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Sparse Adversarial Learning for FDIA Attack Sample Generation in Distributed Smart

computer Modeling in engineering & sciences 2024年第5期139卷 2095-2115页

作者： Fengyong Li Weicheng Shen Zhongqin Bi Xiangjing Su College of Computer Science and Technology Shanghai University of Electric PowerShanghai201306China College of Electrical Engineering Shanghai University of Electric PowerShanghai201306China

False data injection attack(FDIA)is an attack that affects the stability of grid cyber-physical system(GCPS)by evading the detecting mechanism of bad *** FDIA detection methods usually employ complex neural networkmodels to detect FDIA ***,they overlook the fact that FDIA attack samples at public-private network edges are extremely sparse,making it difficult for neural network models to obtain sufficient samples to construct a robust detection *** address this problem,this paper designs an efficient sample generative adversarial model of FDIA attack in public-private network edge,which can effectively bypass the detectionmodel to threaten the power grid system.A generative adversarial network(GAN)framework is first constructed by combining residual networks(ResNet)with fully connected networks(FCN).Then,a sparse adversarial learning model is built by integrating the time-aligned data and normal data,which is used to learn the distribution characteristics between normal data and attack data through iterative ***,we introduce a Gaussian hybrid distributionmatrix by aggregating the network structure of attack data characteristics and normal data characteristics,which can connect and calculate FDIA data with normal ***,efficient FDIA attack samples can be sequentially generated through interactive adversarial *** simulation experiments are conducted with IEEE 14-bus and IEEE 118-bus system data,and the results demonstrate that the generated attack samples of the proposed model can present superior performance compared to state-of-the-art models in terms of attack strength,robustness,and covert capability.

关键词： Distributed smart grid FDIA adversarial learning power public-private network edge

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A Color Image Encryption Scheme Based on Singular Values and Chaos

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computer Modeling in engineering & sciences 2023年第10期137卷 965-999页

作者： Adnan Malik Muhammad Ali Faisal S.Alsubaei Nisar Ahmed Harish Kumar Electrical Engineering Department University of Engineering and TechnologyLahore54000Pakistan Department of Cybersecurity College of Computer Science and EngineeringUniversity of JeddahJeddah21959Saudi Arabia Computer Engineering Department University of Engineering and TechnologyLahore54000Pakistan Department of Computer Science College of Computer ScienceKing Khalid UniversityAbha61421Saudi Arabia

The security of digital images transmitted via the Internet or other public media is of the utmost *** encryption is a method of keeping an image secure while it travels across a non-secure communication medium where it could be intercepted by unauthorized *** study provides an approach to color image encryption that could find practical use in various *** proposed method,which combines four chaotic systems,employs singular value decomposition and a chaotic sequence,making it both secure and *** unified average change intensity,the number of pixels’change rate,information entropy analysis,correlation coefficient analysis,compression friendliness,and security against brute force,statistical analysis and differential attacks are all used to evaluate the algorithm’s *** a thorough investigation of the experimental data,it is concluded that the proposed image encryption approach is secure against a wide range of attacks and provides superior compression friendliness when compared to chaos-based alternatives.

关键词： Encryption image encryption chaos theory color image encryption singular value decomposition compression friendliness

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A Multimodal Micro-gesture Classification Model Based on CLIP

A Multimodal Micro-gesture Classification Model Based on CLI...

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2024 IJCAI Workshop and Challenge on Micro-Gesture Analysis for Hidden Emotion Understanding, MiGA 2024

作者： Wang, Yiwen Dong, Zhenyang Li, Pengxia Liu, Yujie College of Computer Science and Technology Qingdao Software College China University of Petroleum Qingdao266580 China

This paper primarily introduces our approach in the 2nd MiGA-IJCAI Challenge Track 1, which focuses on micro-gesture recognition. The micro-gesture dataset has the characteristics of small action amplitude, short duration, and concentrated actions in specific parts. Regarding these issues, We propose a multimodal micro-gesture recognition network based on CLIP. In the video modality, we use a frozen CLIP model as the teacher network and train the student model via distillation. For the skeleton modality, we convert the data into 3D heatmaps, reducing the inherent sparsity of skeleton data. Additionally, we apply text features learned from CLIP to the skeleton modality, enabling interaction between the two models. Our approach achieved an accuracy of 68.9% in micro-gesture recognition. © 2024 Copyright for this paper by its authors.

