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Trust Based Cooperative Secure Routing Protocols for Internet of Things (IoT) 2

Trust Based Cooperative Secure Routing Protocols for Interne...

2nd International Conference on Recent Trends in Microelectronics, Automation, Computing, and Communications systems, ICMACC 2024

作者： Mekala, Sagar Ramji, Bhukya CVR College of Engineering Dept. of Computer Science and Engineering Hyderabad India Kakatiya Institute of Technology and Science Dept. of Computer Science and Engineering Hanmakonda India

ISBN: (纸本)9798350366570

The Internet of Things has rapidly emerged and continues to create services, software, sensors-embedded devices, and protocols. IoT allows physical objects to communicate, exchange information, and make decisions while performing critical jobs. The Internet of Things is enhanced by wireless sensor networks, which serve as a continuous layer. To gain adoption and spread, Internet of things-based sensor networks must overcome serious general and specialized risks and technological hurdles. The main drawbacks of wireless sensor networks are insufficient resources. Internet of Things (IOT)-based Wireless Sensor Networks have issues in energy consumption, longevity, throughput, routing, and security. The goal of this effort was to enhance network longevity, throughput, packet latency/loss, and better encounters with hostile nodes. Consider wireless energy harvesting in the proposed three-layer cluster-based wireless sensor network routing protocol to enhance network lifespan. The proposed approach is a Trust based Cooperative Secure Routing Protocol (TCSRP) technique with security system to obtain malicious activities of objects and to slant them into blacklist. Finally, experiments were done to test the methodology. It outperforms most traditional routing protocols like AODV, DSR, and DSDV. © 2024 IEEE.

关键词： Network layers

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Enhancing Transaction Security through Iris Recognition 4

Enhancing Transaction Security through Iris Recognition

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4th International Conference on Expert Clouds and Applications, ICOECA 2024

作者： Arepalli, Gopi Suresh Bhavana, Paluvai Krishna, Yakkanti Venkata Sai Surendrababu, Chittibomma Lakireddy Bali Reddy College of Engineering Dept. of computer Science and Engineering Mylavaram India Dept. of Computer Science and Engineering Mylavaram India

ISBN: (纸本)9798350385793

UPI frauds have led to increased use of biometrics for personal identification to ensure security and accuracy. Biometric UPI based on iris recognition technology improves customer service by providing a safe and paperless environment. This system replaces the existing UPI pin and affords protection to the UPI payment from cyber-attacks. During enrollment, genuine user's iris samples and UPI ID are retained in the database, and transactions are completed by capturing and matching iris patterns by using G-6 iris recognizer and biometric Framework. This innovative technology has revolutionized the way to make payments and has significantly reduced the risk of fraudulent activities. With biometric UPI, customers can enjoy a seamless experience when making transactions. The use of biometrics also ensures that there is no chance of identity theft or unauthorized access to sensitive information. © 2024 IEEE.

关键词： Authentication Protocol

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An Intelligent LLM-Powered Personalized Assistant for Digital Banking Using LangGraph and Chain of Thoughts 22

An Intelligent LLM-Powered Personalized Assistant for Digita...

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22nd IEEE International Symposium on Intelligent systems and Informatics, SISY 2024

作者： Easin, Arafat Md Sourav, Saha Tamas, Orosz Data Science and Engineering Dept. Budapest Hungary Dept. of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350385601

