion of the Internet of Things (IoT) and other communication technologies such as 5G/6G. Though these devices have provided the high-end sophistication even to common person, but it has proved its darker side by triggering more security breaches and privacy problems. Hence, securing and authenticating these Internet enabled consumer devices has become a probable issue to be solved for safer and secured communication. Therefore, this paper presents a novel fusion of Web 3.0-based Blockchain (WBC) and Deep learning (DL) technique for securing consumer electronic devices in an IoT ecosystem. The proposed framework k(MTD-BCAM) is devised into two components: Multiple-Threat Detection(MTD) and Access Management Mechanism(AMM). In the first component, a DL model is applied for threat detection, whereas WBC is meant for an efficient authentication process. Furthermore, a novel residual fast-gated recurrent neural network is proposed. To reduce the complexity, the komodo Mlipir optimization (KMO) approach is used to tune the hyper-parameters of the network. The comprehensive experimental outcome study of the proposed approach employs NSL-KDD datasets in which the distinct metrics of both DL and Blockchain (BC) are measured and analyzed. Results demonstrated the superior accuracy of the model by achieving 99.78% with less computational time and higher transaction speed. Additionally, the statistical validation and security strength of the model are also analyzed and examined with the varied state-of-art models. © 2025 IEEE. All rights reserved.

关键词： Recurrent neural networks

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Hybrid Convolutional Neural Network with Change Detection on Hyperspectral Imagery 1

Hybrid Convolutional Neural Network with Change Detection on...

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1st International Conference on Industrial Electronics Research and Applications, ICIERA 2021

作者： Bidari, Indira Chickerur, Satyadhyan Thm, Abhishek School of Computer Science and Engineering KLE Technological University Hubballi India Centre for High Performance Computing KLE Technological University Hubballi India

ISBN: (纸本)9781665435420

Change Detection (CD) is one of the major research areas in the field of remote sensing. Hyperspectral Images (HSI's) boosted the change detection technology with their high spectral resolution features. Traditional CD techniques process the low dimensional images not well suited for high dimensional HSI's. Even tiny details can be captured using hyperspectral images, but processing these images is difficult because of their complex high-dimensional data. HSI contains noise and redundancy that affect the spectral features of hyperspectral imagery. We present a hybrid convolution neural network method to address processing complex high dimensional data of hyperspectral images. The proposed network extracts the spectral-spatial information of hyperspectral images by decomposing the change Tensor in three directions. The spectral dimension of the change Tensor is reduced by using I-D Convolution' and then the Tensor is decomposed from two spatial dimensions. A 2-D convolution is applied to extract the spectral and spatial features along different spatial dimensions to improve accuracy. The results on three hyperspectral image datasets illustrate the performance improvement than most state-of-The-Art techniques. © 2021 IEEE.

关键词： Change detection

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A Study of Feature-Based and Pixel-Level Image Fusion Techniques 1

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3rd International Conference on Information Technology, InCITe 2023

作者： Kumar, Vivek Khanduja, Manisha Anandaram, Harishchander Joshi, Kapil Gupta, Ashulekha Diwakar, Manoj Department of Computer Science Uttaranchal Institute of Technology Uttaranchal University Dehradun India Uttaranchal School of Computing Sciences Uttaranchal University Dehradun India Centre for Excellence in Computational Engineering and Networking Amrita Vishwa Vidyapeetham Tamil Nadu Coimbatore India Department of Management Studies Uttarakhand Dehradun India Uttarakhand Dehradun India

ISBN: (数字)9789819959976

ISBN: (纸本)9789819959969

In order to offer a single, more precise representation of the display than any one of the original origin photographs, image fusion attempts to combine the original images of the same scenario. Symbol, feature, pixel and signal levels are only a few of the levels at which image fusion can be applied. The starting point for more image fusion approaches to the range of IF is pixel-level and feature based, and multi-scale decomposition multi-resolution image fusion is a significant area of image processing. In the research, we investigate different image fusion methods for the standard enhancement and performance evaluation of the merge image and get some relevant results. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Discrete wavelet transforms

