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6th International Youth Conference on Radio Electronics, Electrical and Power engineering, REEPE 2024

作者： Melhem, Heidi Salloum, Emad Oudeh, Atheer Yousif Anbar, Mohammad Molhem, Mohammed Golubtsov, Ivan University of Tartous Information Technology Engineering Department Tartous Syria University of Tartous Communication Technology Engineering Department Tartous Syria College of Education for Pure Science University of Thi-Qar Computer Science Department Iraq Al-Ayen University Computer Engineering Techniques Department Thi-Qar Iraq Bauman Moscow State Technical University Moscow Russia

ISBN: (纸本)9798350382891

Sensitive industries like healthcare require new paradigms in cybersecurity to protect vital data from ever-changing threats. This study presents a new cybersecurity paradigm that combines low- and high-reactivity honeypots to provide a comprehensive defense framework that overcomes the drawbacks of traditional security solutions. The goal of applying the model in a medical setting is to strengthen the security of extremely private hospital data by focusing on weaknesses related to reply attacks and man-in-the-middle attacks. The development of theory and its practical application are both included in the research approach. In theory, low-reactivity and high-reactivity honeypots are combined to create a flexible and durable security system. In the practical portion, Virtual Box is used to replicate a deployment within a hospital network. Relevant technologies are carefully employed to simulate real-world cyber dangers. While the AVISPA tool enables formal security verification to confirm the model's effectiveness. This study presents a ground-breaking cybersecurity model designed specifically for healthcare settings, bridging the gap between theoretical developments and real-world application. The results have significant implications for protecting private health information from modern cyberattacks and for expanding cybersecurity paradigms. © 2024 IEEE.

关键词： Cybersecurity

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Lip-Audio Modality Fusion for Deep Forgery Video Detection

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computers, Materials & Continua 2025年第2期82卷 3499-3515页

作者： Yong Liu Zhiyu Wang Shouling Ji Daofu Gong Lanxin Cheng Ruosi Cheng College of Cyberspace Security Information Engineering UniversityZhengzhou450001China Research Institute of Intelligent Networks Zhejiang LabHangzhou311121China College of Computer Science and Technology Zhejiang UniversityHangzhou310027China Henan Key Laboratory of Cyberspace Situation Awareness Zhengzhou450001China Key Laboratory of Cyberspace Security Ministry of EducationZhengzhou450001China

In response to the problem of traditional methods ignoring audio modality tampering, this study aims to explore an effective deep forgery video detection technique that improves detection precision and reliability by fusing lip images and audio signals. The main method used is lip-audio matching detection technology based on the Siamese neural network, combined with MFCC (Mel Frequency Cepstrum Coefficient) feature extraction of band-pass filters, an improved dual-branch Siamese network structure, and a two-stream network structure design. Firstly, the video stream is preprocessed to extract lip images, and the audio stream is preprocessed to extract MFCC features. Then, these features are processed separately through the two branches of the Siamese network. Finally, the model is trained and optimized through fully connected layers and loss functions. The experimental results show that the testing accuracy of the model in this study on the LRW (Lip Reading in the Wild) dataset reaches 92.3%;the recall rate is 94.3%;the F1 score is 93.3%, significantly better than the results of CNN (Convolutional Neural Networks) and LSTM (Long Short-Term Memory) models. In the validation of multi-resolution image streams, the highest accuracy of dual-resolution image streams reaches 94%. Band-pass filters can effectively improve the signal-to-noise ratio of deep forgery video detection when processing different types of audio signals. The real-time processing performance of the model is also excellent, and it achieves an average score of up to 5 in user research. These data demonstrate that the method proposed in this study can effectively fuse visual and audio information in deep forgery video detection, accurately identify inconsistencies between video and audio, and thus verify the effectiveness of lip-audio modality fusion technology in improving detection performance.

关键词： Deep forgery video detection lip-audio modality fusion mel frequency cepstrum coefficient siamese neural network band-pass filter

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Blockchain-based access control and interoperability framework for electronic health records (ANCILE)

Blockchain-based access control and interoperability framewo...

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International Conference on Artificial Intelligence, Blockchain, Computing and Security, ICABCS 2023

作者： Senthilkumar, G. Srinivasan, Aravindan Venkatesh, J. Kuchipudi, Ramu Vinoth, K. Ramamoorthy, A. Department of Computer Science and Engineering Panimalar Engineering College Chennai India Department of Computer Science Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram India Department of Computer Science and Engineering Malla Reddy Institute of Engineering and Technology Telangana Hyderabad India Department of Information Technology Chaitanya Bharathi Institute of Technology Telangana Hyderabad India Department of Electrical and Electronics Engineering Vel Tech Rangarajan Dr. Sagunthala R and D Institute of Science and Technology Tamil Nadu Chennai India Department of Mathematics Velammal Engineering College Tamil Nadu Chennai India

