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Security Analysis of RPL-Based IoT Networks: Evaluating the Impact of Attacks on Performance Parameters 2

Security Analysis of RPL-Based IoT Networks: Evaluating the ...

引用

2nd International Conference on Advancements in Smart, Secure and Intelligent Computing, ASSIC 2024

作者： Gupta, Himanshu Bhardwaj, Sonam Dave, Mayank National Institute of Technology Department of Computer Engineering Kurukshetra India

ISBN: (纸本)9798350370188

IoT networks employ the RPL routing protocol as it is lightweight and suitable for lossy networks and establishes a Destination Oriented Directed Acyclic Graph (DODAG). The primary constraints on RPL-based networks are limited power sources, lifespan, and reliability. This article examines different types of attacks that can occur in loT network that uses RPL. The Contiki OS Cooja simulator is utilized to simulate various types of attacks. The evaluation of the attacks is conducted by analyzing their impact on various performance parameters, including Packet Delivery Ratio (PDR), Power Consumption, Control Message Overhead, and Network Graph. Upon conducting the analysis, it is determined that these attacks have a detrimental effect on loT networks making it crucial to prioritize the mitigation of vulnerabilities and security risks that are linked to loT devices. © 2024 IEEE.

关键词： Risk assessment

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Edu-lingo: A Unified NLP Video System with Comprehensive Multilingual Subtitles 2

Edu-lingo: A Unified NLP Video System with Comprehensive Mul...

引用

2nd International Conference on Data Science and Information System, ICDSIS 2024

作者： Kaulage, Anant Walunj, Ajinkya Bhandari, Akash Dighe, Aneesh Sagri, Anish Vishwakarma Institute Of Technology Department Of Computer Engineering Pune India

ISBN: (纸本)9798350381665

In India's diverse educational landscape, linguistic barriers and hearing impairments present significant challenges to effective learning. The research introduces an innovative NLP-driven solution aimed at overcoming these hurdles by offering a comprehensive educational platform. This platform includes features such as subtitle generation, text summarization, Q&A generation for content recall, multilingual translation, and Indian Sign Language (ISL) support. Leveraging a fine-tuned Whisper model for Speech-to-Text Translation (STT) and conducting a comparative study of BERT, T5, and TextRank for subtitle summarization, this approach ensures optimized language understanding and content generation across various NLP applications. Through the utilization of ConceptNet and BERT for question answering tasks, the system demonstrates impressive performance metrics, including a 95.12% accuracy rate in subtitle generation and an average WER of 3.43%. While manual evaluation was necessary due to the absence of specific datasets, this innovative approach represents an advancement in addressing educational accessibility gaps in India, fostering the Right to Education to all individual users in an educational environment. © 2024 IEEE.

关键词： Natural language processing systems

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Advancing Software-Defined Vehicles: An End-to-End Framework with Digital Twin Based Attestation for OTA Updates 17

Advancing Software-Defined Vehicles: An End-to-End Framework...

引用

17th International Conference on COMmunication Systems and NETworkS, COMSNETS 2025

作者： Agrawal, Krish Rohan, Jha Luthra, Nishkarsh Sekhar, Pilla Venkata Sharma, Hrishesh Banda, Gourinath Indian Institute of Technology Department of Computer Science and Engineering Indore India