关键词： Musculoskeletal system

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Enhancing Interest Message Forwarding in NDN-based VANETs using Black Hole Optimization 2

Enhancing Interest Message Forwarding in NDN-based VANETs us...

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2nd IEEE International Conference on Data science and Network Security, ICDSNS 2024

作者： Devi, S. Padma Dhanalakshmi, K. Velammal College of Engineering and Technology Department of Computer Science and Engineering Madurai India Psna College of Engineering and Technology Department of Computer Science and Engineering Dindigul India

ISBN: (纸本)9798350373110

In Intelligent Transportation Systems (ITS) and smart cities, Vehicular Ad hoc Networks (VANETs) are vital but face challenges due to their dynamic topology, making traditional IP-based content retrieval impractical. Named Data Networking (NDN) offers an alternative by using content names for retrieval, though locating content-holding nodes efficiently in NDN-based VANETs remains difficult. This paper introduces an optimized method using Black Hole optimization to enhance interest message forwarding in NDN-based VANETs, aiming to improve packet delivery ratios and reduce latency. Simulation studies comparing proposed method with classic NDN and Particle Swarm Optimization NDN (PSO-NDN) show that proposed approach significantly outperforms these methods, achieving higher packet delivery ratios and lower latency. This research advances efficient communication strategies in NDN-based VANETs, crucial for robust ITS in smart cities, demonstrating the potential of Black Hole optimization to enhance VANET performance and support smart city infrastructure. © 2024 IEEE.

关键词： Vehicular ad hoc networks

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RSCache: A Tail Latency Friendly Cache Based on NVMe SSDs 22

RSCache: A Tail Latency Friendly Cache Based on NVMe SSDs

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22nd IEEE International Symposium on Parallel and Distributed Processing with Applications, ISPA 2024

作者： Lu, Jincheng Cai, Miao Ye, Baoliu Hohai University College of Computer Science and Software Engineering Nanjing China Nanjing University of Aeronautics and Astronautics College of Computer Science and Technology Nanjing China Nanjing University State Key Laboratory for Novel Software Technology Nanjing China

ISBN: (纸本)9798331509712

High fan-out requests are prevalent in systems employing multi-tier architectures. These requests are divided into several sub-requests for parallel processing. However, a high fan-out request must await all sub-requests to be completed before returning, but the processing times of sub-requests are unpredictable due to their differences in characteristics, such as data volume and data popularity. Meanwhile, existing SSD-based caches struggle to adjust request processing speeds to ensure timely handling. As a result, some sub-requests are delayed, affecting the overall latency and causing long tail latency *** paper proposes RSCache, a tail latency-friendly cache based on NVMe SSDs. RSCache combines the NVMe Weighted Round-robin (WRR) arbitration mechanism with a priority-based scheduling mechanism to enable differentiated request processing. We propose a fan-out size-based priority assignment strategy along with a latency-aware sub-request sorting method. They collaborate to prioritize sub-requests according to their impacts on tail latency and schedule them to NVMe priority queues with various processing speeds, effectively reducing the processing time variations among sub-requests. In addition, we balance the load across queues to avoid congestion with a novel feedback mechanism. We implement the prototype of RSCache based on SPDK. Our experiments demonstrate that RSCache reduces tail latency by up to 40% and improves throughput by two times compared to state-of-the-art SSD-based cache designs. © 2024 IEEE.

关键词： Parallel architectures

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An implementation of personalized therapy in Clinical Decision Support System using adaptive transformer and hybrid deep learning network

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Australian Journal of Electrical and Electronics engineering 2025年第1期22卷 105-120页

作者： Praveena Rachel Kamala, S. T, Saraswathi. Kaliraj, V. Nithya, A. Hema, R. Ramyadevi, R. Department of Information Technology Easwari Engineering college Tamil Nadu Chennai India Department of CSE S.A Engineering College Tamil Nadu Chennai India Department of Computer Science and Engineering St.Joseph’s College of Engineering Tamil Nadu Chennai India Department of Electronics and Communication Engineering Easwari Engineering College Tamil Nadu Chennai India Department of Computer Science and Applications SRM Institute of Science and Technology Tamil Nadu Chennai India

