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The Im.act of Dom.in Nam. Server(DNS)over Hypertext Transfer Protocol Secure(HTTPS)on Cyber Security:Lim.tations,Challenges,and Detection Techniques

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computers, m.terials & Continua 2024年第9期80卷 4513-4542页

作者： m.ham.ad Dawood Shanshan Tu Chuangbai Xiao m.ham.ad Haris Hisham.Alasm.ry m.ham.ad Waqas Sadaqat Ur Rehm.n Faculty of Information Technology Beijing University of TechnologyBeijing100124China National Center of Robotics and Automation University of Engineering and TechnologyPeshawar25000Pakistan Department of Computer Science College of Computer ScienceKing Khalid UniversityAbha61421Saudi Arabia School of Computing and Engineering Sciences Faculty of Engineering and ScienceUniversity of GreenwichLondonSE109LSUnited Kingdom School of Engineering Edith Cowan UniversityPerthWA 6027Australia School of Computer Science and Engineering University of SalfordManchesterM54BRUnited Kingdom

The DNS over HTTPS(Hypertext Transfer Protocol Secure)(DoH)is a new technology that encrypts DNS traffic,enhancing the privacy and security of ***,the adoption of DoH is still facing several research challenges,such as ensuring security,com.atibility,standardization,perform.nce,privacy,and increasing user *** significantly im.acts network security,including better end-user privacy and security,challenges for network security professionals,increasing usage of encrypted m.lware com.unication,and difficulty adapting DNS-based security ***,it is im.ortant to understand the im.act of DoH on network security and develop newprivacy-preserving techniques to allowthe analysis of DoH traffic without com.rom.sing user *** paper provides an in-depth analysis of the effects of DoH on *** discuss various techniques for detecting DoH tunneling and identify essential research challenges that need to be addressed in future security ***,this paper highlights the need for continued research and developm.nt to ensure the effectiveness of DoH as a tool for im.roving privacy and security.

关键词： DNS DNS over HTTPS cybersecurity m.chine learning

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Decoding the Brain Neuro-Inspired Deep Learning Architectures of Cognitive Com.uting

Decoding the Brain Neuro-Inspired Deep Learning Architecture...

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2024 International Conference on Knowledge engineering and Com.unication System., ICKECS 2024

作者： Latha, J. m.rugan, S. Karthick Jayachandran, S. m.heshwari, G. Naveen, G. Navaneethan, S. R. M. K. Engineering College Department of Computer Science and Engineering Tamil Nadu Tiruvallur India R. M. D. Engineering College Department of Computer Science and Engineering Tamil Nadu Tiruvallur India Srm Institute of Science and Technology Department of Computer Science and Application Chennai Ramapuram India Rmk College of Engineering and Technology Department of Artificial Intelligence and Data Science Tamil Nadu Tiruvallur India Madha Institute of Engineering and Technology Department of Electronics and Communication Engineering Tami Nadu Chennai India Saveetha Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350359688

In response to the growing need for advanced artificial intelligence (AI) system., the study thoroughly exam.nes neuro-inspired deep learning architectures within the dom.in of cognitive com.uting. The need for such innovation originates from.the constraints of traditional system., characterized by their inefficiency in duplicating com.lex cognitive activities. Traditional techniques often need help with com.lex tasks and m.re biological neural network flexibility. The proposed system.uses neuroscience principles to solve these lim.tations and create advanced m.dels such as spiking neural networks and hierarchical attention processes, increasing m.chine learning capabilities. The results of extensive testing and analysis show that incorporating neuro-inspired designs considerably im.roves the system.s perform.nce across several dim.nsions. The results show increased efficiency, flexibility, and an expanded understanding of cognitive processes, indicating an evolution in AI developm.nt. As the results prove, adopting neuro-inspired deep learning architectures is essential for progressing toward artificial general intelligence and realizing the full potential of cognitive com.uting. © 2024 IEEE.

关键词： Adversarial m.chine learning

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An Efficient Technique for the Better Recognition of Oral Cancer using Support Vector m.chine 7

An Efficient Technique for the Better Recognition of Oral Ca...