ISBN: (纸本)9798331531195

Software-defined vehicles (SDVs) are an emerging paradigm in the automotive industry where vehicles' functionality, performance, and safety can be enhanced and updated through software, even after production. Unlike traditional vehicles, which rely primarily on physical components, SDVs leverage advanced connectivity, real-time data analytics, and cloud integration to adapt to changing regulations, driver preferences, and environmental conditions. This shift enables vehicles to evolve continuously, responding to new technological advances and customer needs. In this paper, we propose an end-to-end framework to demonstrate and realize the full benefits of SDVs. We incorporate the concept of digital twin (of the vehicle) driven software update authorization to download or update an application on the vehicle. In our framework, attestation refers to the verification of each software update's compatibility and functionality with the vehicle's current ECUs before deployment. Any impending/requested update is first verified for its compatibility with the vehicle's architecture (as per the twin) on the cloud. The idea is via the SDVs' digital twin - since each and every ECU is virtualized - the simulation and testing of the actual hardware setup with the new application software can be done without direct impact on the physical vehicle. Once the application is installed across all relevant virtualized ECUs, the framework confirms compatibility, ensuring smooth deployment and functionality in the real-world vehicle. Post this successful attestation, the installation will be done on the real vehicle corresponding to that digital twin. Through this proposed framework, we intend to ensure the safety of the updation, while the new updates contribute to the functionality and performance improvements. © 2025 IEEE.

关键词： Automotive industry

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Deep Learning Based Approaches for Handling New User Cold Start Problem

Deep Learning Based Approaches for Handling New User Cold St...

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International Conference on Systems, Control and Automation, ICSCA 2023

作者： Teja, Pulakala Verma, Vijay Department of Computer Engineering National Institute of Technology Haryana Kurukshetra India

ISBN: (纸本)9789819773831

The excess growth of digital information on the Internet and the excessive use by many users have created an issue of information over-load, where the data remains unfiltered and uncategorized. Many large corporations, like Deezer, JioSaavn, Spotify, and Wynk music streaming apps, have included recommendation algorithms to assess possible consumer preferences and propose suitable products to the user. However, it can be challenging to recommend an item to a new user with minimal or no interaction with the system. This problem is popularly known as the new user cold start problem, which refers to the issue of recommending music to users with little or no listening history. Deep learning-based approaches have recently shown great potential in addressing this challenge. This paper aims to extensively analyze the latest deep learning techniques to tackle the challenge of the new user cold start issue in the music recommendation field. Additionally, the paper explores the potential of using supplementary data such as user profiles and lyrics to enhance the performance of deep learning-based methods. Finally, we critically analyze the current state-of-the-art and identify open research directions for future work. This paper provides a valuable resource for researchers and practitioners in the music recommendation domain who seek to use deep learning-based approaches to tackle the new user cold start problem. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Matrix factorization

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Kavach - An Investigation tool for Cryptocurrency Forensics 4

Kavach - An Investigation tool for Cryptocurrency Forensics

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4th IEEE Asian Conference on Innovation in technology, ASIANCON 2024

作者： Shinde, Sandeep Chordia, Sankalp Nilesh Vishwakarma Institute of Technology Department of Computer Engineering Pune India

ISBN: (纸本)9798350354218

The expansion of decentralized cryptocurrencies poses notable complexities for law enforcement in terms of detecting unlawful behaviors, as well as in the identification of individuals and the retrieval of transaction histories of perpetrators who take advantage of the pseudonymous nature inherent in the cryptocurrency system. This research paper puts forth a solution called Kavach. The designated user of Kavach will be an investigator. The tool leverages graph machine learning for the categorization of transactions as illicit or legitimate. It utilizes graph-based embeddings for the recognition of potentially suspicious addresses within the bitcoin network. The tool incorporates a predefined watch list for such suspicious addresses and will have the capability to trace the digital trail of these addresses using Open Source Intelligence (OSINT). © 2024 IEEE.

关键词： Graph embeddings

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Dynamic 3D Facial Expression Reconstruction from Images 5

Dynamic 3D Facial Expression Reconstruction from Images

引用

5th International Conference on Image Processing and Capsule Networks, ICIPCN 2024

作者： Bharadwaj, Rakhi Borse, Rushikesh Ahire, Purvesh Aldhar, Prathamesh Hoshing, Atharva Vishwakarma Institute of Technology Department of Computer Engineering Pune India