This paper suggests a new mechanism from deep learning concept for personalised therapy in Clinical Decision Support Systems (CDSS). Basically, the texts used for the observation are acquired from the standard data sources and then forwarded to the text preprocessing task. In the pre-processing phase, the punctuation and special character removal, stop word removal, and stemming process are applied to remove noise and help to eliminate the redundant information in order to improve the data quality. Further, the pre-processed text is applied to the Adaptive Transformer Net (ATN) for the feature extraction purpose, where the attributes in this task are optimally determined with the aid of the Adaptive Walrus Optimization Algorithm (AWOA). Finally, the resultant text is subjected to the Hybrid Deep Learning Network (HDLNet). The HDLNet model is implemented by integrating the ‘Residual Long Short-Term Memory (Residual LSTM) with Dilated Recurrent Neural Network (Dilated RNN)’. From the results, the sensitivity analysis performed in the implemented technique secured 3.7% more efficient than LSTM, 7.76% improved than MobileNet, 6.7% superior to residual LSTM, and 0.90% effective than dilated RNN in dataset 1. Throughout the validation, the conventional techniques are evaluated with the suggested personalised therapy in CDSS to prove its efficacy. ©, Engineers Australia.

关键词： Long short-term memory

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Deeply‐Recursive Attention Network for video steganography

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CAAI Transactions on Intelligence technology 2023年第4期8卷 1507-1523页

作者： Jiabao Cui Liangli Zheng Yunlong Yu Yining Lin Huajian Ni Xin Xu Zhongfei Zhang College of Computer Science and Technology Zhejiang UniversityHangzhouChina School of Software Technology Zhejiang UniversityNingboChina College of Information Science and Electronic Engineering Zhejiang UniversityHangzhouChina Shanghai SUPREMIND Technology Co. Ltd.ShanghaiChina College of Intelligence Science and Technology National University of Defense TechnologyChangshaChina Department of Computer Science Binghamton UniversityBinghamtonNew YorkUSA

Video steganography plays an important role in secret communication that conceals a secret video in a cover video by perturbing the value of pixels in the cover *** is the first and foremost requirement of any steganographic *** by the fact that human eyes perceive pixel perturbation differently in different video areas,a novel effective and efficient Deeply‐Recursive Attention Network(DRANet)for video steganography to find suitable areas for information hiding via modelling spatio‐temporal attention is *** DRANet mainly contains two important components,a Non‐Local Self‐Attention(NLSA)block and a Non‐Local Co‐Attention(NLCA)***,the NLSA block can select the cover frame areas which are suitable for hiding by computing the correlations among inter‐and intra‐cover *** NLCA block aims to effectively produce the enhanced representations of the secret frames to enhance the robustness of the model and alleviate the influence of different areas in the secret ***,the DRANet reduces the model parameters by performing similar operations on the different frames within an input video *** results show the proposed DRANet achieves better performance with fewer parameters than the state‐of‐the‐art competitors.

关键词： data privacy video processing

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Optimized Visual Recognition Through a Deep Convolutional Neural Network With Hierarchical Modular Organization

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IEEE Access 2024年 12卷 95517-95528页

作者： Clinton Sitepu, Ade Liu, Chuan-Ming National Taipei University of Technology College of Electrical Engineering and Computer Science Taipei10608 Taiwan National Taipei University of Technology Department of Computer Science and Information Engineering Taipei10608 Taiwan

Artificial Intelligence, including machine learning and deep convolutional neural networks (DCNNs), relies on complex algorithms and neural networks to process and analyze data. DCNNs for visual recognition often require access to high-performance hardware, such as GPUs and cloud-based computing resources, to perform tasks efficiently. Visual recognition requires DCNNs with numerous layers. Fully connected layers in DCNNs are often the most computationally intensive. These layers connect every neuron in one layer to every neuron in the next layer, resulting in a large number of parameters to compute. To mitigate redundancy and make DCNNs more efficient, this article implements and demonstrates the concept to identifying and removing redundant or low-impact connections from fully connected layers using convolution neural network with hierarchical modular organization. The modularity of the DCNNs is built based on the cluster hierarchy of the similar image. These clusters are created based on a novel similarity metric, which measures how closely related images are to each other. The architecture uses multiple smaller DCNNs, referred to as modules, designed to progressively classify images into super clusters according to their similarity. Experimental results using popular image datasets show that the proposed DCNNs model to optimized number of operations by 49% to 99% and keeps its performance comparable. © 2024 The Authors.