Text-based applications and chatbots are increasingly popular for delivering banking services and educational tools, offering convenient and efficient solutions for users. Whereas, personalized assistants have transformed user engagement in the digital banking space by utilizing Large Language Models (LLMs) in conjunction with autonomous agents. This study proposes the development of an intelligent personalized assistant for digital banking, utilizing a multi-agent framework based on the LangGraph and Chain of Thoughts (COT) prompting. While COT guarantees context-aware replies, the LangGraph design maps characteristics to nodes to improve user interactions. The objectives of this system are to enhance task efficiency and elevate the capabilities of digital banking assistants. We present a customizable digital banking system powered by LLM-based models, designed to deliver an interactive and personalized banking experience. The system supports a range of services, including adding money, transferring funds, paying bills, accessing telco services like mobile recharge, managing savings interest rates, DPS schemes, fixed deposits, and answering FAQs related to banking information. Therefore, integrating COT for logical reasoning enhances the effectiveness of multi-agent systems, as each single agent benefits from the structured reasoning process. In addition, LangGraph is employed for structured data management, enabling the assistant to support and accelerate various digital banking processes efficiently. The code implementation of this work is available for public access at: https://***/srv-sh/digital-agent. © 2024 IEEE.

关键词： Information management

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Bio-inspired Heuristic Optimization-based Cascaded Network for Diabetic Retinopathy Screening 3

Bio-inspired Heuristic Optimization-based Cascaded Network f...

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3rd International Conference on Advancement in Electrical and Electronic engineering, ICAEEE 2024

作者： Shuvo, Eshat Ahmad Rahman, Wahidur Hossain, Md Shakhawat Islam, Md. Tarequl Iqbal, Md. Sadiq Bangladesh University Dept. of Computer Science and Engineering Adabor Bangladesh Uttara University Dept. of Computer Science and Engineering Uttara Dhaka Bangladesh Center Independent University Dept. of Computer Science & Engineering & RIoT Bangladesh Khwaja Yunus Ali University Dept. of Computer Science & Engineering Sirajganj Bangladesh

ISBN: (纸本)9798350388282

Diabetic retinopathy (DR) is a common yet fatal complication of diabetic patients in which high levels of blood sugar damage the blood vessels in the retina, the light-sensitive eye tissue crucial for human vision. Early detection and timely intervention are essential to managing DR and preventing severe vision loss. Traditionally, the examination is performed by ophthalmologists manually examining the retinal fundus images to check for signs of DR. This approach is helpful but subjective, time-consuming and tedious. Artificial intelligence (AI)-guided computer vision has recently become very compelling and practical for image analysis and diagnosis. Existing AI-based methods achieved sufficient accuracy at the cost of high computing resources and large datasets. This paper proposes a cascaded network incorporating deep learning and the traditional machine learning approaches with a bio-inspired heuristic optimization algorithm for DR detection from fundus images. The proposed method achieved sufficient accuracy (97.1%) when trained using limited data and low computing machines. The AI models for the cascaded networks were selected through an exhaustive search in which five popular CNN models were used for extracting features;the Bacterial foraging optimization (BFO) was used to determine optimal features, and seven traditional machine models were used to detect the DR. The ResNet50-BFO-SVC cascaded network was found to be most suitable in this study. The proposed cascaded network brings efficiency, accuracy, scalability, and robustness to DR screening. © 2024 IEEE.

关键词： Large datasets

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Land Cover Extraction from Satellite Imagery using U-Net Model 18

Land Cover Extraction from Satellite Imagery using U-Net Mod...

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18th INDIAcom;11th International Conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Rani, K. Pushpa Sreedevi, Pogula Dastagiriah, C. Rao, K. Srinivas Subbaraju, G.V. Padmavathi, Jasti Mlr Institute of Technology Dept. of Computer Science and Engineering Hyderabad India G. Pulla Reddy Engineering College Dept. of Computer Science and Engineering Kurnool India Anurag University Dept. of Computer Science and Engineering Hyderabad India Scetw Dept. of Computer Science and Engineering Hyderabad India Hyderabad India

ISBN: (纸本)9789380544519

Satellite imagery offers extensive information that can be used for a variety of societal applications, from the number of buildings in a metropolis to the land cover types of a specific area. However, extracting such information is a difficult task. In the past, images were scanned manually by domain experts to extract features like buildings, infrastructure, and vegetation, which would take weeks or even months depending on the image size. To address this challenge, we trained our model using U-Net architecture, a deep learning model, because of its advantages in multiscale feature learning and faster processing. Data augmentation is done on the dataset in order to enhance the amount of data and prevent overfitting artificially. The trained model achieved an F1 score of 92% and an impressive precision score of 95%. This automated method can be scaled to large sets of data, reducing the need for human labeling. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： Data augmentation Deep learning Machine learning Multiscale feature learning U-Net architecture