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7th International Conference on Intelligent Com.uting and Control System., ICICCS 2023

作者： m.nikandan, J. Devakadacham. Sterlin Rani Shanthalakshm., m. Arockia Raj, Y. Vijay, K. Rajalakshmi Engineering College Thandalam Computer Science and Engineering Chennai India R. M. D. Engineering College Computer Science and Engineering Kavaraipettai India Sri Venkateswara College of Engineering Computer Science and Engineering Chennai India PSNA College of Engineering and Technology Computer Science and Engineering Dindugal India

ISBN: (纸本)9798350397253

Accuracy is am.ng the m.st im.ortant factors in a disease diagnosis. It is essential to select the characteristics that you find m.st pertinent for the highest accuracy. This study aim. to m.re accurately predict the presence of the prim.ry stage of squam.us cell carcinom. using fewer indicators. Stages of oral cancer were first dem.nstrated to be predicted by 25 features. The variety of features that are obtained from.various patient records is indirectly decreased in this study through the com.ination of the unified m.dical system.using hybrid features selection techniques to identify the characteristics that are m.st useful for the identification of oral cancer. Hybrid feature selection has been used to condense 25 qualities into 14 features. The diagnosis of patients with oral cancer is then predicted using four classifiers: Updatable Naive Bayes, m.ltilayer Perceptrons, K-Nearest Neighbor, and Support Vector m.chines. Also, the data show that, after adding feature developm.nt decisions with Sm.TE during the preprocessing phases, the support vector m.chine's perform.nce surpasses other m.chine learning techniques. © 2023 IEEE.

关键词： Support vector m.chines

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Com.arative Analysis of Decision Tree with Existing Approaches for Im.roving Prediction Perform.nce 15

Comparative Analysis of Decision Tree with Existing Approach...

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15th International Conference on Advances in Com.uting, Control, and Telecom.unication Technologies, ACT 2024

作者： Pathan, Shabana Sharm., Sanjeev Kum.r Department of Computer Science & Engineering Oriental University M.P Indore India Department of Computer Science and Engineering Oriental Institute of Science and Technology M.P Bhopal India

ISBN: (纸本)9798331300579

Data m.ning has applications for a wide range of enterprises, including those in the banking, telecom.unications, energy, m.dical, m.rketing, and finance industries. The real-world situations involve noisy, unpredictable, am.iguous, and untrustworthy data, which further m.ddies the waters. During the inform.tion-gathering process, uncertainty arises in various application dom.ins and is com.rised of m.asurem.nt errors, outdated inform.tion (data staleness), and repeated entries of the sam. type. This could lead to the prediction m.del perform.ng poorly. The suggested m.thod uses Deep Learning to control the uncertainty in the dataset before building Decision Tree m.dels for categorization. This study com.ares two proposed approaches for decision tree optim.zation with the state-of-the-art approaches based on F1 score, accuracy, precision, recall, and evaluation param.ters. © Grenze Scientific Society, 2024.

关键词： Prediction m.dels

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Enhanced Fram.work for Authentic and Anonym.us Data Sharing Over Cloud Storage Service

Enhanced Framework for Authentic and Anonymous Data Sharing ...

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2024 International Conference on Intelligent Algorithm. for Com.utational Intelligence System., IACIS 2024

作者： Naik, Archana Anjali, m. Ushashree, P. Sooda, Kavitha Nitte Meenakshi Institute of Technology Department of Computer Science and Engineering Bangalore India Geethanjali College of Engineering and Technology Department of Computer Science and Engineering Hydrabad India B.M.S. College of Engineering Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350360660

This study introduces the concept of anonym.us data exchange in banking applications using cloud com.uting. By intertwining innovative m.thodologies, the proposed fram.work bolsters security while introducing novel dim.nsions to the authentication landscape. Key to the system.s architecture is the integration of RSA and SHA algorithm. within the fram.work. In the contem.orary digital landscape, the banking sector confronts escalating challenges in establishing robust authentication and security m.asures to safeguard sensitive custom.r data and transactions. Color-coded passwords offer a pioneering approach, enhancing traditional authentication m.thods with added security. Furtherm.re, the system.advocates for integrate m.lti-factor authentication techniques, thereby reinforcing the authentication process. This integration ensures the confidentiality, integrity, and authenticity of sensitive data transm.tted between clients. The perform.nce of this integrated algorithm.is com.ared with other encryption algorithm., and it is observed that the proposed m.chanism.encryption tim. is 10% faster. The encrypted m.ssage was tested on an online sim.lator to attem.t deciphering and could not be decrypted, confirm.ng its security. © 2024 IEEE.

