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MI3SE: A Multi-User Index-Based Searchable Symmetric Encryption Scheme for Improving Security of Data in Connected Electronic Devices

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IEEE Transactions on Consumer Electronics 2024年第1期71卷 527-537页

作者： Soleymani, Seyed Ahmad Goudarzi, Shidrokh Anisi, Mohammad Hossein Xiao, Pei Mihaylova, Lyudmila Wang, Wenwu University of Sheffield Department of Automatic Control and Systems Engineering Sheffield United Kingdom University of West London School of Computing and Engineering LondonW5 5RF United Kingdom University of Essex School of Computer Science and Electronic Engineering ColchesterCO4 3SQ United Kingdom GuildfordGU2 7XH United Kingdom GuildfordGU2 7XH United Kingdom

In the Industrial Internet of Things (IIoT), outdoor electronic devices serve crucial roles across sectors, providing vital data for decision-making. However, their exposure to open outdoor environments makes them vulnerable to unauthorized access, physical theft, or compromise, endangering both the device and its data. Ensuring the security of outdoor devices and their data is thus critical. This study addresses data security in outdoor IIoT devices by supporting the encryption of all IIoT-related data in device memory. Accessing and retrieving this data requires operations on encrypted data. Hence, we introduce a Searchable Symmetric Encryption (SSE) scheme called MI3SE, which ensures each device's encryption key is unique and valid for a period based on the device's security sensitivity. Moreover, MI3SE meets key security requirements, including confidentiality, integrity, forward secrecy, and backward secrecy. It is specifically designed to mitigate physical compromise and query pattern analysis through a two-keyword query approach and withstand various attacks, as validated by rigorous security analysis. Comparative evaluations against benchmark schemes underscore the efficacy of MI3SE in terms of both security and performance. Moreover, comprehensive non-mathematical security analysis and simulation experiments affirm the enhanced accuracy and efficacy of MI3SE in securing sensitive data stored in outdoor IIoT devices. © 1975-2011 IEEE.

关键词： Industrial Internet of Things Security Cryptography Encryption Servers Data privacy Privacy Performance evaluation Logic gates Consumer electronics

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Depression Detection with EEG Based on Mutual Information Regularization 22

Depression Detection with EEG Based on Mutual Information Re...

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

作者： Lin, Haoyu Ma, Tianyuan Zhao, Chen Qi, Jun Zhang, Tingting Kong, Xiangzeng University of New South Wales School of Computer Science and Engineering Sydney Australia Fujian Agriculture and Forestry University School of Future Technology Fuzhou China Jiaotong-Liverpool University Department of Computing Xi'an Suzhou China

ISBN: (纸本)9798331509712

Depression is a kind of mental illness that is harmful to the development of society. Electroencephalography (EEG) is a promising tool in the area of auxiliary diagnosing diseases. In this paper, we develop a mutual information-based least absolute shrinkage and selection operator (MI-LASSO) model to learn representative features from the power spectral density (PSD) ratio extracted from data. Specifically, MI-LASSO adds an adaptive weight based on mutual information to LASSO, which can discriminate weights of different features. Following the feature selection accomplished by MI-LASSO, the feature set is input into the classifier. We design a stacking ensemble classifier composed of support vector machine (SVM), adaptive boosting (AdaBoost), random forest (RF), and K-nearest neighbor (KNN). Compared to independent classifiers, stacking has a stronger ability to recognize depression. The proposed framework is validated on the open datasets: MODMA and the dataset from Hospital Universiti Sains Malaysia (HUSM). The best classification accuracy on MODMA achieved 99.025%. The best classification accuracy on the second dataset achieved 99.06%. The results indicate that our framework outperforms other EEG-based methods in the identification of depression. We conducted several experiments whose results demonstrate our framework can effectively assist in the diagnosis of depression based on EEG. © 2024 IEEE.

关键词： Support vector machines

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Zebra Optimization Algorithm with Convolutional Neural Network for Detecting Subtle Changes in Skin Pigmentation for Melanoma Screening 1

Zebra Optimization Algorithm with Convolutional Neural Netwo...