ISBN: (纸本)9798350367171

In today's digital landscape, the demand for highly realistic 3D representations of human faces has surged, driven by the rapid expansion of virtual reality, augmented reality, and face recognition technologies. Despite significant strides in 3D facial geometry reconstruction from 2D images, achieving accurate and detailed translations of facial features and expressions remains a formidable challenge, critical for creating truly immersive digital experiences. In response, our research presents a groundbreaking approach to detailed reconstruction, specifically designed to convert single 2D images into intricately detailed and lifelike 3D models. Our methodology combines state-of-the-art techniques, such as UV displacement mapping and detail decoding, to meticulously capture even the subtlest nuances within 2D images, enhancing aspects like facial textures, sharp edges, and nuanced lighting effects. Furthermore, we employ advanced detail rendering techniques to further enrich the fidelity of the resulting 3D models, effectively narrowing the gap between digital representations and their real-world counterparts. Through the utilization of specialized loss functions, we meticulously optimize the reconstruction process, ensuring the faithful preservation of fine details while maintaining overall coherence and fidelity. This meticulous refinement promises to deliver immersive digital experiences that not only capture users' attention but also deeply engage them, offering a significant advancement in the realm of 3D facial reconstruction. © 2024 IEEE.

关键词： 3D reconstruction

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Analysis of the various YOLOv8 models in Detecting Defects in PCB 15

Analysis of the various YOLOv8 models in Detecting Defects i...

引用

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

作者： Raj, Abhishek Ahlawat, Priyanka National Institute of Technology Department of Computer Engineering Kurukshetra India

ISBN: (纸本)9798350370249

In the arena of electronics, the Printed Circuit Board (PCB) forms the cornerstone of every component, extending from everyday gadgets to advanced defense equipment. The escalating pace of modern life necessitates accelerated PCB production, calling for swift and precise mechanisms to ensure defect-free output for reliable manufacturing. Though such quality control tasks were traditionally manual, the advent of machine learning and deep learning has ensured automated and reliable solutions. A standout among these is YOLO, a speedy computer vision algorithm renowned for its unmatched speed and accuracy. This paper seeks to conduct an all-encompassing study of the cutting-edge YOLOv8 sub-models, delving into their individual performance attributes. The work encapsulates the implementation of these YOLOv8 models, followed by a rigorous analysis of their results on a range of metrics like mAP50, mAP50 - 95, precision, and recall. An integral part of this study is the preprocessing of the HRIPCB dataset, comprising six distinct defect categories, namely Missing hole, Mouse bite, Spur, Spurious copper, Open circuit, and Short. The ensuing robust methodology involves comprehensive data preprocessing, data cleansing, and standardization. Ensuring a high-level of data quality, image enhancement techniques such as Colour Balance Adjustment, Histogram Equalization, and Image Filtering - Sharpening are employed. The study shines a spotlight on the individual training and testing processes of the YOLOv8 sub-models, namely v8n, v8s, v8m, v8l, and v8x, leveraging various hyperparameters to optimize model performance. By surpassing the 95.8% mAP50 achieved by earlier YOLO versions, this research aspires to revolutionize PCB defect detection. It paves the way for enhanced quality control mechanisms in PCB manufacturing, thus contributing to the advancement of electronics production regimes © 2024 IEEE.

关键词： Deep Learning Defect detection Image recognition Printed circuit board YOLOv8

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Deep Convolutional Neural Network Implementation for Detecting Generative Adversarial Network Generated Deepfake Videos

Deep Convolutional Neural Network Implementation for Detecti...

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International Conference on Systems, Control and Automation, ICSCA 2023

作者： Gautam, Abhishek Singh, Awadhesh Kumar Department of Computer Engineering National Institute of Technology Haryana Kurukshetra India

