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Next-Generation Consumer Electronics Data Auditing Scheme Toward Cloud-Edge Distributed and Resilient Machine Learning

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IEEE Transactions on Consumer Electronics 2024年第1期70卷 2244-2256页

作者： Li, Yi Shen, Jian Vijayakumar, Pandi Lai, Chin-Feng Sivaraman, Audithan Sharma, Pradip Kumar Nanjing University of Information Science and Technology School of Computer Science Nanjing210044 China Zhejiang Sci-Tech University School of Information Science and Engineering Hangzhou310018 China University College of Engineering Tindivanam Anna University Chennai Department of Computer Science and Engineering Tindivanam604001 India National Cheng Kung University Department of Engineering Science Tainan701 Taiwan Netaji Subhas University of Technology Department of Information Technology New Delhi110078 India University of Aberdeen Department of Computing Science AberdeenAB24 3UE United Kingdom

Distributed and resilient machine learning (DRML) endues next-generation consumer electronics with AI function. Intuitively, AI provides innovative, humanized, convenient applications based on the data extended by next-generation consumer electronics. Cloud-edge computing is an ideal undertaken architecture of DRML due to its distributed property. However, as one of the core elements driving AI applications, data could be lost or corrupted owing to damaged electronics, unstable communication, and even cloud providers' malicious behavior. It is essential to ensure the data integrity of next-generation electronics before AI applications. To this end, we proposed a privacy-protection distributed data auditing scheme for cloud-edge DRML. An efficient data integrity verification method that only uses algebraic operation is constructed. Then, Function Secret Share (FSS) extends the data integrity verification method to protect consumer privacy. Besides, a consensus for data auditing results is designed among the edge servers. Finally, we present an abundance of theoretical analyses and experimental findings to substantiate and validate the efficiency and effectiveness of our proposed scheme. © 1975-2011 IEEE.

关键词： Edge computing

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Combating Misinformation on Social Media Using Social Noise and Social Entropy as a Measure of Uncertainty

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Proceedings of the Association for information science and Technology 2024年第1期61卷 25-35页

作者： Alsaid, Manar Parvathi Panguluri, Siva Hawamdeh, Suliman Department of Information Science Texas A&M University United States Department of Computer Science University of North Texas United States Department of Information Science University of North Texas United States

Recent events around the world, including the war in Ukraine and the conflict in Gaza have highlighted the effective use of social media as a tool to voice concerns about social issues to create awareness. At the same time, social media has become a fertile ground for spreading misinformation, fake news, and conspiracy theories. Misinformation and conspiracy theories have existed since the existence of mankind. What is new today is the speed by which misinformation can be created, magnified and spread using social media. Efforts to regulate social media and control the widespread spread of misinformation are still lacking due to rapid advances in technology and concerns regarding free speech. One approach to minimizing the impact of misinformation is to focus on social noise as an important factor in magnifying and spreading misinformation. In this paper, we investigate methods of identifying and quantifying social noise using social entropy as a measure of uncertainty and topic modeling. Results from the study have shown a direct relationship between social noise and social entropy. The results have also shown that social noise and social entropy decrease with the use of URLs and rich content (sematic information). Further studies will include the use of machine learning and AI techniques to improve the definition of social news and social entropy. 87 Annual Meeting of the Association for information science & Technology | Oct. 25 – 29, 2024 | Calgary, AB, Canada.

关键词： Conspiracy Theory information Ecosystem Misinformation Social Entropy Social Noise Uncertainty

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EPASAD:ellipsoid decision boundary based Process-Aware Stealthy Attack Detector

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Cybersecurity 2024年第3期7卷 32-48页

作者： Vikas Maurya Rachit Agarwal Saurabh Kumar Sandeep Shukla Department of Computer Science and Engineering Indian Institute of Technology KanpurkanpurIndia Merkle Science BangaloreIndia

Due to the importance of Critical Infrastructure(Cl)in a nation's economy,they have been lucrative targets for cyber *** critical infrastructures are usually Cyber-Physical Systems such as power grids,water,and sewage treatment facilities,oil and gas pipelines,*** recent times,these systems have suffered from cyber attacks numer-ous *** have been developing cyber security solutions for Cls to avoid lasting *** to standard frameworks,cyber security based on identification,protection,detection,response,and recovery are at the core of these *** of an ongoing attack that escapes standard protection such as firewall,anti-virus,and host/network intrusion detection has gained importance as such attacks eventually affect the physical dynamics of the ***,anomaly detection in physical dynamics proves an effective means to implement *** is one example of anomaly detection in the sensor/actuator data,representing such systems physical *** present EPASAD,which improves the detection technique used in PASAD to detect these micro-stealthy attacks,as our experiments show that PASAD's spherical boundary-based detection fails to *** method EPASAD overcomes this by using Ellipsoid boundaries,thereby tightening the boundaries in various dimen-sions,whereas a spherical boundary treats all dimensions *** validate EPASAD using the dataset produced by the TE-process simulator and the C-town *** results show that EPASAD improves PASAD's average recall by 5.8%and 9.5%for the two datasets,respectively.