ISBN: (纸本)9781032669663

Even if big cities are working globally to build the infrastructure for smart cities and there is a greater emphasis on the security of electronic health records, patient privacy is frequently compromised. Previous attempts to combat this have left patients with largely unavailable data. Currently used record-management systems struggle to strike a balance between data privacy and patient and provider access. Blockchain, a new technology, the ability to share data in a decentralized and transactional manner. To balance the accessibility and privacy of electronic health records, blockchain technology may be applied in the healthcare industry. The blockchain-based architecture we present in this work enables patients, healthcare professionals, and third parties to access medical information in a secure, efficient, and straightforward manner while maintaining the privacy of sensitive patient data. Modern cryptographic techniques are used in our architecture, Ancile, to increase security. The usage of smart contracts developed on the Ethereum blockchain improves access control and data obscuration. This article will look at how Ancile works with the varied needs of patients, providers, and third parties in order to understand how the framework might address reoccurring privacy and security concerns in the healthcare industry. © 2024 The Author(s).

关键词： Smart contract

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VIKAS: A Multimodal Framework to Aid in Effective Disaster Management 13th

VIKAS: A Multimodal Framework to Aid in Effective Disaster M...

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13th International Conference on Applications and Techniques in information Security, ATIS 2022

作者： Prabhu, Gautham Manuru Gupta, Tanay Srujan, Metta Venkata Soumya, A.R. Palorkar, Anshita Chowdhury, Anurag Department of Computer Science and Engineering Manipal Institute of Technology Manipal Academy of Higher Education Karnataka Manipal576104 India Department of Data Science and Computer Applications Manipal Institute of Technology Manipal Academy of Higher Education Karnataka Manipal576104 India Department of Information and Communication Technology Manipal Institute of Technology Manipal Academy of Higher Education Karnataka Manipal576104 India

ISBN: (纸本)9789819922635

In the event of a disaster, social media is often used to draw attention to affected areas and distressed people. The massive population and diversity in Indian languages warrant a novel real-time, big-data solution that can increase situational awareness, reduce special forces’ response time, and expedite decision-making. The proposed solution, VIKAS, streams text, images, videos, and audio from posts on microblogging platforms using keywords. It then uses the Google Translate and Transliteration APIs to handle multilingual and macaronic hybrid text, including Hinglish. An Apache Kafka event pipeline processes the sheer volume of posts asynchronously. Duplicate, uninformative, or bot-posted data (checked using the Botometer machine learning algorithm) is discarded. Scraped data is also verified through Google’s FactCheck Explorer API. Audio and video clips are processed leveraging speech-to-text methods. The solution incorporates a BERT pre-trained model and word embeddings for natural language processing tasks, including sentiment analysis and classification of textual data. Image classification and object identification are implemented using a ResNet deep learning model. This multimodal approach pinpoints locations using nearby landmarks, severity, and type of support needed, ranging from humanitarian aid and rescue relief to infrastructure damage. Easy-to-interpret visualizations on an accessible dashboard consolidate many details that can streamline resource distribution and personnel deployment. VIKAS was presented to the National Disaster Response Force at the national-level finals of the Smart India Hackathon 2022. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Sentiment analysis

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Type II Fuzzy Logic Based Cluster Head Selection for Wireless Sensor Network

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computers, Materials & Continua 2022年第1期70卷 801-816页

作者： J.Jean Justus M.Thirunavukkarasan K.Dhayalini G.Visalaxi Adel Khelifi Mohamed Elhoseny Department of Computer Science&Engineering St.Joseph’s College of EngineeringChennai600119India School of Computing Science and Engineering Galgotias UniversityUttar Pradesh203201India Department of Electrical and Electronics Engineering K.Ramakrishnan College of EngineeringTiruchirappalli621112India Department of Computer Science&Engineering Bharath Institute of Higher Education and ResearchChennai600073India Department of Computer Science and Information Technology Abu Dhabi UniversityAbu Dhabi59911United Arab Emirates College of Computer Information Technology American University in the EmiratesDubaiUnited Arab Emirates Faculty of Computers and Information Mansoura UniversityEgypt

Wireless Sensor Network(WSN)forms an essential part of *** is embedded in the target environment to observe the physical parameters based on the type of *** nodes inWSN are constrained by different features such as memory,bandwidth,energy,and its processing *** WSN,data transmission process consumes the maximum amount of energy than sensing and processing of the ***,diverse clustering and data aggregation techniques are designed to achieve excellent energy efficiency in *** this view,the current research article presents a novel Type II Fuzzy Logic-based Cluster Head selection with Low Complexity Data Aggregation(T2FLCH-LCDA)technique for *** presented model involves a two-stage process such as clustering and data ***,three input parameters such as residual energy,distance to Base Station(BS),and node centrality are used in T2FLCH technique for CH selection and cluster ***,the LCDA technique which follows Dictionary Based Encoding(DBE)process is used to perform the data aggregation at ***,the aggregated data is transmitted to the BS where it achieves energy *** experimental validation of the T2FLCH-LCDAtechnique was executed under three different scenarios based on the position of *** experimental results revealed that the T2FLCH-LCDA technique achieved maximum energy efficiency,lifetime,Compression Ratio(CR),and power saving than the compared methods.

