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2023 IEEE International Conference on Data and Software engineering, ICoDSE 2023

作者： Widodo, Anteng Rochim, Adian Fatchur Warsito, Budi School of Postgraduated Studies Diponegoro University Doctoral Program of Information System Semarang Indonesia Muria Kudus University Faculty of Engineering Department of Information System Kudus Indonesia Diponegoro University Faculty of Engineering Department of Computer Engineering Semarang Indonesia Diponegoro University Faculty of Science and Mathematics Department of Statistics Semarang Indonesia

ISBN: (纸本)9798350381382

Low-Rate Denial of Service (LDoS) attacks, an emerging breed of DoS attacks, present a formidable challenge in terms of their detectability. Within the realm of network security, these attacks cast a substantial shadow over the availability and integrity of services. This study seeks to delve into the intricate mechanics of LDoS attacks and explore the landscape of developed detection methodologies. Employing bibliometric analysis, we employ a multifaceted approach to gather research insights pertaining to LDoS attack detection. This approach encompasses the examination of evolving research trends, scrutiny of widely cited scientific publications within the same domain, identification of prolific researchers shaping the field, and a judicious selection of reputable publishers for dependable information acquisition. Furthermore, our study undertakes a meticulous categorization and classification of previously proposed LDoS detection methods documented in well-established scientific literature. This endeavor not only provides a more profound comprehension of the evolutionary trajectory and thematic thrusts within LDoS attack detection research, but it also facilitates the identification of gaps and directions for future exploration. The culmination of these analytical efforts culminates in a comprehensive framework. This contribution manifests through structured categorization, classification, and a forward-looking research roadmap, which collectively serve as valuable tools for both current and aspiring researchers engaged in LDoS attack detection. In summary, this research not only delves into the complexities of LDoS attacks and their mechanisms but also augments the current understanding of detection capabilities. Through its nuanced contributions, this paper extends a helpful guide to the dynamic realm of LDoS attack detection, thus catalyzing advancements and insights for the future. copy;2023 IEEE. © 2023 IEEE.

关键词： Denial-of-service attack

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DESIGN OF TECHNOLOGY FOR SECURE FILE STORAGE BASED ON HYBRID CRYPTOGRAPHY METHODS: SHORT OVERVIEW 4

DESIGN OF TECHNOLOGY FOR SECURE FILE STORAGE BASED ON HYBRID...

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4th IEEE International Conference on Smart Information systems and Technologies, SIST 2024

作者： Abitova, Gulnara A. Manap, Abu Said Kulniyaziva, Korlan Nikulin, Vladimir Dep. of Electrical Engineering and Computer Science Dep. of Computer Engineering State University of New York Astana IT University BinghamtonNY United States Astana Kazakhstan Astana IT University Astana Kazakhstan Dep.of System analysis and management L.N. Gumilyov Eurasian National University Astana Kazakhstan Dep. of Electrical Engineering and Computer Science Binghamton University BinghamtonNY United States

ISBN: (纸本)9798350374865

In the era of pervasive digital data, ensuring secure file storage has become a paramount concern. This paper explores the significance of hybrid cryptography for design of an advanced tools and techniques for secure file storage. Hybrid cryptography, combining symmetric and asymmetric encryption, offers robust protection against unauthorized access, tampering, and data loss. The article reviews recent cryptography literature, highlighting the importance of secure file storage in today's interconnected world and examining the benefits of hybrid cryptography. The analysis of articles on cryptography reveals emerging trends and challenges. Post-quantum cryptography addresses concerns about quantum threats, while blockchain-based cryptography enhances security in IoT data sharing. Homomorphic encryption enables computations on encrypted data, and cryptographic protocols that protect privacy enable safe multi-party computation. Machine learning's intersection with cryptanalysis introduces efficiency but raises ethical considerations. The paper further discusses advancements and trends in cryptography techniques, including post-quantum cryptography, homomorphic encryption, zero-knowledge proofs, post-quantum key exchange, secure multi-party computation, and post-quantum signature schemes. These developments aim to ensure long-term security against quantum attacks, enable privacy-preserving computations, and enhance the confidentiality, integrity, and authentication of digital communication and data storage. In conclusion, the paper advocates for the adoption of hybrid cryptography in secure file storage systems. Its combination of symmetric and asymmetric encryption, along with its adaptability to evolving security landscapes, positions hybrid cryptography as a formidable approach to data protection. By embracing hybrid cryptography and staying informed about the latest advancements, organizations can navigate the digital age with confidence, ensuring the confidentiality