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Deep treatment-adaptive network for causal inference

Deep treatment-adaptive network for causal inference

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作者： Li, Qian Wang, Zhichao Liu, Shaowu Li, Gang Xu, Guandong School of Electrical Engineering Computing and Mathematical Sciences Curtin University Perth Australia School of Electrical Engineering and Telecommunications University of New South Wales Sydney Australia Data Science and Machine Intelligence Lab School of Computer Science University of Technology Sydney Sydney Australia Centre for Cyber Security Research and Innovation Deakin University GeelongVIC3216 Australia

Causal inference is capable of estimating the treatment effect (i.e., the causal effect of treatment on the outcome) to benefit the decision making in various domains. One fundamental challenge in this research is that the treatment assignment bias in observational data. To increase the validity of observational studies on causal inference, representation-based methods as the state-of-the-art have demonstrated the superior performance of treatment effect estimation. Most representation-based methods assume all observed covariates are pre-treatment (i.e., not affected by the treatment) and learn a balanced representation from these observed covariates for estimating treatment effect. Unfortunately, this assumption is often too strict a requirement in practice, as some covariates are changed by doing an intervention on treatment (i.e., post-treatment). By contrast, the balanced representation learned from unchanged covariates thus biases the treatment effect estimation. In light of this, we propose a deep treatment-adaptive architecture (DTANet) that can address the post-treatment covariates and provide a unbiased treatment effect estimation. Generally speaking, the contributions of this work are threefold. First, our theoretical results guarantee DTANet can identify treatment effect from observations. Second, we introduce a novel regularization of orthogonality projection to ensure that the learned confounding representation is invariant and not being contaminated by the treatment, meanwhile mediate variable representation is informative and discriminative for predicting the outcome. Finally, we build on the optimal transport and learn a treatment-invariant representation for the unobserved confounders to alleviate the confounding bias. © 2022, The Author(s).

关键词： Deep neural networks

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Synthesizing Supervisors with a Minimum Control Base for Discrete-Event Systems

Synthesizing Supervisors with a Minimum Control Base for Dis...

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2022 American Control Conference, ACC 2022

作者： Hugh Moulton, Richard Scott, Stephen H. Rudie, Karen Queen's University Department of Electrical and Computer Engineering Kingston Canada Queen's University Department of Biomedical and Molecular Science The Centre for Neuroscience Studies and the Department of Medicine Kingston Canada Queen's University Department of Electrical and Computer Engineering The School of Computing and the Ingenuity Labs Research Institute Kingston Canada

ISBN: (数字)9781665451963

ISBN: (纸本)9781665451963

Controlling a discrete-event system commonly entails synthesizing a supervisor to ensure that the plant's closed-loop behaviour respects a certain specification. In the traditional approach to this problem, if the desired behaviour is not controllable then the specification's supremal controllable sublanguage is enforced instead. Here we invert the problem and formulate the Minimum Control Base Problem, with the goal of finding the minimum set of controllable events that guarantees controllability for the desired behaviour. We show that the sets of controllable events that maintain controllability for the desired behaviour form a complete lattice with respect to subset inclusion and that there is therefore a minimum capability supervisor for any desired sublanguage of the plant's behaviour. We apply our techniques to the Small Factory problem and discuss further applications including systems design, dynamic discrete-event systems, and biological systems. © 2022 American Automatic Control Council.

关键词： Supervisory personnel

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LiDAR Simulation for Performance Evaluation of UAS Detect and Avoid

LiDAR Simulation for Performance Evaluation of UAS Detect an...

