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A Study on Kerberos and Graphical Password-Based Web Authentication Scheme 1

1st International Conference on Machine Intelligence for Research and Innovations, MAiTRI 2023

作者： Murugan, Thangavel Sikdar, Sagar Semwal, Mihir Indira, K. Department of Information Systems & Security College of Information Technology United Arab Emirates University Abu Dhabi Al Ain15551 United Arab Emirates University of Erlangen–Nuremberg Erlangen Germany Bank of America Haryana Gurugram India Department of Computer Science and Engineering Thiagarajar College of Engineering Tamil Nadu Madurai India

ISBN: (数字)9789819981359

ISBN: (纸本)9789819981342

Web authentication methods are subject to several attacks due to the rapid expansion of information technology. It is apparent that the evolution of authentication-bypassing strategies, from brute force to dictionary attacks, will advance. Simple word lists, for instance, are employed in traditional dictionary attacks to crack commonly used usernames and passwords. Robust hashing methods can be used to safeguard credentials sent via an insecure communication channel. However, hackers have created a novel hybrid method called a ‘rainbow attack’ that is like dictionary attacks but focuses on password hashes. In today’s digital era, traditional security methods like one-factor authentication are ineffective at combating the previously mentioned cyber-attacks. Even two-factor authentication may be bypassed using advanced cyber-methods. This work focuses on advanced cyber-techniques that can defeat even two-factor authentication. Server-side forging is one technique used to get around the restrictions imposed by two-factor authentication. Therefore, it is obvious that strong authentication entities are needed if the danger of credential bypassing needs to be lowered, especially in the Web 3.0 platform. In this proposed study, the objective is to discuss the authentication strategies and improved strategies based on Kerberos and graphical password. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Authentication

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Real-time Battery State of Charge and parameters estimation through Multi-Rate Moving Horizon Estimator

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IFAC-PapersOnLine 2023年第2期56卷 6124-6129页

作者： Tushar Desai Federico Oliva Riccardo M.G. Ferrari Daniele Carnevale Delft Center for Systems and Control Delft University of Technology Delft Netherlands Department of Civil Engineering and Computer Science Engineering University of Rome "Tor Vergata" Rome Italy

For reliable and safe battery operations, accurate and robust State of Charge (SOC) and model parameters estimation is vital. However, the nonlinear dependency of the model parameters on battery states makes the problem challenging. We propose a Moving-Horizon Estimation (MHE)-based robust approach for joint state and parameters estimation. Dut to all the time scales involved in the model dynamics, a multi-rate MHE is designed to improve the estimation performance. Moreover, a parallelized structure for the observer is exploited to reduce the computational burden, combining both multi-rate and a reduced-order MHEs. Results show that the battery SOC and parameters can be effectively estimated. The proposed MHE observers are verified on a Simulink-based battery equivalent circuit model.

关键词： Energy systems Observer Design Battery Management Robust Estimation Moving Horizon Estimation Parameter-varying systems

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Auto Digit Selection for Most Significant Digit First Multiplication

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IEEE Embedded systems Letters 2025年

作者： Gorgin, Saeid Jaberipur, Ghassem Lee, Jeong-A Shin, Seokjoo Kim, Jungrae Computer Engineering Iranian Research Organization for Science and Technology Tehran Iran Semiconductor Systems Engineering Department College of Information and Communication Engineering SungKyunKwan University Korea Republic of Brain Pool Program for Chosun University Gwangju Korea Republic of Shahid Beheshti University Tehran Iran Chosun University Department of Computer Engineering Gwangju Korea Republic of

Performance in modern embedded systems, particularly those executing computation-intensive signal/image processing and machine learning algorithms, is critically dependent on the efficiency of multiplication operations. Serial multiplication offers key advantages in these resource-constrained environments, including low memory usage and minimal interconnect complexity. The most significant digit first (MSDF) serial multiplication scheme further enhances efficiency by enabling early termination and supporting variable precision. It is ideal for embedded applications where power and performance are critical. This paper proposes an auto-selection scheme for MSDF multiplication, where product digits are generated as a byproduct of residual maintenance, leading to speed, cost, and power efficiency improvements. We demonstrate the implementation of serial-serial and serial-parallel multipliers, where the multiplier operand in the latter is fully available throughout the computation. Simulation and synthesis results, compared to the most relevant previous works, show reductions in delay (16% and 20%), area consumption (11% and 23%), and power dissipation (7% and 20%), making our approach well-suited for embedded systems requiring efficient, low-power multiplication hardware. © 2009-2012 IEEE.