关键词： Quantum cryptography

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Deep Learning Techniques for Advanced Large-Scale Data Processing 15

Deep Learning Techniques for Advanced Large-Scale Data Proce...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Sunil, M.P. Shekhawat, Jyoti Vanaja, T. Mohammed, Vazeer Ali Thaware, Shailesh J. Singh, Vaishali Karnataka Bangalore India Department of Computer Science & Application Vivekananda Global University Jaipur India Department of Information Technology Prince Shri Venkateshwara Padmavathy Engineering College Chennai127 India Department of Computer & Information System Lewis University Illinois United States Department of Computer Engineering Vishwakarma Institute of Technology Pune India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (纸本)9798350370249

Deep getting to know techniques have become increasingly more popular due to their potential to improve huge-scale records processing performance. Leveraging specialized hardware which includes GPUs, deep getting to know algorithms can attain mind-blowing pace will increase compared to traditional algorithms. further, deep gaining knowledge of fashions can discover complicated non-linear relationships in big datasets, permitting greater accurate overall performance and less fake predictions. Latest advances in deep mastering have enabled it to be implemented to remedy a plethora of information processing duties, from photo recognition to natural language processing. CNNs, RNNs, and GANs are a number of the maximum famous deep gaining knowledge of fashions used in tackling complex data processing obligations. agencies consisting of Google, fb, and Amazon use these sophisticated frameworks to build predictive models that help improve numerous programs. Deep getting to know strategies offer exquisite ability to take massive-scale information processing to the following stage. corporations are increasingly more becoming privy to the emerging opportunities, and actively investing in deep mastering to liberate the untapped strength of their information. ©2024 IEEE.

关键词： Large datasets

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Paintings vs Photos Classification Using Deep Learning 3

Paintings vs Photos Classification Using Deep Learning

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3rd International Conference on Technological Advancements in Computational Sciences, ICTACS 2023

作者： Agarwal, Aryan Ratra, Aryan Vats, Satvik Sharma, Vikrant Singh, Sunny Singh, Karan Graphic Era Hill University Computer Science Engineering Dehradun India Nextgen Techedge Pvt. Ltd. India School of Computer and System Science Jawaharlal Nehru University New Delhi India

ISBN: (纸本)9798350342338

Identifying a picture's content based on its attributes is a fundamental task in computer vision known as image classification. In this article, we provide a study on the use of machine learning algorithms to categorize images of paintings and photographs. We investigate the performance of different models, such as decision trees, convolutional neural networks, and support vector machines, on this problem. We also look into how data preprocessing methods and hyperparameter tuning affect how well the models perform. According to the results of our experiments, CNNs classify artworks and photographs with an accuracy of 98%, which is the highest. © 2023 IEEE.

关键词： computer vision convolutional neural networks decision trees hyperparameter tuning image classification paintings performance support vector machines

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Unveiling Ethical Challenges and Malpractice in Academic Research and Integrity 3

Unveiling Ethical Challenges and Malpractice in Academic Res...

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3rd International Conference on Creative Communication and Innovative Technology, ICCIT 2024

作者： Singh Bist, Ankur Rawat, Bhupesh Rahardja, Untung Hardini, Marviola Kamal, Mustofa Graphic Era Hill University Faculty of Computer Science & Engineering Uttarakhand India Graphic Era Hill University Faculty of Computing Uttarakhand India University of Raharja Faculty of Information Technology Tangerang Indonesia University of Raharja Faculty of Computer System Tangerang Indonesia

ISBN: (纸本)9798350367492

In the pursuit of knowledge and innovation, academic research stands as a cornerstone of intellectual progress. However, the integrity of this process is susceptible to malpractice, raising profound ethical concerns. This research paper delves into the nuanced landscape of malpractice and ethical considerations within the realm of academic research, aiming to shed light on the challenges that undermine the credibility and reliability of scholarly endeavours. The study explores various forms of malpractice, including plagiarism, data fabrication, and authorship disputes, which have the potential to erode the foundations of academic integrity. Through a combination of literature reviews, case studies, and expert interviews, the research seeks to identify patterns and motivations behind such malpractices. The findings reveal that systematic solutions are necessary to address these issues, and propose the integration of advanced technological tools to enhance ethical oversight. This study contributes to the ongoing discourse on ethics in academic research by providing comprehensive insights and recommendations to uphold ethical standards. © 2024 IEEE.

关键词： Confidentiality Ethics Informed Consent Plagiarism Professional Misconduct

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Adaptive Intrusion Detection in Cyber-Physical systems using Reinforcement Learning-Based Autoencoders 2

Adaptive Intrusion Detection in Cyber-Physical Systems using...

