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Strengthening KMS Security with Advanced Cryptography, Machine Learning, Deep Learning, and IoT Technologies

SN computer science 2023年第5期4卷 1-13页

作者： Ogala, Justin Onyarin Ahmad, Shahnawaz Shakeel, Iman Ahmad, Javed Mehfuz, Shabana Department of Cyber Security University of Delta Agbor Nigeria Department of Computer Science & Engineering Mewat Engineering College (WAQF) Mewat Haryana Nuh 122107 India Department of Electrical Engineering Jamia Millia Islamia (A Central University) New Delhi 110025 India Department of Computer Science School of Engineering Sciences & Technology (SEST) Jamia Hamdard (Deemed to be University) Hamdard Nagar New Delhi 110062 India

This paper presents an innovative approach to strengthening Key Management Systems (KMS) against the escalating landscape of cyber threats by integrating advanced cryptographic technologies, machine learning, deep learning, and the Internet of Things (IoT). As digital reliance and cyber-attacks surge, strengthening KMS security becomes paramount. Our research provides a comprehensive overview of the state-of-the-art in cloud data security, identifying key vulnerabilities in existing KMS. The paper also outlines a distinctive framework based on the combined application of advanced cryptography, machine learning, deep learning, and IoT, which represents a novel approach in the quest for robust KMS security. Our experimental results substantiate the efficacy of this unique blend of technologies, providing solid empirical evidence that such a fusion can successfully strengthen KMS against potential threats. As technologies and threat landscapes continue to evolve, our framework can serve as a benchmark for future research and practical implementations. It highlights the potential of integrated technological solutions to counter complex cybersecurity issues. Moreover, the approach we've developed can be adapted and expanded to cater to the specific needs of different sectors, such as finance, healthcare, and e-commerce, which are particularly vulnerable to cyber threats. The novelty of our work lies in the amalgamation of the four technologies and the creation of an empirically backed, robust framework, marking a significant stride in KMS security. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Cloud computing Cryptographic technologies DL IoT KMS ML

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Fingerprint Identification of Generative Models Using a MultiFormer Ensemble Approach 25

Fingerprint Identification of Generative Models Using a Mult...

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25th Working Notes of the Conference and Labs of the Evaluation Forum, CLEF 2024

作者： Emon, Md Ismail Siddiqi Hoque, Mahmudul Hasan, Md Rakibul Khalifa, Fahmi Rahman, Md Mahmudur Department of Computer Science SCMNS School Morgan State University BaltimoreMD21251 United States Electrical & Computer Engineering Dept. School of Engineering Morgan State University BaltimoreMD21251 United States

In the ever-changing realm of medical image processing, ImageCLEF brought a new dimension with the Identifying GAN Fingerprint task, catering to the advancement of visual media analysis. This year, the author presented the task of detecting training image fingerprints to control the quality of synthetic images for the second time (as task 1) and introduced the task of detecting generative model fingerprints for the first time (as task 2). Both tasks are aimed at discerning these fingerprints from images, on both real training images and the generative models. The dataset utilized encompassed 3D CT images of lung tuberculosis patients, with the development dataset featuring a mix of real and generated images, and the test dataset. Our team'CSMorgan' contributed several approaches, leveraging multiformer (combined feature extracted using BLIP2 and DINOv2) networks, additive and mode thresholding techniques, and late fusion methodologies, bolstered by morphological operations. In Task 1, our optimal performance was attained through a late fusion-based reranking strategy, achieving an F1 score of 0.51, while the additive average thresholding approach closely followed with a score of 0.504. In Task 2, our multiformer model garnered an impressive Adjusted Rand Index (ARI) score of 0.90, and a fine-tuned variant of the multiformer yielded a score of 0.8137. These outcomes underscore the efficacy of the multiformer-based approach in accurately discerning both real image and generative model fingerprints. © 2024 Copyright for this paper by its authors.