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1st International Conference on Software, Systems and Information Technology, SSITCON 2024

作者： Sriram, T. Snehachaturya, A. Shnain, Ammar Hameed Ravi, G. Kumar, S. Senthil Kristu Jayanti College Department of Life Sciences Bangalore India Maisammaguda School of Computer Science and Engineering Hyderabad India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq Sona College of Technology Department of Electronics and Communication Engineering Salem India New Prince Shri Bhavani College of Engineering and Technology Department of Electrical and Electronics Engineering Chennai India

ISBN: (纸本)9798350352931

Melanoma is easily detectable by visual examination since it occurs on the skin surface. Traditionally approach inefficiently to capture to diagnosis, it need to facilitate the diagnosis process while still yielding an accurate of diagnosis. This paper, Zebra optimization Algorithm with Convolutional Neural Network (ZOA-CNN) combined to performed in the classification skin pigmentation for melanoma screening achieve better accuracy. The ZOA is used to optimize the CNN technique enhance the classification accuracy. ZOA balances exploration as well as exploitation that supports the CNN avoid local minima and attain best solutions at the time of training. This flexibility enhances the robustness of the model in detecting melanoma. Initially data obtained from the ISIC 2019 dataset and pre-processing involved the Histogram Equalization and Convert RGB images to grayscale images, which is enhance the image quality. Segmentation utilized U-net architecture includes skip connections that allow high resolution feature from the encoder and decoder path. Feature extraction using MobileNet is designed for efficiency to extracted the high level feature and depthwise separable convolutions reduces the amount of parameters while maintaining the performance. The performance of proposed model evaluated the various metrics and it attains the better accuracy of 0.91 compared to GoogleNet. © 2024 IEEE.

关键词： Dermatology

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Enhancing Face Forgery Detection in a Decentralized Landscape: A Federated Learning Approach with ResNet 2

Enhancing Face Forgery Detection in a Decentralized Landscap...

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2nd International Conference on Automation, computing and Renewable Systems, ICACRS 2023

作者： Gautam, Vinay Maheshwari, Himani Tiwari, Raj Gaurang Agarwal, Ambuj Kumar Trivedi, Naresh Kumar Chitkara University Institute of Engineering and Technology Chitkara University Punjab India School of Computing Graphic Era Hill University Uttrakhand Dehradun India Department of Computer Science and Engineering Sharda School of Engineering and Technology Sharda University Greater Noida India

ISBN: (纸本)9798350340235

Recent advancements in technologies could be the reason for fake image and video generation over the internet. This may be the cause of fake identity creation over the internet for forgery. These types of acts may be the reason for security issues in society. The legacy fake forgery method is not that capable of recognizing such forgery as the methods are trained with publicly available centralized datasets and never focus on privacy and security issues and adversely influence the forgery detection. Hence, the objective of this research is to provide decentralized ways to handle these issues effectively. The issue is taken care of with an effective federated learning-based deep learning technique. In the proposal, several deep learning models were used to generate a residual feature map from the available image dataset and later federated learning was used to generate a decentralized infrastructure for collaborative client machines. The complete experiment setup is established with a publicly available dataset and various variable parameters are used such as the number of clients and round of communication. Afterward, deep learning models' performance is compared for forgery detection under federated learning environments, and it has been observed that ResNet deep learning outperforms with an accuracy rate of 87.83% in FaceForensic dataset. © 2023 IEEE.

关键词： Deep Learning Facial Forgery Detection Feature Learning Federated Learning Privacy-Preserving

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Enhancing Skin Disease Classification and Privacy Preservation through Federated Learning-Based Deep Learning 1

Enhancing Skin Disease Classification and Privacy Preservati...

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1st International Conference on Artificial Intelligence for Innovations in Healthcare Industries, ICAIIHI 2023

作者： Tiwari, Raj Gaurang Maheshwari, Himani Gautam, Vinay Agarwal, Ambuj Kumar Trivedi, Naresh Kumar Chitkara University Institute of Engineering and Technology Chitkara University Punjab India School of Computing Graphic Era Hill University Uttrakhand Dehradun India Sharda School of Engineering and Technology Sharda University Department of Computer Science and Engineering Greater Noida India

ISBN: (纸本)9798350330915

Human skin is a very sensitive part and is infected with various types of issues. Skin disease is one of them and 70% to 80% of the population is infected with one of the skin diseases. Expensive clinical screening and dermatological analysis are the only solutions to detect skin disease. Legacy classification methods are not appropriate to tackle the issues related to the color, texture, and privacy of the skin disease dataset. Both deep learning(DL) techniques and federated learning(FL)can be better solutions for classification and privacy issues related to skin datasets. The collaboration of both in the medical field can be a significant contribution. In this paper, federated learning-based deep learning was applied to a publicly available dataset consisting of 10 different skin diseases. Various deep learning models were applied, and privacy concerns were preserved with a federated learning approach. Here, the dataset was enhanced with several image augmentation strategies. In this research, several models were used and after observation, it was found that the InceptionNet outperformed and produced a better accuracy rate of 98.89%. In this research, complete experiments were set with varying client values and communication rounds. © 2023 IEEE.