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2nd IEEE International Conference on Integrated Intelligence and Communication systems, ICIICS 2024

作者： Rajathi, N. Divya, S. Anitha Elavarasi, S. Saritha, G. Jeya ramya, V. Department of Information Technology Kumaraguru College of Technology Coimbatore India Department of Computer Science and Business System Bannari Amman Institute of Technology Erode India Department of Computer Science and Engineering Sona College of Technology Salem India Department of Electronics and Communication Engineering Sri Sai Ram Institute of Technology Chennai India Department of Electronics and Communication Engineering Panimalar Engineering College Chennai India

ISBN: (纸本)9798331504960

The increasing integration of Cyber-Physical systems (CPS) in critical infrastructure presents unique challenges for ensuring robust cybersecurity. Traditional Intrusion Detection systems (IDS) often struggle with the dynamic, complex nature of CPS environments, making adaptive solutions essential. This paper presents an Adaptive Intrusion Detection system (AIDS) that leverages Reinforcement Learning-Based Autoencoders (RL-AE) to detect and respond to cyber intrusions in CPS environments. The proposed model combines the power of autoencoders for unsupervised anomaly detection with reinforcement learning to continuously adapt and improve detection accuracy. Experimental results on benchmark CPS datasets demonstrate that the RL-AE-based IDS achieves a detection accuracy of 98.3%, a precision rate of 96.8%, and a recall rate of 97.5%, effectively minimizing false positives to 2.4%. The system also demonstrated rapid adaptability, with a convergence time of less than 50 iterations in response to new attack patterns. These results highlight the model's ability to enhance system resilience and promote secure and efficient CPS operations in industrial and critical infrastructure applications. This approach provides a scalable and flexible IDS solution, promoting secure and resilient CPS operations in real-time, high-stakes environments. © 2024 IEEE.

关键词： Network intrusion

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An integrated 3D model based face recognition method using synthesized facial expressions and poses for single image applications

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Applied Nanoscience (Switzerland) 2023年第3期13卷 1991-2001页

作者： Milad, Ali Yurtkan, Kamil Management Information System Department Cyprus International University Nicosia 99040 Turkey Computer Engineering Department Middle East Technical University Northern Cyprus Campus Kalkanli 99738 Turkey

The 3D facial expression analysis and synthesis become one of the traditional technics used in face recognition systems which lead to get results with high performance under the varying poses or expressions of the face. In this paper, we propose a new system to generate facial expressions and poses from a single captured image. Expressions are based on the six prototypes, as well as the synthesis of different types of facial ***, using them in face recognition task where only a single image is available is the main methodology. The 3D model was implemented using the process of connecting set of points as triangle meshes in a 3D space. Data sets include the indices and vertices of the triangles. 20 geometrical feature points on the face were used depict the feature of the neutral expression of the face image. In this paper, the analysis are based on facial expression and poses synthesis both using Local Binary Pattern Histograms (LBPH) and Support Vector Machine (SVM). The synthesis poses and expressions of the model, which are artificial faces have been evaluated by using two different databases including the BU-3DFE and FEI *** artificial faces are used for face recognition task, where only single face image is enough for the subjects. The average recognition rates on the BU-3DFE yielded more performance compared to the FEI database, where the average is 78.57% which is significantly 5% more than the FEI database average The recognition rate on the FEI database was reported as 71.78%. Overall, the proposed method attempts to solve the face recognition problem under the limitation of having only single image available. Artificially synthesized images are then used to build the recognition system. Results show that the system achieves satisfactory performance by using only one face image for each subject. © 2021, King Abdulaziz City for Science and Technology.

关键词： Face modeling Facial expression synthesis Facial synthesis Texture mapping

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Improved Chest X-Ray Image Quality Using Median and Gaussian Filter Methods 4

Improved Chest X-Ray Image Quality Using Median and Gaussian...