ISBN: (纸本)9789819773831

Deep learning algorithms have become so powerful as computing power has increased that it has become much easier to create indistinguishable human-synthesized videos, dubbed DeepFake. It’s easy to imagine scenarios in which these realistic Deepfakes are used to commit political pranks, staged terrorist assaults, revenge pornography, and blackmail. This challenge describes a revolutionary deep learning-based technique for detecting genuine AI-generated videos from forgeries. Our method can automatically recognise surrogate and recurring deepfakes. We’re using artificial intelligence to try to counteract it. Our solution use a Deep Convolutional Neural Network to extract framelevel data and then train those features. A recurrent neural network with long-short-term memory used to determine if a picture has been edited. A huge set of balanced blended datasets was created by combining the numerous datasets available in order to recreate real- time events and enhance model performance using real-time data. A simple and dependable way for illustrating how a system might produce competitive performance. An interactive Graphical User Interface (GUI) is also developed for easy access. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Generative adversarial networks

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RISE: Modular Blockchain-Integrated Enhancements for Reducing Food Waste and Boosting Retail Accountability 17

RISE: Modular Blockchain-Integrated Enhancements for Reducin...

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17th International Conference on COMmunication Systems and NETworkS, COMSNETS 2025

作者： Sharma, Rishika Banda, Gourinath Indian Institute of Technology Department of Computer Science and Engineering Indore India

ISBN: (纸本)9798331531195

Effective management of semi-perishable food items is essential for minimising waste, maintaining food safety, and supporting sustainability efforts. This paper introduces the Responsibility Index for Sustainability and Efficiency (RISE), a set of modular components designed to seamlessly integrate with existing invoicing systems and enhance their performance by improving transparency, accountability, and waste management practices in retail stores. Semi-perishable food items, such as flour, bread, packaged meals, and beverages made from unprocessed grains, have a shorter shelf life than their raw counterparts, which can be stored for longer periods before processing. Ineffective management of these items often results in wastage and inflates the prices of raw materials due to imbalanced demand. Our solution holds retailers accountable for their role in demand creation and food waste by integrating a Responsibility Index Score, encouraging them to optimise their product offerings and reduce waste. These modules enhance consumption practices, supporting UN Sustainable Development Goals 2 (Zero Hunger) and 12 (Responsible Consumption and Production). They also help build consumer trust and reinforce retail stores' economic and social accountability. The modular approach ensures adaptability and ease of adoption across various invoicing platforms without requiring a complete system overhaul. © 2025 IEEE.

关键词： Sales

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Blockchain and Deep Learning-Based Decentralized Anomaly Detection Framework for VANET 18

Blockchain and Deep Learning-Based Decentralized Anomaly Det...

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18th IEEE International Conference on Vehicular Electronics and Safety, ICVES 2024

作者： Jetani, Harshil Patel, Janam Mahida, Nikunjkumar Patel, Man Gupta, Rajesh Tanwar, Sudeep Gupta, Ankur Institute of Technology Nirma University Department of Computer Science and Engineering Gujarat 382481 India Model Institute of Engineering and Technology Department of Computer Science and Engineering J and K India

ISBN: (纸本)9798350387278

The automotive industry is increasing exponentially day by day providing a safer and efficient experience to the consumers. Intelligent Transportation Systems (ITS) has become more crucial with the increasing number of vehicles. Moreover, there is a growing demand for intelligent services like efficient traffic monitoring in vehicle-to-vehicle (V2V) and vehicle-to-everything (V2X) communication environments. Vehicular Ad-hoc Network (VANET) represents the demanding and vibrant environment for securing the interaction between the vehicle, RSUs, and pedestrians through the Controller area network (CAN). RF jamming Attacks can be integrated through wireless mediums. The paper presents an improvement in securing VANETs through the combination of Blockchain technology (BT) with Deep learning (DL). In DL-based model, RNN is utilized for detecting and classifying malicious activities. BT ensures secure transactions and automated trust-less interactions using smart contracts. Thus, the non-attack data classified by DL layer is communicated using BT. IPFS is used for storing the information to enhance the scalability while mitigating the maintenance cost of BT. The accuracy achieved by the RNN model during evaluation was 97%. Multiple models were evaluated over metrics like accuracy, precision, recall, F1-score, and ROC-AUC curve. © 2024 IEEE.

关键词： Vehicular ad hoc networks

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