关键词： Convolution

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A novel end-to-end privacy preserving deep Aquila feed forward networks on healthcare 4.0 environment

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Discover Internet of Things 2025年第1期5卷 1-20页

作者： Kalpana, Ponugoti Tappari, Sunitha Smitha, L. Madhavi, Dasari Naresh, K. Vijayalakshmi, Maddala Department of Computer Science and Engineering AVN Institute of Engineering and Technology Telangana Hyderabad501510 India Department of Electronics and Telematics Engineering G Narayanamma Institute of Technology and Science Telangana Hyderabad India Department of Information Technology G Narayanamma Institute of Technology and Science Telangana Hyderabad India Sridevi Women’s Engineering College Telangana Hyderabad India Department of Computer Science and Engineering TKR College of Engineering & Technology Telangana Hyderabad500097 India

Healthcare 4.0 is considered to be the most resilient technology with the integration of Internet of Things (IoT), Artificial Intelligence (AI), and 5G wireless communication. These Internet-enabled devices compile human data and deliver it to remote clinical centers for efficient diagnosis and treatment processes. Though sensor-driven devices have largely eased everyday lives, these healthcare systems have been suffering from various security breaches and data privacy problems. This evokes a need for designing intelligent systems to eradicate data breaches and privacy problems. This research presents a groundbreaking framework that uniquely combines privacy-preserving optimized deep learning to effectively diagnose cardiac troubles utilizing edge and fog computing devices. The recommended framework is tested with real-time datasets and employs federated learning for training the network. To ensure high prediction performance, Gated Aquila Optimized Deep Feedforward Networks (GAODP) are used to predict heart diseases. In the primary instance, information is acquired by the IoT sensors and stored in fog gateways. Subsequently, the federated mode of GAODP is adopted for the prediction of heart diseases. The ablation tests are conducted utilizing IoT nodes connected to medical sensors, with fog gateways implemented using Embedded Jetson Nano gadgets. Extensive evaluations are carried out using the gathered datasets, and performance metrics are examined and analyzed. The experimentation involves various traditional deep learning frameworks to prove the performance of privacy-preserving models. Moreover, the duration required to construct the model is calculated to confirm the recommended model's intricacy. The outcome indicate that the recommended model has attained notable predictive accuracy, including precision (0.98), recall (0.975), specificity (0.98), F1-score (0.99) and accuracy (0.99) with less computational overhead. © The Author(s) 2025.

关键词： Diagnosis

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ECOSAUR - Empowering Sustainable Choices: A Mobile Application for Carbon Footprint Awareness and Mitigation

ECOSAUR - Empowering Sustainable Choices: A Mobile Applicati...

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2025 International Conference on Artificial Intelligence and Data engineering, AIDE 2025

作者： Arunasakthi, K. Akilandeswari, V. Girisha Malni, N. Arun, S.K. Yamuna, B. Velammal College of Engineering and Technology Computer Science and Engineering Madurai India

ISBN: (纸本)9798331527518

Climate change poses a critical global challenge, requiring urgent action to mitigate its impacts. One of the most effective ways to address this issue is by empowering individuals to make sustainable lifestyle choices. This paper examines the potential of mobile applications in reducing individual carbon footprints by providing users with data-driven insights and actionable strategies. The proposed carbon footprint tracking app consists of four main sections: a home overview page, a carbon footprint calculator, a dashboard for tracking greenhouse gas (GHG) emissions, and a chatbot for interactive assistance. By collecting data on transportation, energy use, dietary choices, and domestic waste, the app calculates individual carbon footprints using established emission factors. This information allows users to make informed decisions to reduce their environmental impact. The paper highlights the significance of providing clear and accurate information to users, helping bridge the gap between awareness and actionable change. This mobile-based solution not only tracks carbon emissions but also motivates users to adopt sustainable practices, fostering a community-driven approach to carbon neutrality and climate action. © 2025 IEEE.

关键词： Global warming

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