关键词： classification convolution neural network deep learning federated learning healthcare privacy preservation skin disease

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Internet of Things based Smart Water Leakage Monitoring and Alert System 9

Internet of Things based Smart Water Leakage Monitoring and ...

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9th International Conference on Advanced computing and Communication Systems, ICACCS 2023

作者： Keerthana, S. Elango, S. Judgi, T. Vivek, S.K. Ponmurugan, P. Kumar, Neelam Sanjeev J K K Nataraja College of Engineering and Technology Department of Electrical and Electronics Engineering Komarapalayam638183 India Nandha Engineering College Department of Electrical and Electronics Engineering Tamilnadu Erode India Sathyabama Institute of Science and Technology Department of Computer Science and Engineering India Shree Venkateshwara Hi-Tech Engineering College Department of Electrical and Electronics Engineering Gobichettipalayam638455 India Meenakshi Academy of Higher Education and Research Department of Research Chennai600078 India Saveetha School of Engineering SIMATS Department of Biomedical Engineering Chennai India

ISBN: (纸本)9798350397376

It is critical to manage water supply in a safe and efficient manner. Significant amounts of water are lost each year as a result of leaks in water distribution systems (WDN). As a result, locating leaks in a reliable manner is critical. Water leaks can be detected using monitoring software that analyses the flow of water. Apps that track the user's location can be extremely useful. If this technology is widely adopted, it will aid in the resolution of water management issues such as human error and waste of limited water resources. Water leaks can be located predictably using the Internet of Things (Internet of Things). Many countries struggle to get enough water, but the situation is not hopeless. It is critical to accurately locate water leaks in order to reduce system resistance. © 2023 IEEE.

关键词： Water distribution systems

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Cybernated Watermarking Approach on Image with Cryptographic Encryption 2

Cybernated Watermarking Approach on Image with Cryptographic...

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2nd International Conference on computer, Communication and Control, IC4 2024

作者： Sivasankari, R. Veeramakali, T. Mustafa Nawaz, S.M. Prakash, P. Umadevi, P. Alvin Ancy, A. B.S.Abdur Rahman Crescent Institute of Science and Technology Dept. of Computer Science and Engineering India Srm Institute of Science and Technology School of Computing Dept of Data Science and Business Systems Tamilnadu Kattankulathur India Sri Sai Ram Institute of Technology Dept of Computer Science and Engineering India Sri Sai Ram Engineering College Dept of Electronics and Communication Engineering India Kpr Institute of Engineering and Technology Department of S & H Coimbatore India Anna University Dept of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798350387933

Digital watermarking of interactive media content has become an active experimentation field in recent years. In addition to reviewing some of the methods that have been developed for various media types;a general structure for watermark engrafting and identification is outlined. We highlight a few of these application-oriented differences, including copyright protection, substantiation, tamper identification, and data hiding, as well as the technological and system conditions for different media types such as digital photos, digital files, digital audio, and text. We describe a general structure for digital watermarking-based image copyright protection in this approach. In this paper, we outline the pre-processing and implementation of tide marking technique where Twofish algorithm and double encryption is described. The image processing toolbox is also specified by comparing five different image processing techniques. In addition, the post implementation is carried out with the wavelet toolbox and matlab coder to analyze signal, image properties and generate the efficient code for embedding the text. © 2024 IEEE.

关键词： Cryptography

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Mixed /_Fault Detection for Positive 2-D Systems With Distributed Delays

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IEEE Transactions on Systems, Man, and Cybernetics: Systems 2022年第11期52卷 7090-7100页

作者： Duan, Zhaoxia Fu, Jinna Ahn, Choon Ki Xiang, Zhengrong Ghous, Imran Hohai University College of Energy and Electrical Engineering Nanjing210098 China Ocean College Zhoushan316021 China Korea University School of Electrical Engineering Seoul136-701 Korea Republic of Nanjing University of Science and Technology School of Automation Nanjing210094 China Department of Electrical and Computer Engineering Lahore54000 Pakistan

This work discusses the fault detection problem for two-dimensional (2-D) positive systems (PSs) with distributed delays. First, a systematic method is proposed for the computation of exact values of _1-gain and _- index for delay-free systems, and the dimension expansion technique is used to transfer the computation of _1/ _- index for 2-D delayed PSs to one of the delay-free systems. Second, necessary and sufficient conditions are established such that the 2-D delayed PSs are asymptotically stable with a desired _1/_- performance level (PL) γ/β. Third, based on the above results, some NCSs are established such that the disturbance and the fault impact on the output signal are minimized and maximized, respectively. An algorithm (iterative) is formulated for the solution of the convex optimization problem. Finally, the potency and accuracy of the developed theoretical results are exhibited by an example. © 2013 IEEE.