关键词： Intrusion detection system Critical infrastructure security Industrial control system Machine learning

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Automated decryption of siri bhoovalaya using cryptography and natural language processing techniques

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Multimedia Tools and Applications 2025年第12期84卷 10447-10464页

作者： Jagadeesh Sai, D. Department of Information Science & Engineering Ramaiah Institute of Technology Bangalore India

"The Siri Bhoovalaya is a seminal work of literature, believed to have been composed approximately a millennium ago, which encompasses diverse information encrypted using numerals of the Kannada language—a predominant language of southern India. Currently, only a portion of this enigmatic text is accessible, and deciphering its content remains largely a manual endeavor. This article presents a novel model designed to automate the conversion of these Kannada numerals into phonetic alphabets of the designated language. Subsequent to this conversion, algorithms rooted in Natural Language Processing (NLP) techniques are utilized to form coherent words. These algorithms adhere to the linguistic and grammatical structures of the target language. Through this research, we aim to establish an initial technical blueprint to shed light on the profound content encapsulated within this age-old masterpiece." © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Cryptography

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Ensemble Deep Learning Approaches in Health Care:A Review

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computers, Materials & Continua 2025年第3期82卷 3741-3771页

作者： Aziz Alotaibi Department of Computer Science College of Computing and Information TechnologyTaif UniversityTaif21974Saudi Arabia

Deep learning algorithms have been rapidly incorporated into many different applications due to the increase in computational power and the availability of massive amounts of ***,both deep learning and ensemble learning have been used to recognize underlying structures and patterns from high-level features to make predictions/*** the growth in popularity of deep learning and ensemble learning algorithms,they have received significant attention from both scientists and the industrial community due to their superior ability to learn features from big *** deep learning has exhibited significant performance in enhancing learning generalization through the use of multiple deep learning *** ensemble deep learning has large quantities of training parameters,which results in time and space overheads,it performs much better than traditional ensemble *** deep learning has been successfully used in several areas,such as bioinformatics,finance,and health *** this paper,we review and investigate recent ensemble deep learning algorithms and techniques in health care domains,medical imaging,health care data analytics,genomics,diagnosis,disease prevention,and drug *** cover several widely used deep learning algorithms along with their architectures,including deep neural networks(DNNs),convolutional neural networks(CNNs),recurrent neural networks(RNNs),and generative adversarial networks(GANs).Common healthcare tasks,such as medical imaging,electronic health records,and genomics,are also ***,in this review,the challenges inherent in reducing the burden on the healthcare system are discussed and ***,future directions and opportunities for enhancing healthcare model performance are discussed.

关键词： Deep learning ensemble learning deep ensemble learning deep learning approaches for health care health care

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Automated face recognition using deep learning technique and center symmetric multivariant local binary pattern

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Neural Computing and Applications 2025年第1期37卷 263-281页

作者： Sekhar, J.C. Josephson, P. Joel Chinnasamy, A. Maheswari, M. Sankar, S. Kalangi, Ruth Ramya Department of Computer Science and Engineering NRI Institute of Technology Andhra Pradesh Guntur India Department of Electronics and Communication Engineering Malla Reddy Engineering College Telangana Hyderabad India Department of Data Science and Business Systems School of Computing SRMIST Kattankulathur Chennai India Department of Computer Science and Engineering Panimalar Engineering College Chennai India Department of Computer Science and Engineering Saveetha School of Engineering SIMATS Chennai India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vijayawada India

Researchers have recently created several deep learning strategies for various tasks, and facial recognition has made remarkable progress in employing these techniques. Face recognition is a noncontact, nonobligatory, acceptable, and harmonious biometric recognition method with a promising national and social security future. The purpose of this paper is to improve the existing face recognition algorithm, investigate extensive data-driven face recognition methods, and propose a unique automated face recognition methodology based on generative adversarial networks (GANs) and the center symmetric multivariable local binary pattern (CS-MLBP). To begin, this paper employs the center symmetric multivariant local binary pattern (CS-MLBP) algorithm to extract the texture features of the face, addressing the issue that C2DPCA (column-based two-dimensional principle component analysis) does an excellent job of removing the global characteristics of the face but struggles to process the local features of the face under large samples. The extracted texture features are combined with the international features retrieved using C2DPCA to generate a multifeatured face. The proposed method, GAN-CS-MLBP, syndicates the power of GAN with the robustness of CS-MLBP, resulting in an accurate and efficient face recognition system. Deep learning algorithms, mainly neural networks, automatically extract discriminative properties from facial images. The learned features capture low-level information and high-level meanings, permitting the model to distinguish among dissimilar persons more successfully. To assess the proposed technique’s GAN-CS-MLBP performance, extensive experiments are performed on benchmark face recognition datasets such as LFW, YTF, and CASIA-WebFace. Giving to the findings, our method exceeds state-of-the-art facial recognition systems in terms of recognition accuracy and resilience. The proposed automatic face recognition system GAN-CS-MLBP provides a solid basis for a