关键词： Left-to-Right Arithmetic Machine Learning Algorithms MSDF Multiplication Online Arithmetic

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Optimizing Facial Recognition: An Analytical Comparison of Traditional and Deep Learning Approaches

Optimizing Facial Recognition: An Analytical Comparison of T...

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Data science and Its Applications (ICoDSA), International Conference on

作者： Shadman Sakib Md Nurul Absar Siddiky Md. Arifuzzaman Md. Jalal Uddin Chowdhury Department of Information Systems University of Maryland Baltimore County Maryland USA Department of Electrical and Computer Engineering University of North Carolina at Charlotte Charlotte USA Department of Computer Science and Engineering Leading University Sylhet Sylhet Bangladesh

ISBN: (数字)9798350365351

ISBN: (纸本)9798350365368

In the rapidly evolving domains of AI and Internet tech, face recognition, a key machine learning application, is increasingly used in security, identity verification, and public monitoring. As this technology progresses, its applications are expanding. A critical factor in its growing popularity is the advancement of the underlying algorithms, which drives its effectiveness in various applications. This paper investigates facial recognition technologies using the Olivetti faces dataset. It compares the efficacy of Principal Component Analysis (PCA), Eigenface method, Support Vector Machine (SVM) with linear kernel, a standard Convolutional Neural Network (CNN), and a CNN-Transfer Learning approach with MobileNet. The transfer learning is trained with the Adam optimizer (learning rate 0.0004) and categorical cross entropy loss. Over 100 epochs with a batch size of 64, the model's performance, in terms of accuracy and loss, is meticulously tracked and visualized, providing insights into its learning progression. The final evaluation includes a detailed classification report, highlighting precision, recall, and F1-scores, and these metrics are graphically represented for a comprehensive understanding of the model's performance. The CNN model achieved 80% accuracy, while PCA and SVM exhibited high accuracies, reaching 91% and 97.5%, respectively. We have also applied the eigenface method and achieved 91% accuracy in face recognition. Notably, the CNN-Transfer Learning method demonstrates superior performance with a 95% accuracy, highlighting the potential of deep learning in facial recognition applications.

关键词： Support vector machines Deep learning Adaptation models Accuracy Face recognition Vectors Convolutional neural networks

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HERA: A Novel Heuristic Resource Allocator for Multi-SDM Multi-User Settings

HERA: A Novel Heuristic Resource Allocator for Multi-SDM Mul...

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IEEE Conference on Standards for Communications and Networking (CSCN)

作者： Christos Antoniadis Kostas Katsalis Dimitrios Tyrovolas Sotiris A. Tegos Sotiris Ioannidis Panagiotis D. Diamantoulakis George K. Karagiannidis Christos Liaskos Department of Electrical and Computer Engineering Aristotle University of Thessaloniki Thessaloniki Greece DOCOMO Euro-Labs Munich Germany Dept. of Electrical and Computer Engineering Technical University of Crete Chania Greece Artificial Intelligence & Cyber Systems Research Center Lebanese American University (LAU) Lebanon Computer Science Engineering Department University of Ioannina Ioannina Greece Institute of Computer Science Foundation for Research and Technology Hellas Greece

ISBN: (数字)9798331507428

ISBN: (纸本)9798331507435

The advent of Programmable Wireless Environments (PWEs) has transformed the wireless propagation phenomenon into a software-defined resource, leveraging Software-defined metasurfaces (SDMs). These new technologies have shown that wireless waves can be routed within a space, contrary to the regular, chaotic wireless propagation, yielding considerable benefits to communication efficiency, and even completely new applications. A new topic has risen, in the context of modeling these capabilities as network resources, and allocating them to users. In this context, the present paper contributes HERA, a heuristic resource allocator that receives a setup with users and SDMs as input, and produces the necessary configuration of the latter, for efficient shared performance. Simulation results demonstrate considerable efficiency in this task, while the heuristic nature of HERA sets the basis for a flexible definition of complex user objectives in the future.