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EVALUATION OF HYDROELASTIC RESPONSE AND STRUCTURAL FAILURE OF PONTOON TYPE FLOATING OFFSHORE WIND TURBINES SUBJECTED TO WIND AND OCEAN WAVES

Journal of Structural and Construction Engineering

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Journal of Structural and Construction engineering 2024年第816期89卷 235-246页

作者： Fujita, Ken-Ichi Yashiro, Harumi Dept. of Engineering Faculty of Engineering Nagasaki Institute of Applied Science Japan School of Systems Engineering National Defense Academy of Japan Japan

An evaluation method of hydroelastic response and structural failure of pontoon type floating offshore wind turbines for wind and ocean waves is presented. The response to the mean wind speed is analyzed statically and to both variable wind speed and waves are analyzed by using a stationary random vibration theory. The failure is evaluated by using a first order reliability method. A boundary and finite element hybrid model is used to the hydroelastic analysis of the structures. Moreover, variations of the response and the failure probability of the structures for different wind speed and direction are discussed. © 2024 Architectural Institute of Japan. All rights reserved.

关键词： Wind

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Applying Deep Learning Methods on Spam Review Detection 7

Applying Deep Learning Methods on Spam Review Detection

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7th International Conference on Computing Methodologies and Communication, ICCMC 2023

作者： Ramu, M. Raj, Chinnakotla Jayanth Nithish, Apthiri Boggula, Chandhu Ganesh Babu, G. Srikanth Goud, K.I. Dept. of Computer Science and Systems Engineering SVEC Andhrapradesh Tirupati India

ISBN: (纸本)9781665464086

In today's environment, a reliable and effective technique for identifying spam reviews is essential if you want to purchase things online without being taken advantage of. There are possibilities for publishing reviews in many internet locations, which opens the door for sponsored or deceptive fake reviews. These fabricated evaluations may mislead the general audience and leave them unsure of whether or not to believe them. The issue of spam review finding has been solved by the introduction of prominent deep literacy methods. The focus of recent research has been on supervised literacy practices that contain labelled data, which is inadequate for online review. This initiative aims to expose any dishonest textbook reviews. To do this, we've used both labelled and unlabeled data and suggested deep learning techniques for spam review detection, including Multi-Layer Perceptron (MLP), Convolutional Neural Networks (CNN), and a Long Short-Term Memory (LSTM) variation of Recurrent Neural Networks (RNN). We also used standard machine learning classifiers to identify spam reviews, including Naive Bayes (NB), K Nearest Neighbor (KNN), and Support Vector Machine (SVM). Finally, we compared the effectiveness of traditional and deep literacy classifiers. We'll use deep literacy classifiers to boost the finesse and efficiency. © 2023 IEEE.

关键词： Long short-term memory

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An Integrated Framework with Enhanced Primitives for Post-Quantum Cryptography: HEDT and ECSIDH for Cloud Data Security and Key Exchange

Informatica (Slovenia)

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Informatica (Slovenia) 2025年第11期49卷 165-176页

作者： Ilias, Shaik Mohammad Sharmila, V. Ceronmani Durga, V. Sathya Dept. of Computer Science and Engineering Hindustan Institute of Technology and Science Chennai India Department of Information Technology Hindustan Institute of Technology and Science Chennai India Department of Computer Science&Engineering Hindustan Institute of Technology and Science Chennai India