关键词： Clustering data aggregation energy consumption cluster head selection wireless sensor networks

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Large-Scale Parallel Cognitive Diagnostic Test Assembly Using A Dual-Stage Differential Evolution-Based Approach

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第6期5卷 3120-3133页

作者： Cao, Xi Lin, Ying Liu, Dong Duh, Henry Been-Lirn Zhang, Jun La Trobe University Department of Computer Science and Information Technology MelbourneVIC3086 Australia Sun Yat-sen University Department of Psychology Guangzhou510006 China Henan Normal University College of Computer and Information Engineering Xinxiang453007 China Key Laboratory of Artificial Intelligence and Personalized Learning in Education of Henan Province Xinxiang453007 China Guangzhou City University of Technology School of Computer Engineering Guangzhou510800 China The Hong Kong Polytechnic University PolyU-NVIDIA Joint Research Centre Hong Kong Hong Kong Hanyang University Seoul04763 Korea Republic of Nankai University Tianjin300071 China

Parallel testing, which uses different test forms to assess examinees, is a necessary and important technique in both educational and psychometric assessments. A key but challenging problem for successful parallel testing lies in generating a high-quality parallel test set. Most existing parallel test assembly methods were developed for classic test theory and item response theory. In the context of cognitive diagnosis models, which is a new instrument featuring the ability to assess the examinee's status on fine-grained attributes, the investigation of parallel test assembly is limited, particularly for large parallel scale. This study aims to provide an efficient dual-stage solution for the large-scale parallel cognitive diagnostic test (CDT) assembly problem. In the first stage, the assembly of individual CDTs is treated as a multimodal optimization problem and a niching differential evolution algorithm is developed to find an elite set of CDTs with near-optimal diagnostic performance. By re-designing evolutionary operators, the efficient search mechanism in differential evolution is transferred to the binary context and suits the purpose of optimizing item assignment to a CDT. In the second stage, a graph representation is defined to capture the set of elite CDTs and their overlapping relationships. A deterministic algorithm is applied to the graph to find specific nodal maximum cliques and provide two types of parallel test sets that satisfy different examiner preferences. Simulation studies under a variety of conditions and real-data demonstration show that the proposed method outperforms the existing approaches on large-scale instances while remaining competitive on small-scale cases. © 2020 IEEE.

关键词： Optimization

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Efficient Resource Management in IoTs Using Evolutionary and Swarm Intelligence Algorithms 7

Efficient Resource Management in IoTs Using Evolutionary and...

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7th International Symposium on Innovative Approaches in Smart Technologies, ISAS 2023

作者： Gupta, Keshav Dev Cengiz, Korhan Awasthi, Charu Ramini, Siva Akash Chatterjee, Shyamapriya Pavithra, M. Poornima College of Engineering Department of Computer Science Engineering Jaipur India University of Fujairah College of Information Technology United Arab Emirates Istinye University Department of Computer Engineering Istanbul Turkey University of Hradec Kralove Faculty of Informatics and Management Department of Information Technologies Hradec Kralove500 03 Czech Republic JSS Academy of Technical Education Department of Information Technology Noida India Andhra Pradesh Amaravati India Narula Institute of Technology Department of Information Technology West Bengal Kolkata India Jansons Institute of Technology Karumathampatti Department of Computer Science and Engineering Coimbatore641659 India

ISBN: (纸本)9798350383065

The proliferation of the Internet of Things (IoT) has ushered in a transformative era of connected devices, emphasizing the critical need for effective resource management. This study introduces an innovative approach that harnesses Evolutionary and Swarm Intelligence algorithms for IoT Task Scheduling, addressing resource optimization challenges. The approach offers superior resource utilization through advanced optimization techniques, reducing energy consumption and enhancing efficiency. Furthermore, it significantly reduces task scheduling execution time, enabling prompt decision-making in dynamic IoT environments. This results in improved task allocation quality, meeting application-specific requirements, and prioritizing critical tasks. Notably, the approach excels in minimizing completion time, making it particularly advantageous for real-time IoT applications. This research contributes to the advancement of IoT resource management, offering an adaptive, efficient, and intelligent solution with broad applicability. In the evolving landscape of IoT, the findings presented here lay a solid foundation for future research and practical implementations, ultimately promoting more responsive, sustainable, and intelligent IoT ecosystems. © 2023 IEEE.