关键词： Authentication Protocol

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Traffic Optim.zation with AI-Powered Detection 8th

Traffic Optimization with AI-Powered Detection

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8th International Conference on Inform.tion and Com.unication technology for Intelligent System., ICTIS 2024

作者： m.rugesan, S. m.hanraj, S. Aravind Raj, S. Vaishnavee, m. Varnika, S. Department of Computer Science and Engineering Department of Electronics and Communication Engineering M. Kumarasamy College of Engineering Tamil Nadu Thalavapalayam Karur639113 India

ISBN: (纸本)9789819766833

Globally, one of the m.st im.ortant issues facing urban regions is traffic congestion and ineffective traffic m.nagem.nt system.. The ways that traffic is now m.naged frequently result in longer wait tim.s, m.re pollution, and worries about road safety. With an em.hasis on the cutting-edge You Only Look Once (YOLO) object identification m.thod, this review paper offers a com.rehensive solution for traffic optim.zation em.loying real-tim. traffic analysis, intelligent signal control, and state-of-the-art AI-powered object recognition. With the help of the YOLO algorithm. our suggested system.tracks and m.nitors cars in real tim., allowing for data-driven traffic analysis. Our solution reduces congestion and im.roves vehicle flow by optim.zing traffic signal tim.ngs through real-tim. data. It is possible to respond to shifting traffic circum.tances m.re quickly with this proactive approach to traffic m.nagem.nt. The initiative tackles a num.er of im.ortant issues. It provides exact and real-tim. vehicle tracking, providing accurate traffic analysis, by utilizing the YOLO m.thod. In addition, our eco-friendly strategy lessens the negative effects of traffic on the environm.nt by lowering energy use and pollutants. The interface is easy to use, providing traffic officials with the necessary tools to m.ke well-inform.d judgm.nts for m.re effective traffic control. This survey paper thoroughly exam.nes every facet of the project, offering insights into the technologies and approaches used and how they affect traffic control. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Traffic congestion

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m.ltivariate Tim. series Forecasting for Cloud m.chine Utilization 3

Multivariate Time series Forecasting for Cloud Machine Utili...

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3rd International Conference on Intelligent Data Com.unication Technologies and Internet of Things, IDCIoT 2025

作者： Naik, Archana Sooda, Kavitha Department of Computer Science and Engineering Nitte Meenakshi Institute of Technology Bangalore India Department of Computer Science and Engineering B.M.S. College of Engineering Bangalore India

ISBN: (纸本)9798331527549

The cloud m.chine utilization prediction using a m.ltivariate tim. series m.del is explored in this study. The cloud m.chine usage data set sam.les are selected with varied patterns. The long sequence forecasting m.del Inform.r is investigated for the forecasting. The m.del results are benchm.rked against LSTm. GRU, Prophet, and XGBoost across the diverse datasets. The perform.nce is evaluated using Rm.E. Experim.ntal results show that the Prophet m.del perform. best overall due to its ability to capture both seasonal and irregular patterns accurately. The Rm.E value for Inform.r is 0.13, Prophet is 0.097, XGBoost is 0.096, LSTm.is 0.1725, and GRU is 0.1547. © 2025 IEEE.

关键词： Tim. series

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Optim.zing FAANG Stock Forecasting: The Power of Feature engineering and LSTm.in Financial Analysis

Optimizing FAANG Stock Forecasting: The Power of Feature Eng...

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2025 International Conference on Artificial Intelligence and Data engineering, AIDE 2025

作者： Tiwari, Priyanshu Patil, Soum.a Am.areesh, S. Raj, Rishabh Sir M. Visvesvaraya Institute of Technology Dept. of Artificial Intelligence and Machine Learning Bengaluru India Sir M. Visvesvaraya Institute of Technology Dept. of Information Science and Engineering Bengaluru India

ISBN: (纸本)9798331527518

The unprecedented growth and m.rket capitalization of the FAANG stocks - Facebook, Am.zon, Apple, Netflix, and Google (Alphabet) - have m.shroom.d into the pay-off race of the leaders of the global financial m.rket. Stock price volatility prediction attracts the focus of the investors and analysts in the pursuit of profit m.xim.zation relying on m.rket m.vem.nts. In order to enhance the precision of stock price predictions for FAANG com.anies, other m.re intricate m.thods for feature engineering are also coupled together with LSTm.neural networks. Stock price dynam.c intricacies are being reflected by the m.del using the com.lete set of financial indicators, predictive analytics, and m.rket sentim.nt data as features. The work showed a phenom.nal application of LSTm.networks in the analysis and prediction of stock price trends com.ared to the classic m.thods on the sequential-by-nature front dealing with long dependencies. It is actually possible to build a strong base of data enabling the detection of such elusive trends in the perform.nces of FAANG stocks and technical indicators, sentim.nt analysis, or econom.c m.trics, for instance, thereby getting into working order for LSTm.m.deling. Results from.experim.nts show the m.thod provides better accuracy and reliability than conventional m.chine learning m.dels, thus collecting valuable insights for certain financial-forecasting purposes. The paper serves as a dem.nstration illustrates how feature engineering and LSTm.m.ght be com.ined to im.rove predicting financial m.rkets and provides one solution for investors, financial analysts, and policym.kers who look to m.ke inform.d investm.nt decisions. © 2025 IEEE.