If adversaries were to obtain quantum computers in the future, their massive computing power would likely break existing security schemes. Since security is a continuous process, more substantial security schemes must be developed. Current PQC schemes primarily focus on data security or key exchange, and further improvement towards enhanced PQC primitives is required. Our proposal in this research is an innovative paradigm for PQC-focused cloud data security. The proposed HEDT approach achieves encryption and decryption with significantly lower latency (20% improvement) and higher reliability than AES, DES, and RSA, as demonstrated through experimental results. Furthermore, ECSIDH, a hybrid key exchange mechanism combining SIDH and ECDH, improves security strength by 50% while maintaining computational costs within 1.13x of SIDH. Compared to individual key exchange schemes like SIDH, ECSIDH offers superior security as a PQC candidate. These results confirm the robustness and efficiency of the proposed framework in ensuring secure data outsourcing and key exchange in cloud environments. © 2025 Slovene Society Informatika. All rights reserved.

关键词： Quantum computers

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Multiple Approaches Towards Authentication Using Keystroke Dynamics 2

Multiple Approaches Towards Authentication Using Keystroke D...

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2nd International Conference on Machine Learning and Data engineering, ICMLDE 2023

作者： Arsh, Aditya Kar, Nirmalya Das, Smita Deb, Subhrajyoti Dept. of Computer Science & Engineering NIT Agartala Tripura India Dept. of Computer Science & Engineering ICFAI University Tripura India

In the realm of global computer security, safeguarding sensitive data and computer systems stands as an ongoing challenge. Striking the delicate balance between convenient access for legitimate users and thwarting imposter attacks is imperative. Traditional authentication methods, primarily reliant on username and password schemes, have been the longstanding cornerstone of cyber system authentication. Yet, this conventional approach is marred by inherent vulnerabilities, including password exchange, shoulder surfng, brute force attacks, dictionary attacks, speculation, phishing, and emerging threats. In response to these shortcomings, biometrics technology has emerged as a reliable alternative for authentication and verification, with Keystroke Dynamics being a well-established method. This research advances Keystroke Dynamics-based authentication through cutting-edge technologies, including machine learning, deep learning, and neural networks. A quantitative assessment demonstrates remarkable results - Machine Learning achieves 99.9% accuracy with the Random Forest model, Deep Learning combines CNN with GRU to reach 99.31% accuracy, and the DBN model registers at 98.01%. In the realm of neural networks, Bi-CNN and FFM-NN excel, with accuracy rates of 96.8% and 94.7%, respectively. These findings lay the foundation for robust, secure authentication systems, ensuring the protection of sensitive data and computer assets in our interconnected world. © 2024 Elsevier B.V.. All rights reserved.

关键词： Authentication

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Automated Exam Script Checking using Zero-Shot LLM and Adaptive Generative AI

Automated Exam Script Checking using Zero-Shot LLM and Adapt...

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2024 IEEE International Conference on Computing, Applications and systems, COMPAS 2024

作者： Himi, Shinthi Tasnim Tanzila Monalisa, Natasha Sultana, Shirin Afrin, Anika Hasib, Khan Md Bangladesh University of Business and Technology Dept. of Computer Science and Engineering Dhaka Bangladesh Jahangirnagar University Dept. of Computer Science and Engineering Dhaka Bangladesh Brac University Dept. of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798331529765

The manual grading of exam scripts is a labor-intensive process, often plagued by subjectivity and inconsistency. This study presents an innovative approach to automating the evaluation of exam scripts using the Large Language Model (LLM) integrated with Zero-Shot Learning (ZSL) and Generative AI. Our system leverages the capabilities of GPT-4 to generate and evaluate answers, applying similarity measures to ensure accuracy and fairness in grading. The model's adaptability through ZSL allows it to assess new questions without additional training. The system also incorporates iterative refinement techniques to enhance the quality of standard answers over time. Performance evaluations demonstrate a low error rate, with an average relative error of 1.29% for annotated questions and 1.67% for non-annotated questions compared to human graders. The performance underscores the automated system's transformative potential in revolutionizing educational assessments, offering a consistent, fair, and highly efficient alternative to traditional grading methods. © 2024 IEEE.

关键词： Zero-shot learning

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