关键词： Wireless communication Chaotic communication Simulation Metasurfaces Delays Particle swarm optimization Standards Optimization Context modeling

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Parallel Path Planning for Multi-Robot Coordination

Parallel Path Planning for Multi-Robot Coordination

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IEEE International Conference on Autonomous Robot systems and Competitions (ICARSC)

作者： Jorge Ribeiro Marina Brilhante Diogo Miguel Matos Carlos A. Silva Héber Sobreira Pedro Costa CRIIS - Centre of Robotics in Industry and Intelligent Systems INESC TEC - Institute for Systems and Computer Engineering Technology and Science Porto Portugal Faculty of Engineering University of Porto Porto Portugal Flowbotic Mobile Systems S.A Viseu Portugal

ISBN: (数字)9798331538606

ISBN: (纸本)9798331538613

Multi-robot coordination aims to synchronize robots for optimized, collision-free paths in shared environments, addressing task allocation, collision avoidance, and path planning challenges. The Time Enhanced A* (TEA*) algorithm addresses multi-robot pathfinding offering a centralized and sequential approach. However, its sequential nature can lead to order-dependent variability in solutions. This study enhances TEA* through multi-threading, using thread pooling and parallelization techniques via OpenMP, and a sensitivity analysis enabling parallel exploration of robot-solving orders to improve robustness and the likelihood of finding efficient, feasible paths in complex environments. The results show that this approach improved coordination efficiency, reducing replanning needs and simulation time. Additionally, the sensitivity analysis assesses TEA*'s scalability across various graph sizes and number of robots, providing insights into how these factors influence the efficiency and performance of the algorithm.

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Development of Fast Prediction System of User Action for a Fully Autonomous Smart Home System 4

Development of Fast Prediction System of User Action for a F...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Rajalakshmi, N.R. Sharmila, L. Prabu, S. Veeramakali, T. Arulkumaran, G. Chourasia, Pranshu Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu India School of Computing Srm Institute of Science and Technology Department of Computing Technologies Kattankulathur Chennai India School of Computing Srm Institute of Science and Technology Department of Data Science and Business Systems Kattankulathur Chennai India School of Computing and Information Technology Reva University Karnataka Bangalore560064 India

ISBN: (纸本)9798331523923

The HVAC system, energy storage building, distributed power supply, and other equipment are integrated into the scheduling algorithm, which is aimed at reducing household electricity consumption. It is also assumed that users can provide energy to the grid according to their own conditions. Taking electricity cost and comfort level as optimization targets, a home energy optimization control model for the coordinated management of hybrid energy sources is built. A smart scheduling mechanism based on the improved adaptive particle swarm optimization approach is proposed in order to derive the best time intervals for electric appliances, necessary power for the control of the room temperature for every time frame, and power for charging and discharging of the storage battery at various moments. Simulation results show that through the incorporation of distributed photovoltaic power generation, backup storage by battery, and home energy optimization control, the system efficiently balances between user comfort and electricity consumption. This offers great technical support to the development of home energy management systems. By using time-of-use electricity price for energy acquisition and supply, the optimization control goal is minimizing both power use and cost as well as preserving comfort levels. The hybrid energy management's proposed home energy optimization control model uses an adaptive particle swarm optimization algorithm to find the optimal operation schedules of the electrical appliances, the required power for temperature control in a room, and the charge/discharge power level of the storage battery at each time interval. As per the optimization principle, the proposed dynamic programming algorithm converts the multi-stage problem into a sequence of single-stage problems and solves them separately. This method successfully resolves intricate problems that cannot be addressed through greedy algorithms or divide-and-conquer. In this research, management ac

关键词： Decision making

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Weighted Null Space Fitting (WNSF): A Link between The Prediction Error Method and Subspace Identification

arXiv

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arXiv 2024年

作者： He, Jiabao Hjalmarsson, Håkan Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm100 44 Sweden