关键词： Swarm intelligence

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Tackling Non-IID for Federated Learning with Components Alignment 1

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5th International Conference on Machine Learning for Cyber Security, ML4CS 2023

作者： Xue, Baolu Zhang, Jiale Chen, Bing Li, Wenjuan School of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing211106 China School of Information Engineering Yangzhou University Yangzhou225009 China The Education University of Hong Kong 999077 Hong Kong

ISBN: (数字)9789819724581

ISBN: (纸本)9789819724574

Federated Learning (FL) is a privacy-preserving framework used to perform machine learning tasks with distributed data. One of the key challenges is heterogeneous data distributions among clients, which results in client-drift, leading to the oscillatory and low-accuracy global model. Although lots of work has been proposed to mitigate client-drift, we find there are drawbacks associated with the two common methods: feature alignment and classifier tuning. For the former, the great bias in classifiers still holds in local models and degrades global model performance. For the latter, it’s hard to obtain suitable global features to introduce external knowledge to locals. To address the above drawbacks, in this paper, we propose a privacy-preserving and effective method, named FCA, to tackle client-drift issues in Non-IID federated learning via aligning models’ components. Specifically, FCA enhances similarity among the local models’ components, i.e. feature extractors and classifiers, by utilizing the estimated global feature representations. Experimental results demonstrate that FCA achieves better performance with fewer rounds. Compared with vanilla, our method achieves from 0.4% to 7.5% performance improvement on three popular datasets with four different Non-IID scenarios. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Privacy-preserving techniques

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Continuous Integration, Testing Deployment and Delivery in Devops

Continuous Integration, Testing Deployment and Delivery in D...

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2024 International Conference on Knowledge engineering and Communication Systems, ICKECS 2024

作者： Naresh, E. Murthy, S.V.N. Sreenivasa, N. Merikapudi, Seshaiah Rakhi Krishna, C.R. Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Department of Information Technology Manipal India SJC Institute of Technology Department of Cse Chikkaballapur India Nitte Meenakshi Institute of Technology Department of Computer Science and Engineering Bangalore Yelahanka India Bgsit Department of Cse India

ISBN: (纸本)9798350359688

The proposed research article focuses on Continuous Integration, Testing, Delivery and Deployment in terms of DevOps environment. DevOps is a combination of development and operations. In a usual work environment, both development and operations of that company work entirely differently where mutual interaction is said to be minimum. Thus, to overcome that, DevOps was introduced, where both development and operations must work together in the same environment. In this article we will discuss what each of these terms means, how they work, why they are important in today's software development life cycle (SDLC), what tools are used for each of them, etc. We will consider a few scenarios where the above-mentioned topics are better than the traditional or the most widely used SDLC methodologies. © 2024 IEEE.

关键词： Integration testing

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Wind Driven Optimization-Based Medical Image Encryption for Blockchain-Enabled Internet of Things Environment

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computers, Materials & Continua 2022年第11期73卷 3219-3233页

作者： C.S.S.Anupama Raed Alsini N.Supriya E.Laxmi Lydia Seifedine Kadry Sang-Soo Yeo Yongsung Kim Department of Electronics&Instrumentation Engineering V.R.Siddhartha Engineering CollegeVijayawada520007India Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahSaudi Arabia Department of Computer Science and Engineering Malla Reddy Engineering College(A)Hyderabad500100India Department of Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India Department of Applied Data Science Noroff University College4612KristiansandNorway Department of Computer Engineering Mokwon UniversityDaejeon35349Korea Department of Technology Education Chungnam National University 99Daehak-roYuseong-guDaejeon34134Korea

Internet of Things(IoT)and blockchain receive significant interest owing to their applicability in different application areas such as healthcare,finance,transportation,*** image security and privacy become a critical part of the healthcare sector where digital images and related patient details are communicated over the public *** paper presents a new wind driven optimization algorithm based medical image encryption(WDOA-MIE)technique for blockchain enabled IoT *** WDOA-MIE model involves three major processes namely data collection,image encryption,optimal key generation,and data ***,the medical images were captured from the patient using IoT ***,the captured images are encrypted using signcryption *** addition,for improving the performance of the signcryption technique,the optimal key generation procedure was applied by WDOA *** goal of the WDOA-MIE algorithm is to derive a fitness function dependent upon peak signal to noise ratio(PSNR).Upon successful encryption of images,the IoT devices transmit to the closest server for storing it in the blockchain *** performance of the presented method was analyzed utilizing the benchmark medical image *** security and the performance analysis determine that the presented technique offers better security with maximum PSNR of 60.7036 dB.

关键词： Internet of things image security medical images encryption optimal key generation blockchain

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