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2021 International Conference on Unmanned Aircraft Systems, ICUAS 2021

作者： Riordan, James Manduhu, Manduhu Black, Julie Dow, Alexander Dooly, Gerard Matalonga, Santiago Almada Research Centre School of Computing Engineering and Physical Science University of the West of Scotland Lanarkshire United Kingdom Centre for Robotics and Intelligent Systems University of Limerick Department of Electronic and Computer Engineering

ISBN: (纸本)9780738131153

The solution to mitigating risks associated with beyond Visual Line of Sight (BVLOS) operations of Unmanned Aerial System (UAS) generally focuses on the use of advanced Unmanned Traffic Management (UTM) systems. However, this solution does not take into account other uncooperative objects in the airspace. A more robust approach is to have UTM integrations coupled with onboard machine vision which is tied to automated collision avoidance systems. Future BVLOS regulations in urban situations may require robust embedded software that is capable of detecting air collision hazards in realtime at near and far ranges as uncooperative small aircraft and other unpredictable small objects with fast-changing and unscheduled trajectories pose significant hazards to UAS. This work presents the concept and initial prototyping of a Digital Twin to evaluate the capability of UAS mounted LiDAR to detect small-object air collision risks. A Digital Twin of the Port of Hamburg is augmented with typical port and harbour aerial hazards such as birds, drones, helicopters, and low flying aircraft. The use case scenarios are created in Maya and Unity, with Optix ray tracing of typical LiDAR imaging configurations used to replicate the cause and effect relationship between different LiDAR specifications and their response to small flying objects. Our results demonstrate the inhomogeneous point clouds generated at different spatial-temporal parts of the LiDAR scanning cycle and field of view. These results confirm the challenges of detecting small uncooperative objects by LiDAR. © 2021 IEEE.

关键词： Hazards

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Retraction Note: Data analysis algorithm for internet of things based on federated learning with optical technology

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Optical and Quantum Electronics 2024年第10期56卷 1-2页

作者： Tiwari, Vibha Ananthakumaran, S. Shree, M. Rajani Thangamani, M. Pushpavalli, M. Patil, Swati Bula Centre for Artificial Intelligence Madhav Institute of Technology and Science Gwalior India School of Computing Science and Engineering VIT Bhopal University Sehore India Department of Computer Science and Engineering BNM Institute of Technology Bengaluru India Department of Computer Science and Engineering Hindusthan Institute of Technology Coimbatore India Department of Electronics and Communication Engineering Bannari Amman Institute of Technology Sathyamangalam India Department of Information Technology Vishwakarma Institute of Information Technology Pune India

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Multikernel:Operating system solution to generalized functional safety

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Security and Safety 2023年第2期2卷 30-43页

作者： Yijing Song Huasheng Dai Jinhu Jiang Weihua Zhang School of Computer Science Fudan UniversityShanghai 200433China State Key Laboratory of Mathematical Engineering and Advanced Computing Wuxi 214125China Institute of Big Data Fudan UniversityShanghai 200433China Parallel Processing Institute Fudan UniversityShanghai 200433China

With the trend of digitalization,intelligence,and networking sweeping the world,functional safety and cyber security are increasingly intertwined and overlapped,evolving into the issue of generalized functional *** system reliability technology and network defense technology cannot provide quantifiable design implementation theories and *** the cornerstone of software systems,operating systems in particular are in need of efficient safety *** DHR architecture is a mature and comprehensive solution,and it is necessary to implement an OS-level DHR architecture,for which the multi-kernel operating system is a good *** multi-kernel operating system takes the kernel as the processing scenario element and constructs redundancy,heterogeneity,and dynamism on the kernel,so it has the generalized robustness of the DHR *** article analyzes the significance and requirements of OS-level DHR architecture,and systematically explains how the multi-kernel operating system responds to the requirements of OS-level DHR architecture by analyzing the technical routes of multi-kernel operating systems and develops an operating system solution idea for the generalized functionally safety.