关键词： computerized tomography

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Adversarial Neural Network Classifiers for COVID-19 Diagnosis in Ultrasound Images

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computers, Materials & Continua 2022年第1期70卷 1683-1697页

作者： Mohamed Esmail Karar Marwa Ahmed Shouman Claire Chalopin Department of Industrial Electronics and Control Engineering Faculty of Electronic Engineering(FEE)Menoufia UniversityMenouf32952Egypt Department of Computer Engineering and Networks College of Computing and Information TechnologyShaqra UniversityShaqra11961Saudi Arabia Department of Computer Science and Engineering Faculty of Electronic Engineering(FEE)Menoufia UniversityMenouf32952Egypt Innovation Center for Computer Assisted Surgery(ICCAS) Universit鋞LeipzigLeipzig04103Germany

The novel Coronavirus disease 2019(COVID-19)pandemic has begun in China and is still affecting thousands of patient livesworldwide *** X-ray and Computed Tomography are the gold standardmedical imaging modalities for diagnosing potentially infected COVID-19 cases,applying Ultrasound(US)imaging technique to accomplish this crucial diagnosing task has attracted many physicians *** this article,we propose two modified deep learning classifiers to identify COVID-19 and pneumonia diseases in US images,based on generative adversarial neural networks(GANs).The proposed image classifiers are a semi-supervised GAN and a modifiedGANwith auxiliary *** one includes a modified discriminator to identify the class of the US image using semi-supervised learning technique,keeping its main function of defining the“realness”of tested *** tests have been successfully conducted on public dataset of US images acquired with a convex US *** study demonstrated the feasibility of using chest US images with two GAN classifiers as a new radiological tool for clinical check of COVID-19 *** results of our proposed GAN models showed that high accuracy values above 91.0%were obtained under different sizes of limited training data,outperforming other deep learning-based methods,such as transfer learning models in the recent ***,the clinical implementation of our computer-aided diagnosis of US-COVID-19 is the future work of this study.

关键词： COVID-19 medical imaging machine learning applications adversarial neural networks ultrasound

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Power Quality Challenges in Integrated Distribution Generation and Scope of Intelligent control Techniques 2

Power Quality Challenges in Integrated Distribution Generati...

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2nd IEEE International Conference on Intelligent and Innovative Technologies in Computing, electrical and Electronics, ICIITCEE 2024

作者： Birje, Tushar Karchi, Nalini Kulkarni, D.B. Karchi, Nihal Kle Dr. M S. Sheshgiri College of Engineering & Technology Dept of Electrical & Electronics Belagavi India Kls Gogte Institute of Technology Department of Electrical and Electronics Belagavi India Kls Gogte Institute of Technology Department of Computer Science Engineering Belagavi India

ISBN: (纸本)9798350306415

This paper gives an overview of challenges introduced due to the interconnection of distributed generators into the grid. It also discusses the control technique and scope of AI applications in the mitigation of power quality issues. Exhaustive discussion is carried out on the interconnection of DGs to the grid and power quality issues. It also discusses the different controllers and algorithms that are used to control the parameters of the distributed generation system. A detailed comparison of conventional controllers studied by authors that are used in distribution generation is also discussed. Similarly, the application of artificial intelligence and adaptive algorithm in distribution generation is also presented as per the points, Different connection methods of DG's to the grid, the effect of distributed generation on power quality, the necessity of local voltage control of distribution generator, control methods and intelligent techniques. © 2024 IEEE.

关键词： Adaptive algorithms control techniques Distribution generation FACTS Renewable energy sources

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A Digital controlled of Duty-ratio Feedforward for Four-level Flying Capacitor Boost PFC

A Digital Controlled of Duty-ratio Feedforward for Four-leve...

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11th International Conference on Power Electronics - ECCE Asia, ICPE 2023-ECCE Asia

作者： Chung, Yu-Chen Wang, Hung-Yu Chiu, Huang-Jen Liu, Yu-Chen Lin, Yi-Feng National Taiwan University of Science and Technology Department of Electronic and Computer Engineering Taipei Taiwan National Taipei University of Technology Department of Electrical Engineering Taipei Taiwan National Ilan University Department of Electrical Engineering Ilan Taiwan

ISBN: (纸本)9788957083505

This paper proposes a flying capacitor multilevel (FCML) power factor correction (PFC) with phase-shifted pulse width modulation (PSPWM) technology to achieve voltage balance of the flying capacitors and increase the equivalent frequency of the inductor. Under the rated current ripple, a smaller inductance can be used to reduce the size of the inductor. With an FCML architecture design, the voltage rating of the power devices can be reduced, which decreases the switching loss. Based on PSPWM control, the small-signal model is a detailed analysis, and the compensator is designed considering the phase delay caused by digital control. The duty-ratio feedforward (DFF) control reduces the harmonic distortion of the inductor current. Finally, the 350 W four-level flying capacitor boost PFC with DFF control is implemented with an inductor current frequency of 500 kHz, an input voltage of 90-264 Vac, and an output voltage of 390 V. © 2023 The Korean Institute of Power Electronics.