关键词： Principal component analysis

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A novel medical steganography technique based on Adversarial Neural Cryptography and digital signature using least significant bit replacement

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International Journal of Cognitive Computing in engineering 2024年第1期5卷 379-397页

作者： Hameed, Mohamed Abdel Hassaballah, M. Abdelazim, Riem Sahu, Aditya Kumar Department of Computer Science Faculty of Computers and Information Luxor University Luxor85951 Egypt Department of Computer Science College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University AlKharj16278 Saudi Arabia Department of Computer Science Faculty of Computers and Information South Valley University Qena Egypt Department of Information Systems College of Information Technology Misr University for Science and Technology Giza Egypt Amrita School of Computing Amrita Vishwa Vidyapeetham Andhra Pradesh Amaravati522503 India

With recent advances in technology protecting sensitive healthcare data is challenging. Particularly, one of the most serious issues with medical information security is protecting of medical content, such as the privacy of patients. As medical information becomes more widely available, security measures must be established to protect confidentiality, integrity, and availability. Image steganography was recently proposed as an extra data protection mechanism for medical records. This paper describes a data-hiding approach for DICOM medical pictures. To ensure secrecy, we use Adversarial Neural Cryptography with SHA-256 (ANC-SHA-256) to encrypt and conceal the RGB patient picture within the medical image's Region of Non-Interest (RONI). To ensure anonymity, we use ANC-SHA-256 to encrypt the RGB patient image before embedding. We employ a secure hash method with 256bit (SHA-256) to produce a digital signature from the information linked to the DICOM file to validate the authenticity and integrity of medical pictures. Many tests were conducted to assess visual quality using diverse medical datasets, including MRI, CT, X-ray, and ultrasound cover pictures. The LFW dataset was chosen as a patient hidden picture. The proposed method performs well in visual quality measures including the PSNR average of 67.55, the NCC average of 0.9959, the SSIM average of 0.9887, the UQI average of 0.9859, and the APE average of 3.83. It outperforms the most current techniques in these visual quality measures (PSNR, MSE, and SSIM) across six medical assessment categories. Furthermore, the proposed method offers great visual quality while being resilient to physical adjustments, histogram analysis, and other geometrical threats such as cropping, rotation, and scaling. Finally, it is particularly efficient in telemedicine applications with high achieving security with a ratio of 99% during remote transmission of Electronic Patient Records (EPR) over the Internet, which safeguards the patien

关键词： Steganography

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MAIDS: malicious agent identification-based data security model for cloud environments

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Cluster Computing 2024年第5期27卷 6167-6184页

作者： Gupta, Kishu Saxena, Deepika Gupta, Rishabh Singh, Ashutosh Kumar Department of Computer Science and Engineering National Sun Yat-sen University Kaohsiung80424 Taiwan School of Computer Science and Engineering The University of Aizu Aizuwakamatsu965-0006 Japan Department of Computer Science The University of Economics and Human Sciences Warsaw01-043 Poland Department of Computer Applications SRM Institute of Science & ampTechnology Delhi-NCR Campus Modinagar Uttar Pradesh Ghaziabad201204 India Department of Computer Science and Engineering Indian Institute of Information Technology Bhopal Bhopal462003 India