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4th International Conference on Informatics, Multimedia, Cyber and Information system, ICIMCIS 2022

作者： Kesuma, Lucky Indra Ermatita, Ermatita Erwin, Erwin Sari, Purwita Purabaya, Rudhy Ho Universitas Sriwijaya Doctoral Program in Engineering Faculty of Engineering Palembang Indonesia Universitas Sjakhyakirti India Universitas Sriwijaya Information System Faculty of Computer Science Palembang Indonesia Universitas Sriwijaya Faculty of Computer Science Departement of Computer System Palembang Indonesia Universitas Sriwijaya Informatic Management Faculty of Computer Science Palembang Indonesia Universitas Pembangunan Nasional Veteran Jakarta Information System Faculty of Computer Science Jakarta Indonesia

ISBN: (纸本)9781665473279

The lungs are one of the organs of the body that are responsible for the human respiratory process and are very susceptible to dangerous diseases. For this reason, early detection and diagnosis of lung organs is needed, one of which is through examination of Chest X-Ray (CXR) images. Examination of the results of CXR images is still done manually by doctors and radiologists, this requires time and high accuracy. To facilitate the examination, it is necessary to image quality enhancement in order to get better image quality results so as to produce an accurate diagnosis. The initial stage in this research is to apply the contour improvement technique to the image using Morphology Opening, and followed by noise reduction using Median Filter and Gaussian filter. The results of the two methods of noise reduction are compared with the results of image quality in order to find out the best method that can be applied. The implementation of image quality enhancement results was measured quantitatively using Peak Signal-to-Noise Ratio (PSNR), Mean Square Error (MSE), and Structural Similarity Index Metrics (SSIM). In the Morphology Opening and Median Filter methods, the values obtained are 39.187, 22.252, and 0.952, respectively. Meanwhile, the Morphology Opening and Gaussian Filter methods obtained values of 38.717, 23.917 and 0.956. Based on these results, it can be concluded that both methods are able to improve image quality well. © 2022 IEEE.

关键词： Image quality

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Dipper Throated Optimization for Detecting Black-Hole Attacks inMANETs

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computers, Materials & Continua 2023年第1期74卷 1905-1921页

作者： Reem Alkanhel El-Sayed M.El-kenawy Abdelaziz A.Abdelhamid Abdelhameed Ibrahim Mostafa Abotaleb Doaa Sami Khafaga Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Department of System Programming South Ural State UniversityChelyabinsk454080Russia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia

In terms of security and privacy,mobile ad-hoc network(MANET)continues to be in demand for additional debate and *** more MANET applications become data-oriented,implementing a secure and reliable data transfer protocol becomes a major concern in the ***,MANET’s lack of infrastructure,unpredictable topology,and restricted resources,as well as the lack of a previously permitted trust relationship among connected nodes,contribute to the attack detection burden.A novel detection approach is presented in this paper to classify passive and active black-hole *** proposed approach is based on the dipper throated optimization(DTO)algorithm,which presents a plausible path out of multiple paths for statistics transmission to boost MANETs’quality of service.A group of selected packet features will then be weighed by the DTO-based multi-layer perceptron(DTO-MLP),and these features are collected from nodes using the Low Energy Adaptive Clustering Hierarchical(LEACH)clustering *** is a powerful classifier and the DTO weight optimization method has a significant impact on improving the classification process by strengthening the weights of key features while suppressing the weights ofminor *** hybridmethod is primarily designed to combat active black-hole *** the LEACH clustering phase,however,can also detect passive black-hole *** effect of mobility variation on detection error and routing overhead is explored and evaluated using the suggested *** diverse mobility situations,the results demonstrate up to 97%detection accuracy and faster execution ***,the suggested approach uses an adjustable threshold value to make a correct conclusion regarding whether a node is malicious or benign.

关键词： Black-hole attack mobile ad-hoc network optimization dipper throated optimization

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Time Delay Implementation in Sensorless Control for Ultra-High-Speed Air Compressor Motor of Fuel-Cell systems 10

Time Delay Implementation in Sensorless Control for Ultra-Hi...

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10th IEEE International Power Electronics and Motion Control Conference, IPEMC 2024 ECCE Asia

作者： Kang, Sung-Ho Lee, Hyun-Jun Woo, Tae-Gyeom You, Chang-Seok Lim, Sang-Hak Yoon, Young-Doo Hanyang University Department of Automotive Engineering Automotive-Computer Convergence Korea Republic of Hanyang University Department of Automotive Engineering Korea Republic of Hyundai Motor Company Fuel Cell System Control Development Team1 Korea Republic of

ISBN: (纸本)9798350351330

This paper discusses the implementation of time delays in ultra-high-speed applications. Sensorless controls are used to estimate the rotor speed and angle. In model-based sensorless control, the voltage and current signals are used for angle estimation as an input of the observer. And there is a time delay when calculating these signals. Especially in the high-speed operating range, these time delays degrade in sensorless control performance. This paper proposes a method to compensate for these time delays, improving accurate angle estimation. The proposed time delay implementation method has been validated through simulations and experiments using a surface-mounted permanent magnet(SPM) motor for high-speed air compressor of fuel-cell systems. © 2024 IEEE.

关键词： Time delay

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