关键词： Pulse width modulation

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Digital Twin Enabled Open-Circuit Fault Diagnosis for Five-Level ANPC Multilevel Converters

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IEEE Journal of Emerging and Selected Topics in Power Electronics 2024年

作者： Fard, Majid T. Luckett, Benjamin J. He, JiangBiao University of Kentucky Department of Electrical and Computer Engineering KY United States University of Tennessee Department of Electrical Engineering and Computer Science KnoxvilleTN37996 United States

This paper presents an innovative digital twin strategy for health monitoring of five-level active neutral point clamped (5L-ANPC) power converters, which are extensively utilized in high-power, safety-critical applications. Despite their widespread use, the reliability of 5L-ANPC inverters is often compromised due to the large number of semiconductor devices employed, posing challenges for their broader deployment in safety-critical applications. The proposed approach introduces a real-time digital twin replica that continuously monitors crucial parameters to assess the inverter's performance and detect open-circuit faults in the switches. This methodology not only enhances reliability but also reduces maintenance downtime cost. Specifically focusing on open-circuit switching faults, this paper presents a fast online digital twin based fault diagnostic method. The proposed diagnostic technique leverages the existing DC-link capacitor and flying capacitor voltages, along with load current data and switching patterns, eliminating the need for any additional sensors. The detection process takes less than a fundamental period of the inverter output frequency to diagnose any faulty switch accurately. Experimental results validate the robustness and effectiveness of the proposed digital twin health monitoring method across various operating conditions. © 2013 IEEE.

关键词： Fault detection

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Clustering and Energy Efficient Power Allocation Scheme for D2D Multicast Network 2

Clustering and Energy Efficient Power Allocation Scheme for ...

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2nd IEEE International Conference on Contemporary Computing and Communications, InC4 2024

作者： Nallasivan, G. Shajitha Banu, A. Rajesh, V. Arumuga Babu, M. Ananthan, Bhuvanesh Mahesh, P. Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Chennai India Sri Eshwar College of Engineering Department of English Tamil Nadu Coimbatore India Kalasalingam Academy of Research and Education Department Electronics and Communication Engineering Tamil Nadu Krishnankoil India Christ the King Engineering College Department of Electrical and Electronics Engineering Tamil Nadu Coimbatore India PSN College of Engineering and Technology Department of Electrical and Electronics Engineering Tamil Nadu Tirunelveli India

ISBN: (纸本)9798350383652

The multimedia content dissemination services with the current cellular networks still met several challenging while performing desired performance. Recently, the improvement of energy efficiency is not considered in most of the existing works and for all receiving users, they discussed about how to deliver reasonable QoS. Based on multicast network, this work proposed a novel framework to solve resource allocation as well as clustering in device-to-device (D2D). This study introduce a novel adaptive energy valley optimization (AEVO) model to enhance the clustered and power allocation with energy efficient. While considering the requirement of QoS in D2D, an efficient power control mechanism and resource allocation is maximized complete D2D multicast clusters energy-efficiency. By doing this, higher computational difficulties with energy-efficiency problems are solved by proposing novel allocation of power model. Based on the existing methods, the proposed algorithm indicated good numerical results with respect to energy consumption and throughput. © 2024 IEEE.

关键词： D2D multicast network and Adaptive energy valley optimization Energy efficiency Power allocation QoS

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Modelling and Performance Analysis of Visible Light Communication System in Industrial Implementations

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computers, Materials & Continua 2023年第11期77卷 2189-2204页

作者： Mohammed S.M.Gismalla Asrul I.Azmi Mohd R.Salim Farabi Iqbal Mohammad F.L.Abdullah Mosab Hamdan Muzaffar Hamzah Abu Sahmah M.Supa’at Faculty of Electrical Engineering Faculty of EngineeringUniversiti TeknologiMalaysiaSkudai81310Malaysia Lightwave Communications Research Group(LCRG) Faculty of Electrical EngineeringUniversiti TeknologiSkudaiJohorMalaysia Department of Communication Engineering Faculty of Electrical and Electronic EngineeringUniversiti Tun Hussein Onn Malaysia(UTHM)Parit RajaBatu PahatJohor86400Malaysia Faculty of Computing and Informatics Universiti Malaysia SabahKota Kinabalu88400Malaysia Department of Computer Science University of São PauloSão PauloBrazil