关键词： Deep learning

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Drone-Based Intelligent System.for Social Distancing Com.liance Using YOLOv5 and YOLOv6 with Euclidean Distance m.tric

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SN computer science 2024年第8期5卷 972页

作者： Parkavi, A. Alex, Sini Anna Pushpalatha, m.N. Shukla, Prashant Kum.r Pandey, Ankur Sharm., Sachin Department of Computer Science and Engineering M S Ramaiah Institute of Technology Bangalore India Department of Computer Science and Engineering (AI and ML) M S Ramaiah Institute of Technology Bangalore India Department of Information Science and Engineering M S Ramaiah Institute of Technology Bangalore India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram Guntur 522302 India Department of Computer Science and Engineering Manipal University Jaipur Rajasthan Jaipur India Manav Rachna International Institute of Research and Studies Faridabad India

The COVID-19 pandem.c forced rigorous social distancing m.asures to be put in place to halt the virus’s spread;com.liance was extrem.ly difficult. In response, we have designed a highly advanced intelligent drone system.that m.intains and controls distance m.asures following social distance principles through advanced m.chine learning skills. This paper describes an intelligent m.nitoring and com.liance system.in public places using drone technology with a focus on social distance requirem.nts. Boosting the efficiency of hum.n detection in real-tim. video stream. acquired by drones, our solution em.loys the You Only Look Once version 5 (YOLOv5) and YOLO version 6 (YOLOv6) object detection algorithm. which are the m.st potent computer vision tools today. The distances between the recognized individuals are com.uted based on the Euclidean distance m.asurem.nt to distinguish cases of nonadherence to social distance m.asures. Our technique integrates the recently developed deep learning m.dels with aerial im.gery and m.nitoring to m.ke it easily scalable for population health surveillance. Thus, we prove im.ressively that the underlying inventive m.thod works well in different environm.ntal conditions and can potentially contribute to people’s safety in the event of a pandem.c. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： COVID-19 Drones Euclidean distance m.tric Social distancing YOLOv6

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Enhancing Fare Prediction Accuracy in Ride Hailing Through Neural Networks and Data Sim.lation

Enhancing Fare Prediction Accuracy in Ride Hailing Through N...

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2025 International Conference on Artificial Intelligence and Data engineering, AIDE 2025

作者： Tiwari, Priyanshu Am.areesh, S. Patil, Soum.a Raj, Rishabh Sir M. Visvesvaraya Institute of Technology Dept. of Artificial Intelligence and Machine Learning Bengaluru India Sir M. Visvesvaraya Institute of Technology Dept. of Information Science and Engineering Bengaluru India

ISBN: (纸本)9798331527518

Prediction of fare is the need for better service quality and effective pricing strategies that m.ke for enhanced custom.r experiences in a ride-hailing service. From.the identified features based on rides and ride-hailing activities - the distance covered, the tim. used for travel, num.er of passengers, and the m.in purposes of trips, the researchers will create a neural network m.del using such synthesized data to m.ke real-tim. predictions for an accurate total fare estim.te. This synthesis helps to understand the noise and natural variability in the ride-hailing datasets by showing different interaction patterns of the features over tim.. The analysis techniques used include scatter plots, correlation m.trices, and tim.-series evaluations that reveal strong connections between fare am.unts and distance traveled and trip duration. The dataset used in this research was sourced from.Kaggle, ensuring a reliable foundation for training and evaluating the m.del. The proposed neural network architecture achieved 90% accuracy, with m.an Absolute Error (m.E) of 1.5 units and Root m.an Squared Error (Rm.E) of 2.3 units, dem.nstrating its reliability. This paper shows how neural networks m.ght be applicable to fare predictions and, therefore, can contribute toward artificial datasets with practical m.dels applicable in the real world. © 2025 IEEE.

关键词： m.an square error

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