关键词： Metaverse New infrastructure Security base Highly trusted network Trusted identiry Trusted circulation of data

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G²Face: High-Fidelity Reversible Face Anonymization via Generative and Geometric Priors

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IEEE Transactions on Information Forensics and Security 2024年 19卷 8773-8785页

作者： Haoxin Yang Xuemiao Xu Cheng Xu Huaidong Zhang Jing Qin Yi Wang Pheng-Ann Heng Shengfeng He School of Computer Science and Engineering South China University of Technology Guangzhou China Guangdong Engineering Center for Large Model and GenAI Technology State Key Laboratory of Subtropical Building and Urban Science Guangzhou China Centre for Smart Health The Hong Kong Polytechnic University Hong Kong China School of Future Technology South China University of Technology Guangzhou China School of Computer Science and Network Security Dongguan University of Technology Dongguan China Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong SAR China School of Computing and Information Systems Singapore Management University Victoria St Singapore

Reversible face anonymization, unlike traditional face pixelization, seeks to replace sensitive identity information in facial images with synthesized alternatives, preserving privacy without sacrificing image clarity. Traditional methods, such as encoder-decoder networks, often result in significant loss of facial details due to their limited learning capacity. Additionally, relying on latent manipulation in pre-trained GANs can lead to changes in ID-irrelevant attributes, adversely affecting data utility due to GAN inversion inaccuracies. This paper introduces G2Face, which leverages both generative and geometric priors to enhance identity manipulation, achieving high-quality reversible face anonymization without compromising data utility. We utilize a 3D face model to extract geometric information from the input face, integrating it with a pre-trained GAN-based decoder. This synergy of generative and geometric priors allows the decoder to produce realistic anonymized faces with consistent geometry. Moreover, multi-scale facial features are extracted from the original face and combined with the decoder using our novel identity-aware feature fusion blocks (IFF). This integration enables precise blending of the generated facial patterns with the original ID-irrelevant features, resulting in accurate identity manipulation. Extensive experiments demonstrate that our method outperforms existing state-of-the-art techniques in face anonymization and recovery, while preserving high data utility. Code is available at https://***/Harxis/G2Face .

关键词： Faces Data privacy Information integrity Information filtering Face recognition Generative adversarial networks Feature extraction

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Enhanced Network Experimentation with Machine Learning-Driven Analysis of Beaulieu-Xie Fading and κ-μ Co-Channel Interference Effects

Enhanced Network Experimentation with Machine Learning-Drive...

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Cognitive computing in engineering, Communications, sciences and Biomedical Health Informatics (IC3ECSBHI), International Conference on

作者： Raj Ranjan Anil Kumar Pardeep Singh Deepak Suresh Asudani Deepak Upadhyay Nookala Venu Dept. of Computer Science and Engineering Graphic Era Hill University Dehradun India School of Computing DIT University Dehradun India School of Computer Science and Engineering Galgotias University Noida India Symbiosis Institute of Technology Nagpur Campus Symbiosis International (Deemed University) Pune India Centre for Internet of Things Madhav Institute of Technology & Science Deemed University Gwalior India

ISBN: (数字)9798331518523

ISBN: (纸本)9798331518530

The purpose of this work is to study how to improve large-scale network experiments with machine learning (ML) algorithms, thereby studying the two typical fading models, Beaulieu-Xie model and k-µ co-channel interference. Methods like comparison between the conventional static configuration and ML-based adaptive algorithms were evaluated in terms of traditional performance metrics such as Bit Error Rate (BER), Signal to Noise Ratio (SNR), throughput, and latency. Simulations employing the Beaulieu-Xie model accurately reproduced real-world fading environments in urban, suburban, and rural settings with more fidelity than conventional Rayleigh and Rician models. Our sensitivity analysis of k-µ interference parameters revealed their marked impact on SINR over ranges, thus underlining the importance for careful parameter identification in interference codelength results showed that ML-powered adaptive algorithms always did better than a static configuration in dynamic network situations. These transmission parameters were set in real-time based on online predictions, provided by dynamically adapting algorithms which improved performance network metrics over time. Moreover, sensitivity analysis results pertaining to study parameters further underscored potential improvements in the predictive power and confidence of network simulations via ML models. In summary, the integration of ML for network analysis has great potential in terms of accuracy adaptability and efficiency, so that ML is viewed as a corner stone to drive the design and operation features of wireless networks under complex an evolving environments.

关键词： Fading channels Adaptation models Wireless sensor networks Sensitivity analysis Heuristic algorithms Wireless networks Bit error rate Throughput Real-time systems Signal to noise ratio

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