Visible light communication(VLC)has a paramount role in industrial implementations,especially for better energy efficiency,high speed-data rates,and low susceptibility to ***,since studies on VLC for industrial implementations are in scarcity,areas concerning illumination optimisation and communication performances demand further *** such,this paper presents a new modelling of light fixture distribution for a warehouse model to provide acceptable illumination and communication *** proposed model was evaluated based on various semi-angles at half power(SAAHP)and different height levels for several parameters,including received power,signal to noise ratio(SNR),and bit error rate(BER).The results revealed improvement in terms of received power and SNR with 30 Mbps data *** modulations were studied to improve the link quality,whereby better average BER values of 5.55×10^(−15) and 1.06×10^(−10) had been achieved with 4 PAM and 8 PPM,*** simulation outcomes are indeed viable for the practical warehouse model.

关键词： Visible light communication(VLC) industrial applications warehouse model light fixtures bit error rate(BER)

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Lung Cancer Relapse Prediction Using Self-Organizing Maps: A Clustering-Based Approach for Early Detection

Lung Cancer Relapse Prediction Using Self-Organizing Maps: A...

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2025 International Conference on Ambient Intelligence in Health Care, ICAIHC 2025

作者： Samanta, Indu Sekhar Nayak, Subrat Kumar Rout, Pravat Kumar Department of Computer Science and Engineering Bhubaneswar India Department of Electrical and Electronics Engineering Bhubaneswar India

ISBN: (纸本)9798331530259

Lung cancer relapse is very often found to affect patient prognosis and treatment outcomes potentially and may lead to more challenging management and poorer survival rates. Early prediction of relapse is necessary for timely intervention and personalized treatment plans and to handle the key effects of relapse on prognosis and treatment, such as prognosis and survival, treatment outcomes, impact on treatment strategies, psychosocial effects, and potential for clinical trials. With this motivation, this paper proposed Self-Organizing Maps (SOMs) to predict lung cancer relapse. The SOM technique is considered for predicting lung cancer relapse due to its dimensionality reduction, ability to recognize and cluster patterns, visual way to interpret complex medical data operation without the need for labelled training data, capability to handle non-linear relationships, adaptability to evolving data, and noise tolerance. It is investigated that this unsupervised learning technique clusters patients based on their clinical and pathological features, providing a valuable tool for identifying relapse-prone patients. The results reflect that the proposed approach attains high prediction accuracy by leveraging the clustering capabilities of SOMs and effectively classifying patients into relapse and non-relapse categories. It can be concluded that the SOMs have the potential to enhance early detection and personalized treatment of lung cancer relapse. © 2025 IEEE.

关键词： Personalized medicine

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Edge Caching Based on Deep Reinforcement Learning

Edge Caching Based on Deep Reinforcement Learning

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2023 IEEE/CIC International Conference on Communications in China, ICCC 2023

作者： Niknia, Farnaz Wang, Ping Agarwal, Aakash Wang, Zixu York University Department of Electrical Engineering and Computer Science Toronto Canada IIIT-Naya Raipur Electronic and Communication Engineering Naya Raipur India South China University of Technology School of Computer Science and Engineering Guangdong China

ISBN: (纸本)9798350345384

The use of mobile apps has caused a lot of data to be transmitted repeatedly, which has put a strain on the networks. One solution to this problem is caching, which stores data closer to the user to reduce data transfer and delay. Typically, when designing a caching policy, only a limited set of file features, such as freshness and popularity, are taken into account in the majority of prior studies. Nevertheless, it is crucial to incorporate factors like importance and size since highly valuable files may hold more significance compared to freely accessible ones. In this paper, we modeled caching problem using Semi-Markov Decision Process that considers file features such as popularity, lifetime, size, and importance. Then, we used a Reinforcement Learning algorithm called Double Deep Q-Learning to solve the Semi-Markov Decision Process. Simulation results show that the proposed method outperforms an existing caching method in terms of cache hit rate and total utility in various settings. This method is the first to comprehensively consider all file features, making it more practical for real-world scenarios. © 2023 IEEE.

关键词： Reinforcement learning

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