— Subspace identification method (SIM) has been proven to be very useful and numerically robust for estimating state-space models. However, it is in general not believed to be as accurate as prediction error method (PEM). Conversely, PEM, although more accurate, comes with non-convex optimization problems and requires local non-linear optimization algorithms and good initialization points. This contribution proposes a weighted null space fitting (WNSF) method to identify a state-space model, combining some advantages of the two mainstream approaches aforementioned. It starts with the estimate of a nonparametric model using least-squares, and then the reduction to a state-space model in the observer canonical form is a multi-step least-squares procedure where each step consists of the solution of a quadratic optimization problem. Unlike SIM, which focuses on the range space of the extended observability matrix, WNSF estimates its null space, avoiding the need for singular value decomposition. Moreover, the statistically optimal weighting for the null space fitting problem is derived. It is conjectured that WNSF is asymptotically efficient, which is supported by a simulation study. Copyright © 2024, The Authors. All rights reserved.

关键词： State space methods

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Novel Rank-based Features of Atrial Potentials for the Classification Between Paroxysmal and Persistent Atrial Fibrillation

Novel Rank-based Features of Atrial Potentials for the Class...

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2022 Computing in Cardiology, CinC 2022

作者： Moghaddasi, Hanie Hendriks, Richard C. Van Der Veen, Alle-Jan De Groot, Natasja M.S. Hunyadi, Borbala Delft University of Technology Circuits and Systems Faculty of Electrical Engineering Mathematics and Computer Science Delft Netherlands Erasmus University Medical Center Department of Cardiology Rotterdam Netherlands

ISBN: (纸本)9798350300970

Atrial fibrillation (AF) is the most common arrhythmia. Although the exact cause is unclear, electropathology of atrial tissue is one contributing factor. Electropathological characteristics derived from intra-operative epicardial measurements, such as conduction block (CB) and continues conduction delay and block (cCDCB), can be used to assess the severity of AF. In sinus rhythm, however, these parameters do not indicate significant difference between different development stages of AF, such as paroxysmal and persistent AF. Therefore, we propose a methodology to improve AF severity detection using intra-operative electrograms. We propose a model that describes the spatial diversity of atrial potential waveforms during a single beat on the multi-channel electrograms. Based on this model, we derive two novel features. During sinus rhythm, we used 293 beats from patients with a history of paroxysmal or persistent AF. Using a random forest classifier, we achieved 78.42% classification accuracy, while classification based on the CB and cCDCB leads to an accuracy of 58.34%. © 2022 Creative Commons.

关键词： Cardiology

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Novel Approach for Offshore Photovoltaic Panels Inspection with VTOL UAV

Novel Approach for Offshore Photovoltaic Panels Inspection w...

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OCEANS

作者： R. Morais João J. Martins P. Lima A. Dias A. Martins J. Almeida E. Silva Institute for Systems and Computer Engineering Technology and Science (INESC TEC) Instituto Superior de Engenharia do Porto (ISEP) Porto Portugal

ISBN: (数字)9798350362077

ISBN: (纸本)9798350362084

Solar energy will contribute to global economic growth, increasing worldwide photovoltaic (PV) solar energy production. More recently, one of the outstanding energy achievements of the last decade has been the development of floating photovoltaic panels. These panels differ from conventional (ter-restrial) panels because they occupy space in a more environmen-tally friendly way, i.e., aquatic areas. In contrast, land areas are saved for other applications, such as construction or agriculture. Developing autonomous inspection systems using unmanned aerial vehicles (UAV s) represents a significant step forward in solar PV technology. Given the frequently remote and difficult-to-access locations, traditional inspection methods are no longer practical or suitable. Responding to these challenges, an in-novative inspection framework was developed to autonomously inspect photovoltaic plants (offshore) with a Vertical Takeoff and Landing (VTOL) UAV. This work explores two different methods of autonomous aerial inspection, each adapted to specific scenarios, thus increasing the adaptability of the inspection process. During the flight, the aerial images are evaluated in real-time for the autonomous detection of the photovoltaic modules and the detection of possible faults. This mechanism is crucial for making decisions and taking immediate corrective action. An offshore simulation environment was developed to validate the implemented system.

关键词： Economics Oceans Solar energy Production Inspection Autonomous aerial vehicles Real-time systems

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