关键词： Stability criteria

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KNN-Based Collaborative Filtering for Fine-Grained Intelligent Grad-school Recommendation System 19th

KNN-Based Collaborative Filtering for Fine-Grained Intellig...

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19th EAI International Conference on Heterogeneous Networking for Quality, Reliability, Security and Robustness, QShine 2023

作者： Xu, Jinfeng Liu, Jiyi Ma, Zixiao Wang, Yuyang Wang, Wei Ngai, Edith Department of Electrical and Electronic Engineering The University of Hong Kong 999077 Hong Kong School of Software Engineering Sun Yat-Sen University Guangdong Zhuhai528406 China Department of Electrical and Computer Engineering Duke University DurhamNC27708 United States Department of Computer Science Cornell University IthacaNY14853 United States Guangdong-Hong Kong-Macao Joint Laboratory for Emotional Intelligence and Pervasive Computing Artificial Intelligence Research Institute Shenzhen MSU-BITUniversity Guangdong Shenzhen518172 China

ISBN: (纸本)9783031651250

The development of the Internet has led to information overload, and how to filter and sift information is a rigorous requirement in all fields. In response to this challenge, recommendation systems have emerged as a valuable tool, offering personalized content and services by efficiently searching and processing dynamically generated information. For students applying to grad schools, finding relevant information can be time-consuming and unreliable from official websites or forums. In light of these challenges, we present a novel solution in the form of an application recommendation platform. Our proposed platform leverages specific open-source datasets and real-time information from platform users using KNN (K-Nearest Neighbor) and CF (Collaborative Filtering) techniques to provide recommendations based on users’ individual backgrounds, we aim to reduce the complexity inherent in information retrieval while simultaneously enhancing the relevance of the recommendations delivered to users. Specifically, we first collect user behavior data, then we will construct the data model and perform some preprocessing on it. Calculate the user similarity, and find out the K-nearest neighbors and rate based on K-nearest neighbors, finally, the recommendation engine is used to calculate the highest-rated items to be recommended to the users. © ICST Institute for computer sciences, Social Informatics and Telecommunications engineering 2024.

关键词： Collaborative filtering

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Research on Deep Learning Model of Feature Extraction Based on Convolutional Neural Network 2

Research on Deep Learning Model of Feature Extraction Based ...

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2nd IEEE International Conference on Image Processing and computer Applications, ICIPCA 2024

作者： Liu, Houze Li, Iris Liang, Yaxin Sun, Dan Yang, Yining Yang, Haowei New York University Department Computer Science and Engineering New York United States Graduate School of Art & Science New York University New York United States Viterbi School of Engineering University of Southern California Los Angeles United States Washington University Department of Electrical and Systems Engineering St. Louis United States School of Computer Science Carnegie Mellon University Pittsburgh United States University of Houston Cullen College of Engineering Houston United States

ISBN: (纸本)9798350360240

Neural networks with relatively shallow layers and simple structures may have limited ability in accurately identifying pneumonia. In addition, deep neural networks also have a large demand for computing resources, which may cause convolutional neural networks to be unable to be implemented on terminals. Therefore, this paper will carry out the optimal classification of convolutional neural networks. Firstly, according to the characteristics of pneumonia images, AlexNet and InceptionV3 were selected to obtain better image recognition results. Combining the features of medical images, the forward neural network with deeper and more complex structure is learned. Finally, knowledge extraction technology is used to extract the obtained data into the AlexNet model to achieve the purpose of improving computing efficiency and reducing computing costs. The results showed that the prediction accuracy, specificity, and sensitivity of the trained AlexNet model increased by 4.25 percentage points, 7.85 percentage points, and 2.32 percentage points respectively. The graphics processing usage has decreased by 51% compared to the InceptionV3 mode. © 2024 IEEE.

关键词： Convolutional neural networks

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