With the vigorous development of cloud computing, most organizations have shifted their data and applications to the cloud environment for storage, computation, and sharing purposes. During storage and data sharing across the participating entities, a malicious agent may gain access to outsourced data from the cloud environment. A malicious agent is an entity that deliberately breaches the data. This information accessed might be misused or revealed to unauthorized parties. Therefore, data protection and prediction of malicious agents have become a demanding task that needs to be addressed appropriately. To deal with this crucial and challenging issue, this paper presents a Malicious Agent Identification-based Data Security (MAIDS) Model which utilizes XGBoost machine learning classification algorithm for securing data allocation and communication among different participating entities in the cloud system. The proposed model explores and computes intended multiple security parameters associated with online data communication or transactions. Correspondingly, a security-focused knowledge database is produced for developing the XGBoost Classifier-based Malicious Agent Prediction (XC-MAP) unit. Unlike the existing approaches, which only identify malicious agents after data leaks, MAIDS proactively identifies malicious agents by examining their eligibility for respective data access. In this way, the model provides a comprehensive solution to safeguard crucial data from both intentional and non-intentional breaches, by granting data to authorized agents only by evaluating the agent’s behavior and predicting the malicious agent before granting data. The performance of the proposed model is thoroughly evaluated by accomplishing extensive experiments, and the results signify that the MAIDS model predicts the malicious agents with high accuracy, precision, recall, and F1-scores up to 95.55%, 95.30%, 95.50%, and 95.20%, respectively. This enormously enhances the system’s sec

关键词： Forecasting

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Quadratic and Lagrange interpolation-based butterfly optimization algorithm for numerical optimization and engineering design problem

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Soft Computing 2025年第1期29卷 157-194页

作者： Sharma, Sushmita Saha, Apu Kumar Chakraborty, Sanjoy Deb, Suman Sahoo, Saroj Kumar Department of Computer Science and Engineering Indian Institute of Information Technology Tripura Agartala India Department of Computer Science and Engineering National Institute of Technology Tripura Agartala India Department of Mathematics National Institute of Technology Agartala Tripura Agartala India Department of Information Technology Bir Bikram Memorial College Tripura Agartala India

This work proposes a novel and improved Butterfly Optimization Algorithm (BOA), known as LQBOA, to solve BOA’s inherent limitations. The LQBOA uses Lagrange interpolation and simple quadratic interpolation techniques with adapted parameter settings to enhance the search strategy of BOA and to achieve a better balance of diversification and intensification. LQBOA’s performance was examined on 45 traditional benchmark issues as well as the IEEE CEC 2017 benchmark suites with 10, 30 and 50 dimensions, and the results were compared against popular state-of-the-art and modified algorithms. It was discovered that in more than 85% of cases, the proposed LQBOA outperformed the compared algorithms. The Friedman rank test and Wilcoxon rank test were used to validate the suggested LQBOA’s rank and significance. Additionally, convergence and diversity analyses were performed to investigate its convergence speed and searching behavior, respectively. Furthermore, LQBOA has been successfully deployed to solve twelve real-world issues including several engineering design problems and two multiple gravity assist spacecraft trajectory problems. The results of these problems were compared to those of a wide range of algorithms, demonstrating the superior performance of the proposed LQBOA. In conclusion, LQBOA makes a significant addition to the optimization area, and its application could greatly improve the performance of numerous optimization jobs. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Lagrange multipliers

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Enhanced Multi-Scale Object Detection Algorithm for Foggy Traffic Scenarios

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computers, Materials & Continua 2025年第2期82卷 2451-2474页

作者： Honglin Wang Zitong Shi Cheng Zhu School of Artificial Intelligence Nanjing University of Information Science and TechnologyNanjing210044China School of Computer Science Nanjing University of Information Science and TechnologyNanjing210044China School of Electrical&Computer Engineering University of Illinois at Urbana ChampaignUrbanaIL 61801USA

In foggy traffic scenarios, existing object detection algorithms face challenges such as low detection accuracy, poor robustness, occlusion, missed detections, and false detections. To address this issue, a multi-scale object detection algorithm based on an improved YOLOv8 has been proposed. Firstly, a lightweight attention mechanism, Triplet Attention, is introduced to enhance the algorithm’s ability to extract multi-dimensional and multi-scale features, thereby improving the receptive capability of the feature maps. Secondly, the Diverse Branch Block (DBB) is integrated into the CSP Bottleneck with two Convolutions (C2F) module to strengthen the fusion of semantic information across different layers. Thirdly, a new decoupled detection head is proposed by redesigning the original network head based on the Diverse Branch Block module to improve detection accuracy and reduce missed and false detections. Finally, the Minimum Point Distance based Intersection-over-Union (MPDIoU) is used to replace the original YOLOv8 Complete Intersection-over-Union (CIoU) to accelerate the network’s training convergence. Comparative experiments and dehazing pre-processing tests were conducted on the RTTS and VOC-Fog datasets. Compared to the baseline YOLOv8 model, the improved algorithm achieved mean Average Precision (mAP) improvements of 4.6% and 3.8%, respectively. After defogging pre-processing, the mAP increased by 5.3% and 4.4%, respectively. The experimental results demonstrate that the improved algorithm exhibits high practicality and effectiveness in foggy traffic scenarios.

关键词： Deep learning object detection foggy scenes traffic detection YOLOv8

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